diff --git a/examples/jansen_rit_liu_et_al.jl b/examples/jansen_rit_liu_et_al.jl
new file mode 100644
index 00000000..555a7199
--- /dev/null
+++ b/examples/jansen_rit_liu_et_al.jl
@@ -0,0 +1,99 @@
+# A basic example using delayed differential equations to introduce optional delays in interregional connections.
+# The system being built here is the same as in Liu et al. (2020). DOI: 10.1016/j.neunet.2019.12.021.
+
+using Neuroblox  # Core functionality
+using DifferentialEquations # Needed for solver
+using MetaGraphs # Set up graph of systems
+
+τ_factor = 1000 #needed because the paper units were in seconds, and we need ms to be consistent
+
+# Create Jansen-Rit blocks with the same parameters as the paper and store them in a list
+@named Str = JansenRit(τ=0.0022*τ_factor, H=20, λ=300, r=0.3)
+@named GPE = JansenRit(τ=0.04*τ_factor, cortical=false) # all default subcortical except τ
+@named STN = JansenRit(τ=0.01*τ_factor, H=20, λ=500, r=0.1)
+@named GPI = JansenRit(cortical=false) # default parameters subcortical Jansen Rit blox
+@named Th  = JansenRit(τ=0.002*τ_factor, H=10, λ=20, r=5)
+@named EI  = JansenRit(τ=0.01*τ_factor, H=20, λ=5, r=5)
+@named PY  = JansenRit(cortical=true) # default parameters cortical Jansen Rit blox
+@named II  = JansenRit(τ=2.0*τ_factor, H=60, λ=5, r=5)
+blox = [Str, GPE, STN, GPI, Th, EI, PY, II]
+
+# test graphs
+g = MetaDiGraph()
+add_blox!.(Ref(g), blox)
+
+# Store parameters to be passed later on
+params = @parameters C_Cor=60 C_BG_Th=60 C_Cor_BG_Th=5 C_BG_Th_Cor=5
+
+# Add the edges as specified in Table 2 of Liu et al.
+# This is a subset of the edges selected to run a shorter version of the model.
+# If you want to add more edges in a batch, you can create an adjacency matrix and then call create_adjacency_edges!(g, adj_matrix).
+add_edge!(g, 2, 1, Dict(:weight => -0.5*C_BG_Th))
+add_edge!(g, 2, 2, Dict(:weight => -0.5*C_BG_Th))
+add_edge!(g, 2, 3, Dict(:weight => C_BG_Th))
+add_edge!(g, 3, 2, Dict(:weight => -0.5*C_BG_Th))
+add_edge!(g, 3, 7, Dict(:weight => C_Cor_BG_Th))
+add_edge!(g, 4, 2, Dict(:weight => -0.5*C_BG_Th))
+add_edge!(g, 4, 3, Dict(:weight => C_BG_Th))
+add_edge!(g, 5, 4, Dict(:weight => -0.5*C_BG_Th))
+add_edge!(g, 6, 5, Dict(:weight => C_BG_Th_Cor))
+add_edge!(g, 6, 7, Dict(:weight => 6*C_Cor))
+add_edge!(g, 7, 6, Dict(:weight => 4.8*C_Cor))
+add_edge!(g, 7, 8, Dict(:weight => -1.5*C_Cor))
+add_edge!(g, 8, 7, Dict(:weight => 1.5*C_Cor))
+add_edge!(g, 8, 8, Dict(:weight => 3.3*C_Cor))
+
+# Create the ODE system. This will have some warnings as delays are set to 0 - ignore those for now.
+@named final_system = system_from_graph(g, params)
+final_system_sys = structural_simplify(final_system)
+
+# Collect the graph delays and create a DDEProblem. This will all be zero in this case.
+final_delays = graph_delays(g)
+sim_dur = 1000.0 # Simulate for 1 second
+prob = DDEProblem(final_system_sys,
+    [],
+    (0.0, sim_dur),
+    constant_lags = final_delays)
+
+# Select the algorihm. MethodOfSteps will return the same as Vern7() in this case because there are no non-zero delays, but is required since this is a DDEProblem.
+alg = MethodOfSteps(Vern7())
+sol_dde_no_delays = solve(prob, alg, saveat=1)
+
+
+# Example of delayed connections
+
+# First, recreate the graph to remove previous connections
+g = MetaDiGraph()
+add_blox!.(Ref(g), blox)
+
+# Now, add the edges with the specified delays. Again, if you prefer, there's a version using adjacency and delay matrices to assign all at once.
+add_edge!(g, 2, 1, Dict(:weight => -0.5*60, :delay => 1))
+add_edge!(g, 2, 2, Dict(:weight => -0.5*60, :delay => 2))
+add_edge!(g, 2, 3, Dict(:weight => 60, :delay => 1))
+add_edge!(g, 3, 2, Dict(:weight => -0.5*60, :delay => 1))
+add_edge!(g, 3, 7, Dict(:weight => 5, :delay => 1))
+add_edge!(g, 4, 2, Dict(:weight => -0.5*60, :delay => 4))
+add_edge!(g, 4, 3, Dict(:weight => 60, :delay => 1))
+add_edge!(g, 5, 4, Dict(:weight => -0.5*60, :delay => 2))
+add_edge!(g, 6, 5, Dict(:weight => 5, :delay => 1))
+add_edge!(g, 6, 7, Dict(:weight => 6*60, :delay => 2))
+add_edge!(g, 7, 6, Dict(:weight => 4.8*60, :delay => 3))
+add_edge!(g, 7, 8, Dict(:weight => -1.5*60, :delay => 1))
+add_edge!(g, 8, 7, Dict(:weight => 1.5*60, :delay => 4))
+add_edge!(g, 8, 8, Dict(:weight => 3.3*60, :delay => 1))
+
+# Now you can run the same code as above, but it will handle the delays automatically.
+@named final_system = system_from_graph(g, params)
+final_system_sys = structural_simplify(final_system)
+
+# Collect the graph delays and create a DDEProblem.
+final_delays = graph_delays(g)
+sim_dur = 1000.0 # Simulate for 1 second
+prob = DDEProblem(final_system_sys,
+    [],
+    (0.0, sim_dur),
+    constant_lags = final_delays)
+
+# Select the algorihm. MethodOfSteps is now needed because there are non-zero delays.
+alg = MethodOfSteps(Vern7())
+sol_dde_with_delays = solve(prob, alg, saveat=1)
\ No newline at end of file
diff --git a/examples/larter_breakspear/whole_brain_larter_breakspear.jl b/examples/larter_breakspear/whole_brain_larter_breakspear.jl
new file mode 100644
index 00000000..f9a26550
--- /dev/null
+++ b/examples/larter_breakspear/whole_brain_larter_breakspear.jl
@@ -0,0 +1,137 @@
+# This is a tutorial for how to run a whole-brain simulation using the Larter-Breakspear model.
+# This approach is the same as that taken in Antal et al. (2023) and van Nieuwenhuizen et al. (2023), with the exception that a different DTI dataset is used.
+# The data used in those papers is available upon request from the authors where it was validated in Endo et al. (2020).
+# As we'd like to make this tutorial as accessible as possible, we'll use a different dataset that is publicly available.
+# The dataset used here is from Rosen and Halgren (2021) and is available at https://zenodo.org/records/10150880.
+
+# References:
+# 1. Antal et al. (2023). DOI: 10.48550/arXiv.2303.13746.
+# 2. van Nieuwenhuizen et al. (2023). DOI: 10.1101/2023.05.10.540257.
+# 3. Endo et al. (2020). DOI: 10.3389/fncom.2019.00091.
+# 4. Rosen and Halgren (2021). DOI: 10.1523/ENEURO.0416-20.2020.
+
+using Neuroblox # Core functionality
+using CSV, MAT, DataFrames # Import/export data functions
+using MetaGraphs # Set up graph of systems
+using DifferentialEquations # Needed for solver TODO: make a new simulate that can handle system_from_graph
+using StatsBase # Needed for rescaling the DTI matrix
+#using Plots # Only uncomment if you actually want to do plotting, otherwise save yourself the overhead
+
+# A note on the data: this is a 360-region parcellation of the brain. For this example, we'll extract and rescale the left hemisphere default mode network.
+# If you want a true rescaling method, you'd need to do some additional corrections (e.g., volume correction) for the number of streamlines computed.
+# For this tutorial though, the rescaling is approximate to just give us a working example.
+# See Endo et al. for more details on how to do a better DTI rescaling.
+
+# Load the data
+# As mentioned above, this data is from Rosen and Halgren (2021). You can download the original at https://zenodo.org/records/10150880.
+# For convenience, we have included the average connectivity matrix (log scaled probability from streamline counts) available from that link within this repository.
+data = matread("averageConnectivity_Fpt.mat")
+
+# Extract the data
+adj = data["Fpt"]
+adj[findall(isnan, adj)] .= 0 # Replace NaNs with 0s
+adj = StatsBase.transform(StatsBase.fit(UnitRangeTransform, adj, dims=2), adj) # Equivalent of Matlab rescale function. Resamples to unit range.
+
+# For the purpose of this simulation, we want something that will run relatively quickly.
+# In this parcellation, there are 40 regions per hemisphere in the default mode network, so we'll extract the left hemisphere DMN.
+# Original indices with networks are listed in allTables.xlsx at the same download link from the Rosen and Halgren (2021) dataset listed above.
+left_indices = [30, 31, 32, 33, 34, 35, 61, 62, 64, 65, 66, 67, 68, 69, 70, 71, 72, 76, 87, 88, 90, 93, 94, 118, 119, 120, 126, 130, 131, 132, 134, 150, 151, 155, 161, 162, 164, 165, 176, 177]
+adj = adj[left_indices, left_indices]
+
+# Extract the names of the regions
+names = data["parcelIDs"]
+names = names[left_indices]
+
+# Plot the connectivity matrix
+# This is optional but gives you a sense of what the overall connectivity looks like.
+# If you want to do plotting remember to uncomment the Plots import above.
+#heatmap(adj)
+
+# Number of regions in this particular dataset
+N = 40
+
+# Create list of all blocks
+blocks = Vector{LarterBreakspear}(undef, N)
+
+for i in 1:N
+    # Since we're creating an ODESystem inside of the blox, we need to use a symbolic name
+    # Why the extra noise in connectivity? The DTI scaling is arbitrary in this demo, so adding stochasticity to this parameter helps things from just immediately synchronizing.
+    blocks[i] = LarterBreakspear(name=Symbol(names[i]), C=rand()*0.3)
+end
+
+# Create a graph using the blocks and the DTI defined adjacency matrix
+g = MetaDiGraph()
+add_blox!.(Ref(g), blocks)
+create_adjacency_edges!(g, adj)
+
+# Create the ODE system
+# This may take a minute, as it is compiling the whole system
+@named sys = system_from_graph(g)
+sys = structural_simplify(sys)
+
+# Simulate for 100ms
+sim_dur = 1e3
+
+# Create random initial conditions because uniform initial conditions are no bueno. There are 3 states per node.
+v₀ = 0.9*rand(N) .- 0.6
+z₀ = 0.9*rand(N) .- 0.9
+w₀ = 0.4*rand(N) .+ 0.11
+u₀ = [v₀ z₀ w₀]'[:] # Trick to interleave vectors based on column-major ordering
+
+# Create the ODEProblem to run all the final system of equations
+prob = ODEProblem(sys, u₀, (0.0, sim_dur), [])
+
+# Run the simulation and save every 2ms
+@time sol = solve(prob, AutoVern7(Rodas4()), saveat=2)
+
+# Visualizing the results
+# Again, to use any of these commands be sure to uncomment the Plots import above.
+# This plot shows all of the states computed. As such, it is very messy, but useful to make sure that no out-of-bounds behavior (due to bad connectivity) occured.
+# plot(sol)
+
+# More interesting is to choose the plot from a specific region and see the results. Here, we'll plot a specific region's average voltage.
+# First, confirm the region (left orbitofrontal cortex)
+states(sys)[64] # Should give L_OFC₊V(t)
+# Next plot just this variable
+#plot(sol, idxs=(64))
+
+# Suppose we want to run a simulation with different initial conditions. We can do that by remaking the problem to avoid having to recompile the entire system.
+# For example, let's say we want to run the simulation with a different initial condition for the voltage.
+v₀ = 0.9*rand(N) .- 0.61
+z₀ = 0.9*rand(N) .- 0.89
+w₀ = 0.4*rand(N) .+ 0.1
+u₀ = [v₀ z₀ w₀]'[:] # Trick to interleave vectors based on column-major ordering
+
+# Now remake and re-solve
+prob2 = remake(prob; u0=u₀)
+@time sol2 = solve(prob2, AutoVern7(Rodas4()), saveat=2)
+
+# Running a longer simulation
+# Now that we've confirmed this runs, let's go ahead and do a 10min (600s) simulation.
+# Takes <1min to simulate this run. If you save more frequently it'll take longer to run, but you'll have more data to work with.
+# Remember to update the dt below for the BOLD signal if you don't save at the same frequency.
+# If you find this is taking an *excessively* long time (i.e., more than a couple minutes), you likely happened upon a parameter set that has put you into a very stiff area.
+# In that case, you can try to re-run the simulation with a different initial condition or parameter set.
+# In a real study, you'd allow even these long runs to finish, but for the sake of this tutorial we'll just stop it early.
+sim_dur = 6e5
+prob = remake(prob; tspan=(0.0, sim_dur))
+@time sol = solve(prob, AutoVern7(Rodas4()), saveat=2)
+
+# Instead of plotting the data, let's save it out to a CSV file for later analysis
+CSV.write("example_output.csv", DataFrame(sol))
+
+# You should see a noticeable difference in speed compared to the first time, and notice you save the overhead of structural_simplify.
+
+# Now let's do a simulation that creates an fMRI signal out of the neural simulation run above.
+# This example will use the Balloon model with slightly adjusted parameters - see Endo et al. for details.
+
+TR = 800 # Desired repetition time (TR) in ms
+dt = 2 # Time step of the simulation in ms
+bold = boldsignal_endo_balloon(sol.t/1000, sol[1:3:end, :], TR, dt) # Note this returns the signal in units of TR
+
+# Remember that the BOLD signal takes a while to equilibrate, so drop the first 90 seconds of the signal.
+omit_idx = Int(round(90/(TR/1000)))
+bold = bold[:, omit_idx:end]
+
+# Plot an example region to get a sense of what the BOLD signal looks like
+# plot(bold[1, :])
\ No newline at end of file
diff --git a/examples/wholebrainWC/DATA/brainamp.loc b/examples/wholebrainWC/DATA/brainamp.loc
deleted file mode 100644
index b493cc6b..00000000
--- a/examples/wholebrainWC/DATA/brainamp.loc
+++ /dev/null
@@ -1,63 +0,0 @@
-1	     -18	 0.51111	     Fp1
-2	      18	 0.51111	     Fp2
-3	     -39	 0.33333	      F3
-4	      39	 0.33333	      F4
-5	     -90	 0.25556	      C3
-6	      90	 0.25556	      C4
-7	    -141	 0.33333	      P3
-8	     141	 0.33333	      P4
-9	    -162	 0.51111	      O1
-10	     162	 0.51111	      O2
-11	     -54	 0.51111	      F7
-12	      54	 0.51111	      F8
-13	     -90	 0.51111	      T7
-14	      90	 0.51111	      T8
-15	    -126	 0.51111	      P7
-16	     126	 0.51111	      P8
-17	       0	 0.25556	      Fz
-18	      90	       0	      Cz
-19	     180	 0.25556	      Pz
-20	     180	 0.51111	      Oz
-21	     -45	 0.17778	     FC1
-22	      45	 0.17778	     FC2
-23	    -135	 0.17778	     CP1
-24	     135	 0.17778	     CP2
-25	     -69	 0.39444	     FC5
-26	      69	 0.39444	     FC6
-27	    -111	 0.39444	     CP5
-28	     111	 0.39444	     CP6
-29	     -72	 0.63889	     FT9
-30	      72	 0.63889	    FT10
-31	    -108	 0.63889	     TP9
-32	     108	 0.63889	    TP10
-33	     -22	 0.27778	      F1
-34	      22	 0.27778	      F2
-35	     -90	 0.12778	      C1
-36	      90	 0.12778	      C2
-37	    -158	 0.27778	      P1
-38	     158	 0.27778	      P2
-39	     -23	 0.41111	     AF3
-40	      23	 0.41111	     AF4
-41	     -62	 0.27778	     FC3
-42	      62	 0.27778	     FC4
-43	    -118	 0.27778	     CP3
-44	     118	 0.27778	     CP4
-45	    -157	 0.41111	     PO3
-46	     157	 0.41111	     PO4
-47	     -49	 0.41667	      F5
-48	      49	 0.41667	      F6
-49	     -90	 0.38333	      C5
-50	      90	 0.38333	      C6
-51	    -131	 0.41667	      P5
-52       131     0.41667          P6
-53	     -38	 0.51111	     AF7
-54	      38	 0.51111	     AF8
-55	     -72	 0.51111	     FT7
-56	      72	 0.51111	     FT8
-57	    -108	 0.51111	     TP7
-58	     108	 0.51111	     TP8
-59	    -144	 0.51111	     PO7
-60	     144	 0.51111	     PO8
-61	       0	 0.38333	     AFz
-62	     180	 0.12778	     CPz
-63	     180	 0.38333	     POz
\ No newline at end of file
diff --git a/examples/wholebrainWC/DATA/eeg_chanloc.mat b/examples/wholebrainWC/DATA/eeg_chanloc.mat
deleted file mode 100644
index 9364348c..00000000
Binary files a/examples/wholebrainWC/DATA/eeg_chanloc.mat and /dev/null differ
diff --git a/examples/wholebrainWC/DATA/empirical_microstates.mat b/examples/wholebrainWC/DATA/empirical_microstates.mat
deleted file mode 100644
index 6139ccce..00000000
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diff --git a/examples/wholebrainWC/DATA/mean_empirical_functional_connectivity.mat b/examples/wholebrainWC/DATA/mean_empirical_functional_connectivity.mat
deleted file mode 100644
index d94d6eb5..00000000
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diff --git a/examples/wholebrainWC/DATA/mean_leadfield_svd.mat b/examples/wholebrainWC/DATA/mean_leadfield_svd.mat
deleted file mode 100644
index 8c5d35e3..00000000
Binary files a/examples/wholebrainWC/DATA/mean_leadfield_svd.mat and /dev/null differ
diff --git a/examples/wholebrainWC/DATA/mean_structural_connectivity.mat b/examples/wholebrainWC/DATA/mean_structural_connectivity.mat
deleted file mode 100644
index 480e2218..00000000
Binary files a/examples/wholebrainWC/DATA/mean_structural_connectivity.mat and /dev/null differ
diff --git a/examples/wholebrainWC/Project.toml b/examples/wholebrainWC/Project.toml
deleted file mode 100644
index 2b2495c9..00000000
--- a/examples/wholebrainWC/Project.toml
+++ /dev/null
@@ -1,10 +0,0 @@
-[deps]
-CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
-DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
-Graphs = "86223c79-3864-5bf0-83f7-82e725a168b6"
-LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
-MAT = "23992714-dd62-5051-b70f-ba57cb901cac"
-MetaGraphs = "626554b9-1ddb-594c-aa3c-2596fe9399a5"
-Neuroblox = "769b91e5-4c60-41ee-bfae-153c84203cb2"
-OrdinaryDiffEq = "1dea7af3-3e70-54e6-95c3-0bf5283fa5ed"
-Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
diff --git a/examples/wholebrainWC/connectivity_96/areas.txt b/examples/wholebrainWC/connectivity_96/areas.txt
deleted file mode 100644
index f755772f..00000000
--- a/examples/wholebrainWC/connectivity_96/areas.txt
+++ /dev/null
@@ -1,96 +0,0 @@
-   3.3106483e+03
-   8.0458529e+03
-   6.9618231e+02
-   1.9739193e+03
-   1.0341594e+03
-   2.2295400e+03
-   2.1620802e+03
-   1.9730447e+03
-   7.5186205e+02
-   1.4413142e+03
-   6.6223273e+02
-   3.9851836e+03
-   1.4989436e+03
-   2.2737392e+03
-   1.3314346e+03
-   1.7072077e+03
-   8.2105051e+02
-   2.0281819e+03
-   1.9958510e+03
-   1.1458539e+03
-   7.0236088e+02
-   3.4799446e+03
-   2.5012464e+03
-   2.4069852e+03
-   1.4767574e+03
-   7.5472547e+02
-   2.3396232e+03
-   3.7893705e+03
-   1.5114071e+03
-   4.0638359e+03
-   2.4497384e+02
-   3.5459302e+03
-   4.5515081e+03
-   9.7920289e+02
-   9.1954887e+02
-   1.7705557e+03
-   1.2857267e+03
-   8.5359190e+02
-   1.8859106e+03
-   1.4781911e+03
-   9.9181814e+02
-   0.0000000e+00
-   0.0000000e+00
-   0.0000000e+00
-   0.0000000e+00
-   0.0000000e+00
-   0.0000000e+00
-   0.0000000e+00
-   3.2923455e+03
-   8.3424532e+03
-   7.7243318e+02
-   2.1179635e+03
-   1.0570599e+03
-   2.0356497e+03
-   2.1498050e+03
-   1.9703127e+03
-   6.3812861e+02
-   1.0662421e+03
-   6.4970746e+02
-   4.0281095e+03
-   1.5037374e+03
-   2.2085162e+03
-   1.1855909e+03
-   1.9552541e+03
-   8.0844537e+02
-   2.2294491e+03
-   1.8746843e+03
-   1.2442520e+03
-   8.1365011e+02
-   3.6025734e+03
-   2.4821369e+03
-   2.5023220e+03
-   1.3994776e+03
-   7.0036063e+02
-   2.1914892e+03
-   3.8489185e+03
-   1.5077860e+03
-   4.5250197e+03
-   2.3234962e+02
-   3.3782580e+03
-   4.5659269e+03
-   9.6330780e+02
-   8.1473444e+02
-   1.8561484e+03
-   1.1406765e+03
-   9.1005852e+02
-   1.8034139e+03
-   1.4595088e+03
-   1.1245761e+03
-   0.0000000e+00
-   0.0000000e+00
-   0.0000000e+00
-   0.0000000e+00
-   0.0000000e+00
-   0.0000000e+00
-   0.0000000e+00
diff --git a/examples/wholebrainWC/connectivity_96/average_orientations.txt b/examples/wholebrainWC/connectivity_96/average_orientations.txt
deleted file mode 100644
index d6f15741..00000000
--- a/examples/wholebrainWC/connectivity_96/average_orientations.txt
+++ /dev/null
@@ -1,96 +0,0 @@
-  -4.3542886e-01  -2.3988195e-01  -8.6767411e-01
-   9.3594447e-01  -2.4372541e-01  -2.5417684e-01
-  -6.3913598e-01  -7.5594350e-02  -7.6536965e-01
-   2.3517191e-01   1.1003136e-01  -9.6570558e-01
-   6.5533931e-01   7.0672421e-01   2.6659198e-01
-  -7.4968473e-01  -4.9440590e-02  -6.5994579e-01
-   7.4415916e-01  -2.8208353e-01  -6.0552129e-01
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-   2.1732020e-01  -3.8293051e-01  -8.9785085e-01
-   4.8403250e-01   3.5159322e-01   8.0130815e-01
-   9.9475636e-01  -5.2273306e-02  -8.7904952e-02
-  -4.0139504e-01  -6.7520162e-01   6.1885765e-01
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-  -3.9410730e-01  -6.1949076e-01   6.7890399e-01
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-   5.5611557e-01   5.1876820e-01   6.4931889e-01
-  -9.8703215e-01   2.6680469e-02   1.5828987e-01
-   4.7254546e-01   3.7068840e-01   7.9955669e-01
-   0.0000000e+00   0.0000000e+00   0.0000000e+00
-   0.0000000e+00   0.0000000e+00   0.0000000e+00
-   0.0000000e+00   0.0000000e+00   0.0000000e+00
-   0.0000000e+00   0.0000000e+00   0.0000000e+00
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-   0.0000000e+00   0.0000000e+00   0.0000000e+00
-   5.4620351e-01  -2.9915513e-01  -7.8241161e-01
-  -9.3702379e-01  -2.4473424e-01  -2.4918180e-01
-   5.7854001e-01  -1.8414162e-01  -7.9459633e-01
-  -2.2788109e-01   6.8384755e-02  -9.7128458e-01
-  -6.2283567e-01   7.4929722e-01   2.2500978e-01
-   8.4698209e-01  -3.2776607e-02  -5.3061006e-01
-  -7.1165109e-01  -2.8218547e-01  -6.4336932e-01
-  -3.7393898e-01   8.2970383e-01  -4.1444082e-01
-   9.7740039e-01   7.6697593e-02  -1.9699227e-01
-   6.3056819e-01  -3.9877008e-01  -6.6585748e-01
-  -3.5626411e-01  -9.3431642e-01  -1.1344898e-02
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-   7.5606604e-01   1.7483735e-01  -6.3071074e-01
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-  -8.3932548e-01   4.7330126e-01   2.6742971e-01
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-   3.3662149e-01  -9.6582579e-03  -9.4159051e-01
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-  -4.8003040e-01   7.4275220e-01   4.6678688e-01
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-  -1.6799712e-01  -3.9711636e-01  -9.0226136e-01
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-  -5.8998665e-01  -4.1287049e-01   6.9386866e-01
-   9.9069889e-01   9.5527024e-02  -9.6903559e-02
-   7.8368475e-01   3.1712792e-01   5.3410495e-01
-  -2.4768911e-01  -5.1905685e-01   8.1806485e-01
-   4.7992363e-01  -5.3570610e-01   6.9476060e-01
-  -8.3046594e-01   7.1217306e-02   5.5249835e-01
-  -5.4466496e-01   5.2148259e-01   6.5680742e-01
-   9.9406839e-01   2.5465514e-02   1.0573335e-01
-  -4.5619366e-01   3.5665549e-01   8.1528167e-01
-   0.0000000e+00   0.0000000e+00   0.0000000e+00
-   0.0000000e+00   0.0000000e+00   0.0000000e+00
-   0.0000000e+00   0.0000000e+00   0.0000000e+00
-   0.0000000e+00   0.0000000e+00   0.0000000e+00
-   0.0000000e+00   0.0000000e+00   0.0000000e+00
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diff --git a/examples/wholebrainWC/connectivity_96/centres.txt b/examples/wholebrainWC/connectivity_96/centres.txt
deleted file mode 100755
index aab91b39..00000000
--- a/examples/wholebrainWC/connectivity_96/centres.txt
+++ /dev/null
@@ -1,96 +0,0 @@
-RM-TCpol_R  33.079347  -7.593124 -32.799272 
-RM-TCs_R  54.915934 -33.810819  -1.375730 
-RM-Amyg_R  17.438152  -5.277610 -13.745837 
-RM-PFCoi_R  21.645635  26.941077 -19.611492 
-RM-IA_R  38.224291  11.482410 -13.623293 
-RM-PFCom_R   6.267575  25.181871 -16.701842 
-RM-TCc_R  43.660324 -58.514435  -7.694181 
-RM-PFCol_R  29.444259  49.076630  -0.952121 
-RM-TCi_R  28.718069 -54.508717 -14.420098 
-RM-PHC_R  19.729065 -32.245093  -9.132630 
-MM82a-G_R  34.917765  25.065066  -3.766634 
-RM-PMCvl_R  47.004645  20.874793  11.634857 
-RM-VACv_R  24.783182 -82.874647 -12.077967 
-RM-Ip_R  40.808202  -3.305432   1.892959 
-RM-PFCpol_R   8.216735  61.863249   8.139174 
-RM-HC_R  22.506788 -20.364649 -11.303707 
-RM-CCs_R   3.427869  25.074243  -4.747290 
-RM-PFCvl_R  40.812508  40.111957  17.487273 
-RM-V2_R  11.774463 -79.525795   3.404145 
-RM-PFCm_R   8.294410  45.075418   9.876705 
-RM-TCv_R  24.810789 -54.934840  -7.515384 
-RM-VACd_R  27.690285 -83.455823  20.802248 
-RM-V1_R  11.890945 -74.310932   7.222870 
-RM-PFCcl_R  24.035479  43.044884  33.627260 
-RM-A2_R  48.584832 -28.586783  15.135759 
-RM-CCr_R   6.702045 -43.575783  12.352452 
-RM-CCp_R  10.632581 -49.270173  24.902874 
-RM-CCa_R   4.846725   9.108356  26.416168 
-RM-S2_R  53.367495 -19.080477  19.550660 
-RM-S1_R  37.713158 -25.333413  52.415061 
-RM-A1_R  36.950384 -18.793378  13.708190 
-RM-M1_R  24.874698 -18.375774  52.963887 
-RM-PCi_R  39.153217 -54.570078  43.080367 
-RM-PCm_R   9.131634 -56.584867  39.751706 
-RM-PFCdm_R   8.017632  35.951673  49.465444 
-RM-PCip_R  21.444711 -63.620710  54.468403 
-RM-PCs_R  14.127554 -53.207194  59.343654 
-RM-FEF_R  43.858507   1.583726  49.671365 
-RM-PFCdl_R  25.309093  12.552618  53.221126 
-RM-PMCm_R   7.224909   3.586255  59.539745 
-RM-PMCdl_R  23.475495  -5.407731  67.187371 
-TM-F_R  10.709611 -13.305076   7.165679 
-TM-T_R   9.487179 -17.753905  10.494842 
-TM-OP_R  17.718292 -22.263158   6.059965 
-BG-Cd_R  14.301561  11.010804  11.490516 
-BG-Pu_R  26.495749   2.776009   1.303447 
-BG-Pa_R  20.850489  -3.005123  -0.189101 
-BG-Acc_R  10.368263  13.203593  -5.534431 
-RM-TCpol_L -32.681790  -8.320529 -31.793774 
-RM-TCs_L -54.720063 -33.892198  -1.422995 
-RM-Amyg_L -15.859426  -7.452867 -13.127293 
-RM-PFCoi_L -20.226823  25.432919 -19.776290 
-RM-IA_L -37.990116  11.507916 -13.796844 
-RM-PFCom_L  -5.461125  25.482359 -16.462528 
-RM-TCc_L -42.827596 -58.286399  -8.750547 
-RM-PFCol_L -28.607213  49.095480  -1.984764 
-RM-TCi_L -28.371168 -54.266414 -14.198983 
-RM-PHC_L -18.955951 -31.746896  -9.391177 
-MM82a-G_L -34.158674  24.411811  -4.311981 
-RM-PMCvl_L -46.597057  21.230801  10.684409 
-RM-VACv_L -24.006268 -83.189723 -12.050953 
-RM-Ip_L -40.876741  -3.791963   1.243455 
-RM-PFCpol_L  -7.689917  61.850339   8.155949 
-RM-HC_L -21.143986 -25.894536  -8.450204 
-RM-CCs_L  -3.171100  23.997086  -4.759688 
-RM-PFCvl_L -40.452662  39.734075  18.317703 
-RM-V2_L -11.495151 -80.341203   3.657959 
-RM-PFCm_L  -8.230249  44.635723   8.745645 
-RM-TCv_L -24.553983 -55.141679  -7.629212 
-RM-VACd_L -27.764819 -83.351504  20.366430 
-RM-V1_L -12.020475 -73.679251   6.866555 
-RM-PFCcl_L -23.527592  42.995398  33.663668 
-RM-A2_L -48.848261 -28.326444  14.382296 
-RM-CCr_L  -4.908909 -43.424849  15.482611 
-RM-CCp_L -10.925561 -50.543513  25.301208 
-RM-CCa_L  -4.746795   8.564223  26.351959 
-RM-S2_L -52.006466 -19.011937  19.451158 
-RM-S1_L -37.573215 -25.088520  52.189595 
-RM-A1_L -36.797423 -19.116026  13.896768 
-RM-M1_L -24.507209 -18.054854  51.754528 
-RM-PCi_L -38.940697 -54.678016  43.108789 
-RM-PCm_L  -9.154303 -57.042530  39.462044 
-RM-PFCdm_L  -7.508670  36.248376  49.003248 
-RM-PCip_L -21.544740 -63.315526  54.590945 
-RM-PCs_L -13.094641 -53.667567  59.445681 
-RM-FEF_L -43.671270   0.704116  50.160082 
-RM-PFCdl_L -24.184760  13.958344  53.500476 
-RM-PMCm_L  -6.812133   3.455651  59.219364 
-RM-PMCdl_L -22.721598  -4.686517  67.235649 
-TM-F_L  -7.675865 -14.379325   7.551905 
-TM-T_L  -6.747257 -19.515702  10.371169 
-TM-OP_L -14.908959 -23.774755   5.438157 
-BG-Cd_L -11.785498   9.976993  10.737393 
-BG-Pu_L -23.901491   1.482650   1.339547 
-BG-Pa_L -17.957794  -4.120351  -0.333891 
-BG-Acc_L  -8.463883  12.496614  -6.170429 
diff --git a/examples/wholebrainWC/connectivity_96/cortical.txt b/examples/wholebrainWC/connectivity_96/cortical.txt
deleted file mode 100644
index b2bef0f9..00000000
--- a/examples/wholebrainWC/connectivity_96/cortical.txt
+++ /dev/null
@@ -1,96 +0,0 @@
-1 
-1 
-0 
-1 
-1 
-1 
-1 
-1 
-1 
-1 
-1 
-1 
-1 
-1 
-1 
-1 
-1 
-1 
-1 
-1 
-1 
-1 
-1 
-1 
-1 
-1 
-1 
-1 
-1 
-1 
-1 
-1 
-1 
-1 
-1 
-1 
-1 
-1 
-1 
-1 
-1 
-0 
-0 
-0 
-0 
-0 
-0 
-0 
-1 
-1 
-0 
-1 
-1 
-1 
-1 
-1 
-1 
-1 
-1 
-1 
-1 
-1 
-1 
-1 
-1 
-1 
-1 
-1 
-1 
-1 
-1 
-1 
-1 
-1 
-1 
-1 
-1 
-1 
-1 
-1 
-1 
-1 
-1 
-1 
-1 
-1 
-1 
-1 
-1 
-0 
-0 
-0 
-0 
-0 
-0 
-0 
diff --git a/examples/wholebrainWC/connectivity_96/info.txt b/examples/wholebrainWC/connectivity_96/info.txt
deleted file mode 100644
index fb84b597..00000000
--- a/examples/wholebrainWC/connectivity_96/info.txt
+++ /dev/null
@@ -1,2 +0,0 @@
-weights_unit = "au"
-length_unit = "mm"area_unit = "mm^2"
\ No newline at end of file
diff --git a/examples/wholebrainWC/connectivity_96/tract_lengths.txt b/examples/wholebrainWC/connectivity_96/tract_lengths.txt
deleted file mode 100644
index a1bcc6f6..00000000
--- a/examples/wholebrainWC/connectivity_96/tract_lengths.txt
+++ /dev/null
@@ -1,96 +0,0 @@
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diff --git a/examples/wholebrainWC/connectivity_96/weights.txt b/examples/wholebrainWC/connectivity_96/weights.txt
deleted file mode 100644
index 739160d9..00000000
--- a/examples/wholebrainWC/connectivity_96/weights.txt
+++ /dev/null
@@ -1,96 +0,0 @@
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-   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   2.0000000e+00   0.0000000e+00   3.0000000e+00   2.0000000e+00   2.0000000e+00   0.0000000e+00   3.0000000e+00   3.0000000e+00   0.0000000e+00   3.0000000e+00   2.0000000e+00   2.0000000e+00   2.0000000e+00   3.0000000e+00   3.0000000e+00   0.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   2.0000000e+00   2.0000000e+00   2.0000000e+00   2.0000000e+00   2.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   3.0000000e+00   3.0000000e+00   2.0000000e+00   2.0000000e+00   2.0000000e+00   0.0000000e+00   3.0000000e+00   2.0000000e+00   2.0000000e+00   0.0000000e+00   1.0000000e+00   2.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   3.0000000e+00   0.0000000e+00   2.0000000e+00   0.0000000e+00   2.0000000e+00   2.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   3.0000000e+00   3.0000000e+00   0.0000000e+00   3.0000000e+00   3.0000000e+00   0.0000000e+00   3.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   2.0000000e+00   0.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00
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-   2.0000000e+00   2.0000000e+00   0.0000000e+00   1.0000000e+00   3.0000000e+00   1.0000000e+00   0.0000000e+00   3.0000000e+00   0.0000000e+00   0.0000000e+00   2.0000000e+00   2.0000000e+00   2.0000000e+00   3.0000000e+00   3.0000000e+00   2.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   0.0000000e+00   0.0000000e+00   2.0000000e+00   3.0000000e+00   2.0000000e+00   3.0000000e+00   3.0000000e+00   2.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   2.0000000e+00   2.0000000e+00   3.0000000e+00   2.0000000e+00   2.0000000e+00   3.0000000e+00   2.0000000e+00   2.0000000e+00   3.0000000e+00   3.0000000e+00   2.0000000e+00   0.0000000e+00   2.0000000e+00   2.0000000e+00   2.0000000e+00   3.0000000e+00   0.0000000e+00   0.0000000e+00   2.0000000e+00   2.0000000e+00   0.0000000e+00   2.0000000e+00   3.0000000e+00   2.0000000e+00   0.0000000e+00   3.0000000e+00   0.0000000e+00   0.0000000e+00   2.0000000e+00   2.0000000e+00   0.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   0.0000000e+00   3.0000000e+00   0.0000000e+00   0.0000000e+00   2.0000000e+00   3.0000000e+00   0.0000000e+00   0.0000000e+00   3.0000000e+00   0.0000000e+00   2.0000000e+00   0.0000000e+00   3.0000000e+00   0.0000000e+00   0.0000000e+00   2.0000000e+00   0.0000000e+00   0.0000000e+00   2.0000000e+00   0.0000000e+00   0.0000000e+00   2.0000000e+00   2.0000000e+00   2.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00
-   2.0000000e+00   2.0000000e+00   0.0000000e+00   2.0000000e+00   3.0000000e+00   3.0000000e+00   0.0000000e+00   2.0000000e+00   0.0000000e+00   2.0000000e+00   1.0000000e+00   2.0000000e+00   0.0000000e+00   3.0000000e+00   2.0000000e+00   3.0000000e+00   2.0000000e+00   3.0000000e+00   3.0000000e+00   0.0000000e+00   2.0000000e+00   1.0000000e+00   2.0000000e+00   0.0000000e+00   0.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   2.0000000e+00   2.0000000e+00   0.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   2.0000000e+00   3.0000000e+00   2.0000000e+00   2.0000000e+00   2.0000000e+00   0.0000000e+00   2.0000000e+00   2.0000000e+00   2.0000000e+00   2.0000000e+00   2.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   2.0000000e+00   3.0000000e+00   2.0000000e+00   0.0000000e+00   1.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   2.0000000e+00   0.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   1.0000000e+00   1.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   1.0000000e+00   0.0000000e+00   0.0000000e+00   3.0000000e+00   2.0000000e+00   0.0000000e+00   0.0000000e+00   1.0000000e+00   0.0000000e+00   0.0000000e+00   3.0000000e+00   0.0000000e+00   0.0000000e+00   3.0000000e+00   0.0000000e+00   0.0000000e+00   3.0000000e+00   3.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00
-   0.0000000e+00   0.0000000e+00   0.0000000e+00   2.0000000e+00   3.0000000e+00   3.0000000e+00   2.0000000e+00   2.0000000e+00   0.0000000e+00   2.0000000e+00   1.0000000e+00   1.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   0.0000000e+00   3.0000000e+00   1.0000000e+00   2.0000000e+00   0.0000000e+00   2.0000000e+00   3.0000000e+00   2.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   2.0000000e+00   0.0000000e+00   2.0000000e+00   2.0000000e+00   1.0000000e+00   2.0000000e+00   1.0000000e+00   2.0000000e+00   3.0000000e+00   3.0000000e+00   2.0000000e+00   2.0000000e+00   2.0000000e+00   2.0000000e+00   2.0000000e+00   2.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   2.0000000e+00   3.0000000e+00   2.0000000e+00   0.0000000e+00   1.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   2.0000000e+00   0.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   1.0000000e+00   1.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   1.0000000e+00   0.0000000e+00   0.0000000e+00   3.0000000e+00   2.0000000e+00   0.0000000e+00   0.0000000e+00   1.0000000e+00   0.0000000e+00   0.0000000e+00   3.0000000e+00   0.0000000e+00   0.0000000e+00   3.0000000e+00   0.0000000e+00   0.0000000e+00   3.0000000e+00   3.0000000e+00   2.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   2.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00
-   2.0000000e+00   3.0000000e+00   1.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   2.0000000e+00   0.0000000e+00   3.0000000e+00   3.0000000e+00   0.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   0.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   2.0000000e+00   3.0000000e+00   2.0000000e+00   3.0000000e+00   2.0000000e+00   2.0000000e+00   3.0000000e+00   2.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   0.0000000e+00   2.0000000e+00   2.0000000e+00   2.0000000e+00   2.0000000e+00   3.0000000e+00   2.0000000e+00   3.0000000e+00   2.0000000e+00   0.0000000e+00   2.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   2.0000000e+00   1.0000000e+00   0.0000000e+00   0.0000000e+00   2.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   1.0000000e+00   0.0000000e+00   2.0000000e+00   2.0000000e+00   1.0000000e+00   1.0000000e+00   3.0000000e+00   3.0000000e+00   0.0000000e+00   3.0000000e+00   2.0000000e+00   2.0000000e+00   2.0000000e+00   3.0000000e+00   3.0000000e+00   1.0000000e+00   3.0000000e+00   3.0000000e+00   2.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   1.0000000e+00   0.0000000e+00   1.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00
-   2.0000000e+00   2.0000000e+00   1.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   2.0000000e+00   0.0000000e+00   3.0000000e+00   3.0000000e+00   2.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   2.0000000e+00   0.0000000e+00   3.0000000e+00   2.0000000e+00   2.0000000e+00   3.0000000e+00   2.0000000e+00   0.0000000e+00   2.0000000e+00   2.0000000e+00   2.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   0.0000000e+00   1.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   2.0000000e+00   1.0000000e+00   0.0000000e+00   0.0000000e+00   2.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   1.0000000e+00   0.0000000e+00   3.0000000e+00   3.0000000e+00   1.0000000e+00   1.0000000e+00   3.0000000e+00   3.0000000e+00   0.0000000e+00   1.0000000e+00   2.0000000e+00   2.0000000e+00   2.0000000e+00   2.0000000e+00   2.0000000e+00   1.0000000e+00   2.0000000e+00   2.0000000e+00   2.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   1.0000000e+00   0.0000000e+00   1.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00
-   0.0000000e+00   0.0000000e+00   0.0000000e+00   2.0000000e+00   2.0000000e+00   0.0000000e+00   2.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   2.0000000e+00   2.0000000e+00   0.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   2.0000000e+00   2.0000000e+00   2.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   3.0000000e+00   0.0000000e+00   3.0000000e+00   3.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00
-   0.0000000e+00   0.0000000e+00   2.0000000e+00   3.0000000e+00   2.0000000e+00   0.0000000e+00   3.0000000e+00   1.0000000e+00   2.0000000e+00   0.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   2.0000000e+00   0.0000000e+00   2.0000000e+00   2.0000000e+00   3.0000000e+00   3.0000000e+00   0.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   2.0000000e+00   3.0000000e+00   3.0000000e+00   2.0000000e+00   2.0000000e+00   2.0000000e+00   2.0000000e+00   2.0000000e+00   3.0000000e+00   2.0000000e+00   3.0000000e+00   0.0000000e+00   0.0000000e+00   1.0000000e+00   0.0000000e+00   2.0000000e+00   0.0000000e+00   2.0000000e+00   2.0000000e+00   3.0000000e+00   0.0000000e+00   3.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   1.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   2.0000000e+00   3.0000000e+00   1.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   1.0000000e+00   2.0000000e+00   0.0000000e+00   3.0000000e+00   2.0000000e+00   0.0000000e+00   2.0000000e+00   2.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   3.0000000e+00   0.0000000e+00   3.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00
-   2.0000000e+00   2.0000000e+00   3.0000000e+00   3.0000000e+00   2.0000000e+00   0.0000000e+00   3.0000000e+00   0.0000000e+00   2.0000000e+00   0.0000000e+00   3.0000000e+00   3.0000000e+00   2.0000000e+00   2.0000000e+00   2.0000000e+00   2.0000000e+00   2.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   2.0000000e+00   0.0000000e+00   3.0000000e+00   2.0000000e+00   2.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   2.0000000e+00   2.0000000e+00   3.0000000e+00   2.0000000e+00   3.0000000e+00   0.0000000e+00   3.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   1.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   3.0000000e+00   0.0000000e+00   1.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   3.0000000e+00   3.0000000e+00   0.0000000e+00   1.0000000e+00   3.0000000e+00   0.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   2.0000000e+00   3.0000000e+00   3.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   2.0000000e+00   0.0000000e+00   2.0000000e+00   2.0000000e+00   2.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00
-   2.0000000e+00   3.0000000e+00   2.0000000e+00   3.0000000e+00   2.0000000e+00   0.0000000e+00   3.0000000e+00   3.0000000e+00   0.0000000e+00   0.0000000e+00   3.0000000e+00   3.0000000e+00   0.0000000e+00   3.0000000e+00   3.0000000e+00   2.0000000e+00   2.0000000e+00   3.0000000e+00   3.0000000e+00   0.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   2.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   0.0000000e+00   0.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   0.0000000e+00   2.0000000e+00   2.0000000e+00   2.0000000e+00   3.0000000e+00   1.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   0.0000000e+00   2.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   1.0000000e+00   1.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   3.0000000e+00   3.0000000e+00   1.0000000e+00   1.0000000e+00   2.0000000e+00   2.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   3.0000000e+00   0.0000000e+00   3.0000000e+00   3.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00
-   2.0000000e+00   2.0000000e+00   2.0000000e+00   3.0000000e+00   2.0000000e+00   0.0000000e+00   3.0000000e+00   0.0000000e+00   2.0000000e+00   0.0000000e+00   3.0000000e+00   3.0000000e+00   2.0000000e+00   2.0000000e+00   2.0000000e+00   2.0000000e+00   0.0000000e+00   3.0000000e+00   3.0000000e+00   0.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   2.0000000e+00   0.0000000e+00   2.0000000e+00   0.0000000e+00   0.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   2.0000000e+00   2.0000000e+00   0.0000000e+00   3.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   1.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   3.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   3.0000000e+00   3.0000000e+00   0.0000000e+00   3.0000000e+00   3.0000000e+00   0.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   3.0000000e+00   0.0000000e+00   3.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00
-   2.0000000e+00   3.0000000e+00   2.0000000e+00   3.0000000e+00   2.0000000e+00   0.0000000e+00   3.0000000e+00   2.0000000e+00   2.0000000e+00   0.0000000e+00   3.0000000e+00   3.0000000e+00   2.0000000e+00   2.0000000e+00   2.0000000e+00   2.0000000e+00   0.0000000e+00   3.0000000e+00   3.0000000e+00   0.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   2.0000000e+00   2.0000000e+00   2.0000000e+00   0.0000000e+00   0.0000000e+00   3.0000000e+00   2.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   0.0000000e+00   0.0000000e+00   2.0000000e+00   0.0000000e+00   2.0000000e+00   2.0000000e+00   2.0000000e+00   2.0000000e+00   2.0000000e+00   0.0000000e+00   3.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   1.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   3.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   3.0000000e+00   2.0000000e+00   0.0000000e+00   3.0000000e+00   3.0000000e+00   0.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   2.0000000e+00   3.0000000e+00   3.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   2.0000000e+00   0.0000000e+00   2.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00
-   2.0000000e+00   3.0000000e+00   2.0000000e+00   3.0000000e+00   3.0000000e+00   0.0000000e+00   3.0000000e+00   3.0000000e+00   2.0000000e+00   0.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   0.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   2.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   2.0000000e+00   3.0000000e+00   3.0000000e+00   2.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   0.0000000e+00   2.0000000e+00   2.0000000e+00   2.0000000e+00   3.0000000e+00   2.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   0.0000000e+00   2.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   2.0000000e+00   3.0000000e+00   0.0000000e+00   0.0000000e+00   2.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   1.0000000e+00   2.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   2.0000000e+00   0.0000000e+00   3.0000000e+00   2.0000000e+00   2.0000000e+00   2.0000000e+00   3.0000000e+00   3.0000000e+00   1.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   1.0000000e+00   0.0000000e+00   1.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00
-   2.0000000e+00   2.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   0.0000000e+00   3.0000000e+00   2.0000000e+00   2.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   2.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   2.0000000e+00   3.0000000e+00   3.0000000e+00   0.0000000e+00   3.0000000e+00   3.0000000e+00   2.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   2.0000000e+00   0.0000000e+00   1.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   2.0000000e+00   3.0000000e+00   2.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   2.0000000e+00   3.0000000e+00   2.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   2.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   1.0000000e+00   2.0000000e+00   2.0000000e+00   2.0000000e+00   2.0000000e+00   2.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   1.0000000e+00   1.0000000e+00   0.0000000e+00   1.0000000e+00   1.0000000e+00   2.0000000e+00   3.0000000e+00   2.0000000e+00   2.0000000e+00   1.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   3.0000000e+00   0.0000000e+00   3.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00
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-   2.0000000e+00   2.0000000e+00   0.0000000e+00   2.0000000e+00   2.0000000e+00   0.0000000e+00   0.0000000e+00   3.0000000e+00   2.0000000e+00   0.0000000e+00   3.0000000e+00   3.0000000e+00   2.0000000e+00   3.0000000e+00   3.0000000e+00   2.0000000e+00   2.0000000e+00   3.0000000e+00   3.0000000e+00   0.0000000e+00   2.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   2.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   2.0000000e+00   3.0000000e+00   3.0000000e+00   2.0000000e+00   2.0000000e+00   3.0000000e+00   2.0000000e+00   0.0000000e+00   2.0000000e+00   2.0000000e+00   0.0000000e+00   3.0000000e+00   1.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   0.0000000e+00   2.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   1.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   3.0000000e+00   3.0000000e+00   1.0000000e+00   1.0000000e+00   2.0000000e+00   2.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   3.0000000e+00   0.0000000e+00   3.0000000e+00   2.0000000e+00   3.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00
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-   2.0000000e+00   2.0000000e+00   0.0000000e+00   2.0000000e+00   2.0000000e+00   0.0000000e+00   0.0000000e+00   2.0000000e+00   2.0000000e+00   0.0000000e+00   2.0000000e+00   2.0000000e+00   2.0000000e+00   3.0000000e+00   2.0000000e+00   2.0000000e+00   2.0000000e+00   2.0000000e+00   2.0000000e+00   0.0000000e+00   0.0000000e+00   2.0000000e+00   2.0000000e+00   2.0000000e+00   2.0000000e+00   3.0000000e+00   2.0000000e+00   3.0000000e+00   3.0000000e+00   3.0000000e+00   2.0000000e+00   2.0000000e+00   2.0000000e+00   1.0000000e+00   2.0000000e+00   2.0000000e+00   2.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   2.0000000e+00   3.0000000e+00   2.0000000e+00   2.0000000e+00   0.0000000e+00   0.0000000e+00   2.0000000e+00   2.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   2.0000000e+00   0.0000000e+00   2.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   1.0000000e+00   2.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   2.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00
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-   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   2.0000000e+00   0.0000000e+00
-   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   2.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00
-   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   0.0000000e+00   2.0000000e+00   0.0000000e+00
diff --git a/examples/wholebrainWC/small_LB.jl b/examples/wholebrainWC/small_LB.jl
deleted file mode 100644
index 5f9fc4e8..00000000
--- a/examples/wholebrainWC/small_LB.jl
+++ /dev/null
@@ -1,51 +0,0 @@
-using Plots, Neuroblox, OrdinaryDiffEq, MetaGraphs, Graphs, CSV, DataFrames,LinearAlgebra, MAT, Random, Plots
-
-clb = [0.0 1.0 0.0; 0.0 0.0 1.0; 1.0 0.0 0.0]
-
-function LinearConnectionsLB(;name, sys=sys, adj_matrix=adj_matrix, connector=connector)
-    adj = adj_matrix .* connector
-    eqs = []
-    for region_num in 1:length(sys)
-		norm_factor = sum(adj_matrix[:, region_num])
-		# @show num_conn
-    	push!(eqs, sys[region_num].jcn ~ sum(adj[:, region_num])/norm_factor)
-    end
-    return @named Circuit = ODESystem(eqs, systems = sys)
-end
-
-blox = []
-for i = 1:3
-	lb = LarterBreakspearBlox(name=Symbol("LB$i"))
-	push!(blox,lb)
-end
-
-sys = [b.odesystem for b in blox]
-con = [s.connector for s in blox]
-@named LB_circuit_lin = LinearConnectionsLB(sys=sys, adj_matrix=clb,connector=con)
-mysys = structural_simplify(LB_circuit_lin)
-
-mysys.states
-mysys.ps
-
-uw = rand(Int(length(mysys.states)/3))
-uv = 0.1*rand(Int(length(mysys.states)/3)) .- 0.05
-uz = 0.1*rand(Int(length(mysys.states)/3)) .- 0.05
-u0 = collect(Iterators.flatten(zip(uv,uz,uw)))
-
-prob = ODEProblem(mysys,u0,(0.0,6e5),[])
-
-p_new = prob.p
-for t_index in 1:3
-	p_new[(t_index-1)*2+1] = 0.35
-	p_new[(t_index-1)*2+2] = 0.65
-end
-prob2 = remake(prob;p=p_new)
-sol2 = solve(prob2,Rodas5(),saveat=0.1)
-
-plot(sol2,xlim=(0,600),ylim=(-1,1))
-savefig("LBsmall.png")
-
-
-
-
-
diff --git a/examples/wholebrainWC/wb_LB.jl b/examples/wholebrainWC/wb_LB.jl
deleted file mode 100644
index 3cd46a80..00000000
--- a/examples/wholebrainWC/wb_LB.jl
+++ /dev/null
@@ -1,52 +0,0 @@
-using Plots, Neuroblox, OrdinaryDiffEq, MetaGraphs, Graphs, CSV, DataFrames,LinearAlgebra, MAT, Random, Plots
-
-connLB = matread(joinpath(@__DIR__, "DATA","mean_structural_connectivity.mat"))
-clb = connLB["mean_structural_connectivity"]
-
-function LinearConnectionsLB(;name, sys=sys, adj_matrix=adj_matrix, connector=connector)
-    adj = adj_matrix .* connector
-    eqs = []
-    for region_num in 1:length(sys)
-		norm_factor = sum(adj_matrix[:, region_num])
-		# @show num_conn
-    	push!(eqs, sys[region_num].jcn ~ sum(adj[:, region_num])/norm_factor)
-    end
-    return @named Circuit = ODESystem(eqs, systems = sys)
-end
-
-blox = []
-for i = 1:78
-	lb = LarterBreakspearBlox(name=Symbol("LB$i"))
-	push!(blox,lb)
-end
-
-sys = [b.odesystem for b in blox]
-con = [s.connector for s in blox]
-@named LB_circuit_lin = LinearConnectionsLB(sys=sys, adj_matrix=clb,connector=con)
-mysys = structural_simplify(LB_circuit_lin)
-
-mysys.states
-mysys.ps
-
-uw = rand(Int(length(mysys.states)/3))
-uv = 0.1*rand(Int(length(mysys.states)/3)) .- 0.05
-uz = 0.1*rand(Int(length(mysys.states)/3)) .- 0.05
-u0 = collect(Iterators.flatten(zip(uv,uz,uw)))
-
-prob = ODEProblem(mysys,u0,(0.0,6e5),[])
-
-p_new = prob.p
-for t_index in 1:78
-	p_new[(t_index-1)*2+1] = 0.35
-	p_new[(t_index-1)*2+2] = 0.61
-end
-prob2 = remake(prob;p=p_new)
-sol2 = solve(prob2,Rodas5(),saveat=0.1)
-
-plot(sol2.t,sol2[1,:],label=false,xlim=(0,600),ylim=(-1,1))
-
-
-
-
-
-
diff --git a/examples/wholebrainWC/wholebrainLB.jl b/examples/wholebrainWC/wholebrainLB.jl
deleted file mode 100644
index baa0bc8d..00000000
--- a/examples/wholebrainWC/wholebrainLB.jl
+++ /dev/null
@@ -1,155 +0,0 @@
-### A Pluto.jl notebook ###
-# v0.19.19
-
-using Markdown
-using InteractiveUtils
-
-# This Pluto notebook uses @bind for interactivity. When running this notebook outside of Pluto, the following 'mock version' of @bind gives bound variables a default value (instead of an error).
-macro bind(def, element)
-    quote
-        local iv = try Base.loaded_modules[Base.PkgId(Base.UUID("6e696c72-6542-2067-7265-42206c756150"), "AbstractPlutoDingetjes")].Bonds.initial_value catch; b -> missing; end
-        local el = $(esc(element))
-        global $(esc(def)) = Core.applicable(Base.get, el) ? Base.get(el) : iv(el)
-        el
-    end
-end
-
-# ╔═╡ 44e6a44a-d577-11ec-384e-23dbb7b36131
-begin
-	import Pkg
-	Pkg.add("CSV")
-	Pkg.add("DataFrames")
-	Pkg.add("MAT")
-	Pkg.add("Plots")
-	Pkg.add("OrdinaryDiffEq")
-	Pkg.develop(path=joinpath(@__DIR__, "..", "..","..", "Neuroblox.jl"))
-    using Plots, Neuroblox, OrdinaryDiffEq, CSV, DataFrames,LinearAlgebra, MAT, Random
-end
-
-# ╔═╡ 9b4e0bc6-4af6-4311-be1b-5948ec818b29
-connectivity = CSV.read(joinpath(@__DIR__,"connectivity_96/weights.txt"), DataFrame;
-				types=Float64,
-				header=false,
-				delim=' ',
-				ignorerepeated = true)
-
-# ╔═╡ 34b168a9-529f-4b9f-a616-713f6a0d644b
-connLB = matread(joinpath(@__DIR__, "DATA","mean_structural_connectivity.mat"))
-
-# ╔═╡ a22cd490-b682-482e-9601-3ac82f49770a
-clb = connLB["mean_structural_connectivity"]
-
-# ╔═╡ f43cde71-80f3-462b-b792-0be92f9f28bc
-0.140202/sum(clb[:,1])
-
-# ╔═╡ ef5dde28-2525-452e-9428-08f2708c36f5
-c = Matrix{Float64}(connectivity)
-
-# ╔═╡ 706e0021-e86d-4e96-8b79-87277c628afd
-begin
-	blox = []
-	for i = 1:78
-	    lb = LarterBreakspearBlox(name=Symbol("LB$i"))
-	    push!(blox,lb)
-	end
-end
-
-# ╔═╡ 1abbb345-e14c-4501-9717-c09b25d73e91
-sys = [b.odesystem for b in blox]
-
-# ╔═╡ 4e617a58-6887-4782-941a-b2e387272d5b
-function LinearConnectionsWC(;name, sys=sys, adj_matrix=adj_matrix, connector=connector)
-    adj = adj_matrix .* connector
-    eqs = []
-    for region_num in 1:length(sys)
-		norm_factor = sum(adj_matrix[:, region_num])
-		# @show num_conn
-    	push!(eqs, sys[region_num].jcn ~ sum(adj[:, region_num])/norm_factor)
-    end
-    return @named Circuit = ODESystem(eqs, systems = sys)
-end
-
-# ╔═╡ abac20d3-bdcb-4ed6-b807-4278a72bc0d3
-connect = [s.connector for s in blox]
-
-# ╔═╡ 99dba142-6ee9-4ffa-b9dc-a377c0de9f2a
-@named LB_Circuit_lin = LinearConnectionsWC(sys=sys, adj_matrix=clb, connector=connect)
-
-# ╔═╡ eb905a35-b314-4aa4-8f31-bdf382d8f528
-equations(LB_Circuit_lin)[1]
-
-# ╔═╡ 69920cca-0cd3-4761-92f5-8dcca67500dc
-mysys = structural_simplify(LB_Circuit_lin)
-
-# ╔═╡ d8f367ad-52dd-4cc2-9c65-0f84cd22d62e
-mysys.states
-
-# ╔═╡ d593209e-2fc8-40c0-883b-935b5512c779
-mysys.ps
-
-# ╔═╡ 5865eab8-fc50-417c-85e6-32a8915ce3cf
-C_list = [ s for s in mysys.ps if endswith("₊C", string(s))]
-
-# ╔═╡ 4d417746-9435-4ca9-9ce3-299dc848e5c1
-deltaVZ_list = [ s for s in mysys.ps if endswith("₊δ_VZ", string(s))]
-
-# ╔═╡ 649e0326-64d8-436e-818d-2705d16a74a0
-begin
-	uw = 0.4*rand(Int(length(mysys.states)/3)) .+ 0.11
-	uv = 0.9*rand(Int(length(mysys.states)/3)) .- 0.6
-	uz = 0.9*rand(Int(length(mysys.states)/3)) .- 0.9
-	u0 = collect(Iterators.flatten(zip(uv,uz,uw)))
-end
-
-# ╔═╡ 9778982a-e706-457f-8723-703d6fefbdb0
-prob = ODEProblem(mysys,u0,(0.0,100.0),[])
-
-# ╔═╡ 41587582-f793-4198-af39-7cd0637d57cc
-md"""
-coup 
-$(@bind coup html"<input type=range value=0.5 min=0 max=1 step=0.01>")
-delta
-$(@bind delta html"<input type=range value=0.1 min=0 max=1 step=0.01>")
-"""
-
-# ╔═╡ b201b708-908a-4321-8c06-da3b442af069
-begin
-	p_new = Dict()
-	for (c,d) in zip(C_list,deltaVZ_list)
-		p_new[c] = coup
-		p_new[d] = delta
-	end
-	prob2 = remake(prob;p=p_new)
-	sol = solve(prob2,Rodas4(),saveat=0.1)
-end
-
-# ╔═╡ 4545b94d-fd25-4a3d-ba30-faeff8c89b8c
-plot(sol,label=false,xlim=(0,100),ylim=(-1,3))
-
-# ╔═╡ 1357d862-557b-4460-8c20-61c7ec9ef872
-size(sol)
-
-# ╔═╡ Cell order:
-# ╠═44e6a44a-d577-11ec-384e-23dbb7b36131
-# ╠═9b4e0bc6-4af6-4311-be1b-5948ec818b29
-# ╠═34b168a9-529f-4b9f-a616-713f6a0d644b
-# ╠═a22cd490-b682-482e-9601-3ac82f49770a
-# ╠═f43cde71-80f3-462b-b792-0be92f9f28bc
-# ╠═ef5dde28-2525-452e-9428-08f2708c36f5
-# ╠═4e617a58-6887-4782-941a-b2e387272d5b
-# ╠═706e0021-e86d-4e96-8b79-87277c628afd
-# ╠═1abbb345-e14c-4501-9717-c09b25d73e91
-# ╠═abac20d3-bdcb-4ed6-b807-4278a72bc0d3
-# ╠═99dba142-6ee9-4ffa-b9dc-a377c0de9f2a
-# ╠═eb905a35-b314-4aa4-8f31-bdf382d8f528
-# ╠═69920cca-0cd3-4761-92f5-8dcca67500dc
-# ╠═d8f367ad-52dd-4cc2-9c65-0f84cd22d62e
-# ╠═d593209e-2fc8-40c0-883b-935b5512c779
-# ╠═5865eab8-fc50-417c-85e6-32a8915ce3cf
-# ╠═4d417746-9435-4ca9-9ce3-299dc848e5c1
-# ╠═b201b708-908a-4321-8c06-da3b442af069
-# ╠═649e0326-64d8-436e-818d-2705d16a74a0
-# ╠═9778982a-e706-457f-8723-703d6fefbdb0
-# ╟─41587582-f793-4198-af39-7cd0637d57cc
-# ╠═4545b94d-fd25-4a3d-ba30-faeff8c89b8c
-# ╠═1357d862-557b-4460-8c20-61c7ec9ef872
diff --git a/examples/wholebrainWC/wholebrainWC.jl b/examples/wholebrainWC/wholebrainWC.jl
deleted file mode 100644
index 1c72b524..00000000
--- a/examples/wholebrainWC/wholebrainWC.jl
+++ /dev/null
@@ -1,195 +0,0 @@
-### A Pluto.jl notebook ###
-# v0.19.9
-
-using Markdown
-using InteractiveUtils
-
-# This Pluto notebook uses @bind for interactivity. When running this notebook outside of Pluto, the following 'mock version' of @bind gives bound variables a default value (instead of an error).
-macro bind(def, element)
-    quote
-        local iv = try Base.loaded_modules[Base.PkgId(Base.UUID("6e696c72-6542-2067-7265-42206c756150"), "AbstractPlutoDingetjes")].Bonds.initial_value catch; b -> missing; end
-        local el = $(esc(element))
-        global $(esc(def)) = Core.applicable(Base.get, el) ? Base.get(el) : iv(el)
-        el
-    end
-end
-
-# ╔═╡ 44e6a44a-d577-11ec-384e-23dbb7b36131
-begin
-    import Pkg
-    # activate the shared project environment
-    Pkg.activate(@__DIR__)
-    # instantiate, i.e. make sure that all packages are downloaded
-    Pkg.instantiate()
-	Pkg.add("OrdinaryDiffEq")
-	Pkg.add("MetaGraphs")
-	Pkg.add("Graphs")
-	Pkg.add("Plots")
-	Pkg.add("CSV")
-	Pkg.add("DataFrames")
-	Pkg.add("LinearAlgebra")
-	Pkg.develop(path=joinpath(@__DIR__, "..", "..","..", "Neuroblox.jl"))
-    using Plots, Neuroblox, OrdinaryDiffEq, MetaGraphs, Graphs, CSV, DataFrames,LinearAlgebra
-end
-
-# ╔═╡ 9b4e0bc6-4af6-4311-be1b-5948ec818b29
-connectivity = CSV.read(joinpath(@__DIR__,"connectivity_96/weights.txt"), DataFrame;
-				types=Float64,
-				header=false,
-				delim=' ',
-				ignorerepeated = true)
-
-# ╔═╡ ef5dde28-2525-452e-9428-08f2708c36f5
-c = Matrix{Float64}(connectivity)
-
-# ╔═╡ b208f23a-69c3-4de0-b0c8-52c36808952e
-begin
-	c_nt = copy(c)
-	c_nt[diagind(c_nt)] .= 0.0
-end
-
-# ╔═╡ 512ea317-ce6c-4791-81f6-0fbde4b3b646
-c_nt
-
-# ╔═╡ 706e0021-e86d-4e96-8b79-87277c628afd
-begin
-	blox = []
-	for i = 1:96
-	    wc = wilson_cowan(name=Symbol("WC$i"),
-							τ_E=0.5,
-							τ_I=1.0,
-							a_E=1.0,
-	                        a_I=1.0,
-	                        c_EE=4.0,
-	                        c_IE=30.0,
-	                        c_EI=30.0,
-	                        c_II=10.0,
-	                        θ_E=0.0,
-	                        θ_I=0.0,
-	                        η=0.4)
-	    push!(blox,wc)
-	end
-end
-
-# ╔═╡ 1abbb345-e14c-4501-9717-c09b25d73e91
-sys = [b.odesystem for b in blox]
-
-# ╔═╡ 4e617a58-6887-4782-941a-b2e387272d5b
-function LinearConnectionsWC(;name, sys=sys, adj_matrix=adj_matrix, connector=connector)
-    adj = adj_matrix .* connector
-    eqs = []
-    for region_num in 1:length(sys)
-		#num_conn = 1.0*count(!iszero,adj_matrix[:, region_num])
-		# @show num_conn
-    	push!(eqs, sys[region_num].jcn ~ sum(adj[:, region_num]))
-		push!(eqs, sys[region_num].P ~ 0.0)
-    end
-    return @named Circuit = ODESystem(eqs, systems = sys)
-end
-
-# ╔═╡ abac20d3-bdcb-4ed6-b807-4278a72bc0d3
-connect = [s.connector for s in blox]
-
-# ╔═╡ 99dba142-6ee9-4ffa-b9dc-a377c0de9f2a
-@named WC_Circuit_lin = LinearConnectionsWC(sys=sys, adj_matrix=c_nt, connector=connect)
-
-# ╔═╡ 69920cca-0cd3-4761-92f5-8dcca67500dc
-mysys = structural_simplify(WC_Circuit_lin)
-
-# ╔═╡ d8f367ad-52dd-4cc2-9c65-0f84cd22d62e
-length(mysys.states)
-
-# ╔═╡ d593209e-2fc8-40c0-883b-935b5512c779
-mysys.ps
-
-# ╔═╡ 9778982a-e706-457f-8723-703d6fefbdb0
-prob = ODEProblem(mysys,0.5*ones(length(mysys.states)),(0.0,100.0),[])
-
-# ╔═╡ c8a72998-f76a-4bad-acd9-d16c1a997415
-md"""
-tau_E
-$(@bind tauE html"<input type=range value=1.0 min=0.1 max=10.0 step=0.1>")
-tau_I
-$(@bind tauI html"<input type=range value=0.9 min=0.1 max=10.0 step=0.1>")
-eta
-$(@bind eta html"<input type=range value=0.4 min=0.0 max=30.0 step=0.1>")
-"""
-
-# ╔═╡ 9e0007f9-829b-4e70-b38a-ee9120daafdb
-md"""
-WC₊θ_E
-$(@bind tE html"<input type=range value=16.7 min=0 max=50 step=0.1>")
-WC₊θ_I
-$(@bind tI html"<input type=range value=13.9 min=0 max=50 step=0.1>")
-"""
-
-# ╔═╡ 4feee4e3-e36c-448f-84de-88f61b586c91
-md"""
-WC₊c_EE, 
-$(@bind cEE html"<input type=range value=26.8 min=0 max=50 step=0.1>")
-WC₊c_IE
-$(@bind cIE html"<input type=range value=26 min=0 max=50 step=0.1>")
-"""
-
-# ╔═╡ 41587582-f793-4198-af39-7cd0637d57cc
-md"""
-WC₊c_II, 
-$(@bind cII html"<input type=range value=31.9 min=0 max=50 step=0.1>")
-WC₊c_EI
-$(@bind cEI html"<input type=range value=11.4 min=0 max=50 step=0.1>")
-"""
-
-# ╔═╡ b201b708-908a-4321-8c06-da3b442af069
-begin
-	p_new = prob.p
-	for t_index in 1:96
-		p_new[(t_index-1)*11+1] = tauE
-		p_new[(t_index-1)*11+2] = tauI
-		p_new[(t_index-1)*11+5] = cEE
-		p_new[(t_index-1)*11+6] = cIE
-		p_new[(t_index-1)*11+7] = cII
-		p_new[(t_index-1)*11+8] = cEI
-		p_new[(t_index-1)*11+9] = tE
-		p_new[(t_index-1)*11+10] = tI
-		p_new[(t_index-1)*11+11] = eta
-	end
-	prob2 = remake(prob;p=p_new)
-	sol = solve(prob2,Rodas4(),saveat=0.1)
-end
-
-# ╔═╡ 469f3bd1-18f7-42f8-8972-3e9a4713bf66
-
-
-# ╔═╡ 20c27485-3265-49b3-bb9e-4867b539a71e
-tauE,tauI, tE,tI,cEE,cIE,cII,cEI,eta
-
-# ╔═╡ 4545b94d-fd25-4a3d-ba30-faeff8c89b8c
-plot(sol,label=false)
-
-# ╔═╡ 1357d862-557b-4460-8c20-61c7ec9ef872
-size(sol)
-
-# ╔═╡ Cell order:
-# ╠═44e6a44a-d577-11ec-384e-23dbb7b36131
-# ╠═9b4e0bc6-4af6-4311-be1b-5948ec818b29
-# ╠═ef5dde28-2525-452e-9428-08f2708c36f5
-# ╠═b208f23a-69c3-4de0-b0c8-52c36808952e
-# ╠═512ea317-ce6c-4791-81f6-0fbde4b3b646
-# ╠═4e617a58-6887-4782-941a-b2e387272d5b
-# ╠═706e0021-e86d-4e96-8b79-87277c628afd
-# ╠═1abbb345-e14c-4501-9717-c09b25d73e91
-# ╠═abac20d3-bdcb-4ed6-b807-4278a72bc0d3
-# ╠═99dba142-6ee9-4ffa-b9dc-a377c0de9f2a
-# ╠═69920cca-0cd3-4761-92f5-8dcca67500dc
-# ╠═d8f367ad-52dd-4cc2-9c65-0f84cd22d62e
-# ╠═d593209e-2fc8-40c0-883b-935b5512c779
-# ╠═9778982a-e706-457f-8723-703d6fefbdb0
-# ╠═b201b708-908a-4321-8c06-da3b442af069
-# ╟─c8a72998-f76a-4bad-acd9-d16c1a997415
-# ╟─9e0007f9-829b-4e70-b38a-ee9120daafdb
-# ╟─4feee4e3-e36c-448f-84de-88f61b586c91
-# ╠═41587582-f793-4198-af39-7cd0637d57cc
-# ╠═469f3bd1-18f7-42f8-8972-3e9a4713bf66
-# ╠═20c27485-3265-49b3-bb9e-4867b539a71e
-# ╠═4545b94d-fd25-4a3d-ba30-faeff8c89b8c
-# ╠═1357d862-557b-4460-8c20-61c7ec9ef872
diff --git a/src/Neuroblox.jl b/src/Neuroblox.jl
index 255ff2b5..1bfc7654 100644
--- a/src/Neuroblox.jl
+++ b/src/Neuroblox.jl
@@ -8,12 +8,18 @@ using Reexport
 using Graphs
 using MetaGraphs
 
-import LinearAlgebra as la
+using ForwardDiff: Dual, Partials, jacobian
+using ForwardDiff
+ForwardDiff.can_dual(::Type{Complex{Float64}}) = true
+using ChainRules: _eigen_norm_phase_fwd!
+
+using LinearAlgebra
+using ToeplitzMatrices: Toeplitz
+using ExponentialUtilities: exponential!
+
 using AbstractFFTs
 using FFTW
-import ToeplitzMatrices as tm
 using DSP, Statistics
-import ExponentialUtilities as eu
 using OrdinaryDiffEq
 using DifferentialEquations
 using Interpolations
@@ -52,7 +58,7 @@ abstract type AbstractNeuronBlox <: AbstractBlox end
 abstract type NeuralMassBlox <: AbstractBlox end
 abstract type CompositeBlox <: AbstractBlox end
 abstract type StimulusBlox <: AbstractBlox end
-abstract type ObserverBlox <: AbstractBlox end
+abstract type ObserverBlox end # not AbstractBlox since it should not show up in the GUI
 
 # we define these in neural_mass.jl
 # abstract type HarmonicOscillatorBlox <: NeuralMassBlox end
@@ -81,6 +87,7 @@ Para_dict = Dict{Symbol, Union{<: Real, Num}}
 
 include("utilities/spectral_tools.jl")
 include("utilities/learning_tools.jl")
+include("utilities/bold_methods.jl")
 include("control/controlerror.jl")
 include("measurementmodels/fmri.jl")
 include("datafitting/spectralDCM.jl")
@@ -174,11 +181,9 @@ function __init__()
     #end
 end
 
-export harmonic_oscillator, jansen_ritC, jansen_ritSC, jansen_rit_spm12, 
-    next_generation, thetaneuron, qif_neuron, if_neuron, hh_neuron_excitatory, 
-    hh_neuron_inhibitory, synaptic_network, van_der_pol, wilson_cowan
-export IFNeuronBlox, LIFNeuronBlox, QIFNeuronBlox, HHNeuronExciBlox, HHNeuronInhibBlox, LinearNeuralMassBlox,
-    WilsonCowanBlox, HarmonicOscillatorBlox, JansenRitCBlox, JansenRitSCBlox, LarterBreakspearBlox,
+export JansenRitSPM12, next_generation, qif_neuron, if_neuron, hh_neuron_excitatory, 
+    hh_neuron_inhibitory, synaptic_network, van_der_pol
+export IFNeuronBlox, LIFNeuronBlox, QIFNeuronBlox, HHNeuronExciBlox, HHNeuronInhibBlox,
     CanonicalMicroCircuitBlox, WinnerTakeAllBlox, CorticalBlox, SuperCortical
 export LinearNeuralMass, HarmonicOscillator, JansenRit, WilsonCowan, LarterBreakspear, NextGenerationBlox, NextGenerationResolvedBlox, NextGenerationEIBlox
 export Matrisome, Striosome, Striatum, GPi, GPe, Thalamus, STN, TAN, SNc
@@ -189,18 +194,18 @@ export CosineSource, CosineBlox, NoisyCosineBlox, PhaseBlox, ImageStimulus
 export PowerSpectrumBlox, BandPassFilterBlox
 export OUBlox, OUCouplingBlox
 export phase_inter, phase_sin_blox, phase_cos_blox
-export LinearConnections, SynapticConnections, ODEfromGraph, ODEfromGraphNeuron, 
-    connectcomplexblox, spikeconnections, adjmatrixfromdigraph, create_rl_loop
+export SynapticConnections, create_rl_loop
 export add_blox!
 export powerspectrum, complexwavelet, bandpassfilter, hilberttransform, phaseangle, mar2csd, csd2mar, mar_ml
 export learningrate, ControlError
 export boldsignal, BalloonModel
-export vecparam, unvecparam, csd_Q, spectralVI
+export vecparam, csd_Q, spectralVI
 export simulate, random_initials
 export system_from_graph, graph_delays
-export create_adjacency_edges!
+export create_adjacency_edges!, adjmatrixfromdigraph
 export get_namespaced_sys, nameof
 export run_experiment!, run_trial!
 export addnontunableparams, get_hemodynamic_observers
+export boldsignal_endo_balloon
 
 end
diff --git a/src/Neurographs.jl b/src/Neurographs.jl
index 16b04e81..412b24d0 100644
--- a/src/Neurographs.jl
+++ b/src/Neurographs.jl
@@ -12,154 +12,6 @@ function add_blox!(g::MetaDiGraph,blox)
     add_vertex!(g, :blox, blox)
 end
 
-function joinmetagraphs(metagraphs::Vector{T}) where T <: Any
-    ngraphs = length(metagraphs)
-    
-    wholegraph = MetaDiGraph()
-    nvertex = 0
-    for i = 1:ngraphs
-        for j in vertices(metagraphs[i].lngraph)
-            add_vertex!(wholegraph, props(metagraphs[i].lngraph, j))
-        end
-        for e in edges(metagraphs[i].lngraph)
-            add_edge!(wholegraph, nvertex+src(e), nvertex+dst(e), props(metagraphs[i].lngraph, e))
-        end
-        nvertex += nv(metagraphs[i].lngraph)
-    end
-    return wholegraph
-end
-
-function LinearConnections(;name, sys=sys, adj_matrix=adj_matrix, connector=connector)
-    adj = adj_matrix .* connector
-    eqs = []
-    for region_num in 1:length(sys)
-       push!(eqs, sys[region_num].jcn ~ sum(adj[:, region_num]))
-    end
-    return ODESystem(eqs, name=name, systems=sys)
-end
-
-function SynapticConnections(;name, sys=sys, adj_matrix=adj_matrix, connector=connector)
-    adj = adj_matrix .* connector
-    syn_eqs = [ 0~sys[1].V - sys[1].V]
-    Nrns = length(sys)
-    for ii = 1:Nrns
-        # for sparse adj matrices it is efficient to only consider presynaptic neurons for computing synaptic input
-        presyn = findall(x-> x>0.0, adj_matrix[:,ii])
-        wts = adj_matrix[presyn,ii]
-        presyn_nrn = sys[presyn]
-        postsyn_nrn = sys[ii]
-        if length(presyn)>0
-            ind = collect(1:length(presyn))
-            eqs = [postsyn_nrn.I_syn ~ sum(p-> (presyn_nrn[p].E_syn-postsyn_nrn.V)*adj[presyn[p],ii],ind),
-                   postsyn_nrn.jcn~postsyn_nrn.I_syn]
-            push!(syn_eqs,eqs[1])
-            push!(syn_eqs,eqs[2])
-        else
-            eqs = [postsyn_nrn.I_syn~0,
-                  postsyn_nrn.jcn~postsyn_nrn.I_syn]
-            push!(syn_eqs,eqs[1])
-            push!(syn_eqs,eqs[2])
-        end
-        
-    end
-    popfirst!(syn_eqs)
-    @named synaptic_eqs = ODESystem(syn_eqs,t)
-    synaptic_network = compose(synaptic_eqs, sys;name=name)
-    return synaptic_network   
-end
-
-function ODEfromGraph(g::MetaDiGraph, jcn; name)
-    vert = []
-    sys = []
-    for v in vertices(g)
-        b = get_prop(g, v, :blox)
-        push!(vert, v)
-        push!(sys, b.odesystem)
-    end
-    eqs = []
-    for (i, (v, s)) in enumerate(zip(vert, sys))
-        if any(occursin.("jcn(t)", string.(states(s)))) # only connect systems with jcn
-            if s.jcn isa Symbolics.Arr
-                input = []
-                for vn in inneighbors(g, v) # vertices that point towards s
-                    M = get_prop(g, vn, v, :weightmatrix)
-                    connector = get_prop(g, vn, :blox).connector
-                    push!(input, M*connector)
-                end
-                input = sum(input)
-                for i = 1:length(s.jcn)
-                    push!(eqs, s.jcn[i] ~ input[i])
-                end
-            else
-                input = Num(0)
-                for vn in inneighbors(g, v) # vertices that point towards s
-                    input += get_prop(g, vn, :blox).connector * get_prop(g, vn, v, :weight)
-                end
-                push!(eqs, s.jcn ~ input + jcn[i])
-            end
-        end
-    end
-    return compose(ODESystem(eqs; name=:connected), sys; name=name)
-end
-
-function ODEfromGraph(g::MetaDiGraph ;name)
-    eqs = []
-    sys = []
-    for v in vertices(g)
-        b = get_prop(g, v, :blox)
-        if isa(b, AbstractBlox) || isa(b, Neuroblox.AbstractComponent)
-            s = b.odesystem
-            push!(sys, s)
-            if any(occursin.("jcn(t)", string.(states(s))))
-                if isa(b, AbstractNeuronBlox)
-                    input = Num(0)
-                    for vn in inneighbors(g, v) # vertices that point towards s
-                        bn = get_prop(g, vn, :blox)
-                        if !isa(bn, AbstractNeuronBlox) # only neurons can be inputs to neurons
-                            continue
-                        end
-                        input += bn.connector * get_prop(g, vn, v, :weight) * (bn.odesystem.E_syn - s.V)
-                    end
-                    push!(eqs, s.I_syn ~ input)
-                    push!(eqs, s.jcn ~ s.I_syn)
-                else
-                    if s.jcn isa Symbolics.Arr
-                        bi = b.bloxinput # bloxinput only exists if s.jcn isa Symbolics.Arr
-                        input = [zeros(Num,length(s.jcn))]
-                        for vn in inneighbors(g, v) # vertices that point towards s
-                            M = get_prop(g, vn, v, :weightmatrix)
-                            connector = get_prop(g, vn, :blox).connector
-                            if connector isa Symbolics.Arr
-                                connector = collect(connector)
-                            end
-                            push!(input, vec(M*connector))
-                        end
-                        input = sum(input)
-                        for i = 1:length(s.jcn)
-                            push!(eqs, bi[i] ~ input[i])
-                        end
-                    else
-                        input = Num(0)
-                        for vn in inneighbors(g, v) # vertices that point towards s
-                            connector = get_prop(g,vn,:blox).connector
-                            if connector isa Symbolics.Arr
-                                input += sum(vec(get_prop(g, vn, v, :weightmatrix)*collect(connector)))
-                            else
-                                input += connector * get_prop(g, vn, v, :weight)
-                            end
-                        end
-                        if haskey(props(g,v),:jcn)
-                            input += get_prop(g,v,:jcn)
-                        end
-                        push!(eqs, s.jcn ~ input)
-                    end
-                end
-            end
-        end
-    end
-    return compose(ODESystem(eqs, t; name=:connected), sys; name=name)
-end
-
 function get_blox(g::MetaDiGraph)
     bs = []
     for v in vertices(g)
@@ -174,6 +26,21 @@ end
 
 get_sys(g::MetaDiGraph) = get_sys.(get_blox(g))
 
+function connector_from_graph(g::MetaDiGraph)
+    bloxs = get_blox(g)
+    link = BloxConnector(bloxs)
+    for v in vertices(g)
+        b = get_prop(g, v, :blox)
+        for vn in inneighbors(g, v)
+            bn = get_prop(g, vn, :blox)
+            kwargs = props(g, vn, v)
+            link(bn, b; kwargs...)
+        end
+    end
+
+    return link
+end
+
 # Helper function to get delays from a graph
 function graph_delays(g::MetaDiGraph)
     bc = connector_from_graph(g)
@@ -218,21 +85,6 @@ function system_from_parts(parts::AbstractVector; name)
     return compose(ODESystem(Equation[], t, [], []; name), get_sys.(parts))
 end
 
-function connector_from_graph(g::MetaDiGraph)
-    bloxs = get_blox(g)
-    link = BloxConnector(bloxs)
-    for v in vertices(g)
-        b = get_prop(g, v, :blox)
-        for vn in inneighbors(g, v)
-            bn = get_prop(g, vn, :blox)
-            kwargs = props(g, vn, v)
-            link(bn, b; kwargs...)
-        end
-    end
-
-    return link
-end
-
 function action_selection_from_graph(g::MetaDiGraph)
     idxs = findall(vertices(g)) do v
         b = get_prop(g, v, :blox)
@@ -277,39 +129,6 @@ function learning_rules_from_graph(g::MetaDiGraph)
     return d
 end
 
-function spikeconnections(;name, sys=sys, psp_amplitude=psp_amplitude, τ=τ, spiketimes=spiketimes)
-    psps = psp_amplitude .* exp.(-(t .- spiketimes) ./ τ)
-    eqs = []
-    for region_num in 1:length(sys)
-       push!(eqs, sys[region_num].jcn ~ sum(psps[:, region_num]))
-    end
-    return ODESystem(eqs, t, name=name, systems=sys)
-end
-
-function connectcomplexblox(bloxlist, adjacency_matrices ;name)
-    nr = length(bloxlist)
-    g = joinmetagraphs(bloxlist)
-    row = 0
-    for i = 1:nr
-        nodes_source = nv(bloxlist[i].lngraph)
-        col = 0
-        for j = 1:nr
-            nodes_sink = nv(bloxlist[j].lngraph)
-            if i == j
-                col += nodes_sink
-                continue
-            end
-            for idx in CartesianIndices(adjacency_matrices[i, j])
-                add_edge!(g, row+idx[1], col+idx[2], :weight, adjacency_matrices[i, j][idx])
-            end
-            col += nodes_sink
-        end
-        row += nodes_source
-    end
-    
-    return ODEfromGraph(g, name=name)
-end
-
 ## Create Learning Loop
 function create_rl_loop(;name, ROIs, datasets, parameters, c_ext)
     # Create LearningBlox for each Region
diff --git a/src/blox/blox_utilities.jl b/src/blox/blox_utilities.jl
index d345a92c..61787483 100644
--- a/src/blox/blox_utilities.jl
+++ b/src/blox/blox_utilities.jl
@@ -1,57 +1,17 @@
-# function progress_scope(params; lvl=0)
-#     para_list = []
-#     for p in params
-#         pp = ModelingToolkit.unwrap(p)
-#         if ModelingToolkit.hasdefault(pp)
-#             d = ModelingToolkit.getdefault(pp)
-#             if typeof(d)==SymbolicUtils.BasicSymbolic{Real}
-#                 if lvl==0
-#                     pp = ParentScope(pp)
-#                 else
-#                     pp = DelayParentScope(pp,lvl)
-#                 end
-#             end
-#         end
-#         push!(para_list,ModelingToolkit.wrap(pp))
-#     end
-#     return para_list
-# end
-
-"""
-This function progresses the scope of parameters and leaves floating point values untouched
-"""
-function progress_scope(args...)
-    paramlist = []
-    for p in args
-        if p isa Num
-            p = ParentScope(p)
-            # pp = ModelingToolkit.unwrap(p)
-            # if ModelingToolkit.hasdefault(pp)
-            #     d = ModelingToolkit.getdefault(pp)
-            #     if typeof(d)==SymbolicUtils.BasicSymbolic{Real}
-            #         pp = ParentScope(pp)
-            #     end
-            # end
-            # push!(para_list,ModelingToolkit.wrap(pp))
-            push!(paramlist, p)
-        else
-            push!(paramlist, p)
-        end
-    end
-    return paramlist
-end
-
 """
-    This function compiles already existing parameters with floats after making them parameters.
-    Keyword arguments are used because parameter definition requires names, not just values
+    function paramscoping(;kwargs...)
+    
+    Scope arguments that are already a symbolic model parameter thereby keep the correct namespace 
+    and make those that are not yet symbolic a symbol.
+    Keyword arguments are used, because parameter definition require names, not just values.
 """
-function compileparameterlist(;kwargs...)
+function paramscoping(;kwargs...)
     paramlist = []
     for (kw, v) in kwargs
-        if v isa Union{Float64, Int}  # note that Num is also subtype of Real. Thus union of types seems to be the solution.
-            paramlist = vcat(paramlist, @parameters $kw = v [tunable=true])
+        if v isa Num
+            paramlist = vcat(paramlist, ParentScope(v))
         else
-            paramlist = vcat(paramlist, v)
+            paramlist = vcat(paramlist, @parameters $kw = v [tunable=true])
         end
     end
     return paramlist
@@ -162,13 +122,13 @@ function input_equations(blox)
         if isnothing(idx)
             namespace_equation(
                 inp ~ 0, 
-                sys, 
+                sys,
                 namespaced_name(inner_namespaceof(blox), nameof(blox))
-            )
+            ) 
         else
             namespace_equation(
-                sys_eqs[idx], 
-                sys, 
+                sys_eqs[idx],
+                sys,
                 namespaced_name(inner_namespaceof(blox), nameof(blox))
             )
         end
@@ -198,6 +158,8 @@ weight_learning_rules(bc::BloxConnector) = bc.learning_rules
 weight_learning_rules(blox::AbstractComponent) = weight_learning_rules(blox.connector)
 weight_learning_rules(blox::CompositeBlox) = weight_learning_rules(blox.connector)
 
+get_blox_parts(blox) = blox.parts
+
 function get_weight(kwargs, name_blox1, name_blox2)
     if haskey(kwargs, :weight)
         return kwargs[:weight]
@@ -206,6 +168,14 @@ function get_weight(kwargs, name_blox1, name_blox2)
     end
 end
 
+function get_weightmatrix(kwargs, name_blox1, name_blox2)
+    if haskey(kwargs, :weightmatrix)
+        return kwargs[:weightmatrix]
+    else
+        error("Connection weight from $name_blox1 to $name_blox2 is not specified.")
+    end
+end
+
 function get_delay(kwargs, name_blox1, name_blox2)
     if haskey(kwargs, :delay)
         return kwargs[:delay]
@@ -249,10 +219,24 @@ function count_spikes(x::AbstractVector{T}; minprom=zero(T), maxprom=nothing, mi
     return length(spikes)
 end
 
-function get_hemodynamic_observers(sys_from_graph, nr)
+"""
+    function get_hemodynamic_observers(sys, nr)
+    
+    Function extracts those states of an MTK system that were tagged "hemodynamic_observer".
+
+    Arguments:
+    - `sys`: MTK system
+    - `nr` : number of regions of a model
+
+    Returns:
+    - `obs_idx`: indices of states with "hemodynamic_observer" tag in MTK system
+    - `obs_states`: states with "hemodynamic_observer" tag in MTK system
+
+"""
+function get_hemodynamic_observers(sys, nr)
     obs_idx = Dict([k => [] for k in 1:nr])
     obs_states = Dict([k => [] for k in 1:nr])
-    for (i, s) in enumerate(states(sys_from_graph))
+    for (i, s) in enumerate(states(sys))
         if isequal(getdescription(s), "hemodynamic_observer")
             regionidx = parse(Int64, split(string(s), "₊")[1][end])
             push!(obs_idx[regionidx], i)
@@ -262,17 +246,30 @@ function get_hemodynamic_observers(sys_from_graph, nr)
     return (obs_idx, obs_states)
 end
 
-function addnontunableparams(param, model)
-    newparam = []
+"""
+    function addnontunableparams(param, model)
+    
+    Function adds parameters of a model that were not marked as tunable to a list of tunable parameters
+    and respects the MTK ordering of parameters.
+
+    Arguments:
+    - `paramlist`: parameters of an MTK system that were tagged as tunable
+    - `sys`: MTK system
+
+    Returns:
+    - `completeparamlist`: complete parameter list of a system, including those that were not tagged as tunable
+"""
+function addnontunableparams(paramlist, sys)
+    completeparamlist = []
     k = 0
-    for p in parameters(model)
+    for p in parameters(sys)
         if istunable(p)
             k += 1
-            push!(newparam, param[k])
+            push!(completeparamlist, paramlist[k])
         else
-            push!(newparam, Symbolics.getdefaultval(p))
+            push!(completeparamlist, Symbolics.getdefaultval(p))
         end
     end
-    append!(newparam, param[k+1:end])
-    return newparam
+    append!(completeparamlist, paramlist[k+1:end])
+    return completeparamlist
 end
\ No newline at end of file
diff --git a/src/blox/canonicalmicrocircuit.jl b/src/blox/canonicalmicrocircuit.jl
index f0060f9e..b5449604 100644
--- a/src/blox/canonicalmicrocircuit.jl
+++ b/src/blox/canonicalmicrocircuit.jl
@@ -1,62 +1,41 @@
 # Canonical micro-circuit model
-# two ways to design it: either add function to edges or to blox.
-
-# some thoughts on design:
-# - include measurement models into blox. Or at least define which variables will be measured (which would be the input to the measurement model). 
-#   Differ that from connector, since that is between things.
-# - 
-
-@parameters t
-D = Differential(t)
-
-# define a sigmoid function
-sigmoid(x::Real, r::Real) = one(x) / (one(x) + exp(-r*x))
 
 """
 Jansen-Rit model block for canonical micro circuit, analogous to the implementation in SPM12
 """
-mutable struct jansen_rit_spm12 <: AbstractComponent
-    τ::Num
-    r::Num
-    connector::Num
-    odesystem::ODESystem
-    function jansen_rit_spm12(;name, τ=1.0, r=2.0/3.0)
-        params = @parameters τ=τ
-        sts    = @variables x(t)=1.0 y(t)=1.0 jcn(t)=0.0
+mutable struct JansenRitSPM12 <: NeuralMassBlox
+    params
+    output
+    jcn
+    odesystem
+    namespace
+    function JansenRitSPM12(;name, namespace=nothing, τ=1.0, r=2.0/3.0)
+        p = paramscoping(τ=τ, r=r)
+        τ, r = p
+
+        sts    = @variables x(t)=1.0 [output=true] y(t)=1.0 jcn(t)=0.0 [input=true]
         eqs    = [D(x) ~ y - ((2/τ)*x),
                   D(y) ~ -x/(τ*τ) + jcn/τ]
-        odesys = ODESystem(eqs, t, sts, params; name=name)
-        new(τ, r, sigmoid(odesys.x, r), odesys)
+
+        sys = System(eqs, name=name)
+        new(p, sts[1], sts[3], sys, namespace)
     end
 end
 
 mutable struct CanonicalMicroCircuitBlox <: CompositeBlox
-    τ_ss::Num
-    τ_sp::Num
-    τ_ii::Num
-    τ_dp::Num
-    r_ss::Num
-    r_sp::Num
-    r_ii::Num
-    r_dp::Num
-    connector::Symbolics.Arr{Num}
-    noDetail::Vector{Num}
-    detail::Vector{Num}
-    bloxinput::Symbolics.Arr{Num}
-    odesystem::ODESystem
-    function CanonicalMicroCircuitBlox(;name, τ_ss=0.002, τ_sp=0.002, τ_ii=0.016, τ_dp=0.028, r_ss=2.0/3.0, r_sp=2.0/3.0, r_ii=2.0/3.0, r_dp=2.0/3.0)
-        @variables jcn(t)[1:4], x(t)[1:4]
-
-        @named ss = jansen_rit_spm12(τ=τ_ss, r=r_ss)  # spiny stellate
-        @named sp = jansen_rit_spm12(τ=τ_sp, r=r_sp)  # superficial pyramidal
-        @named ii = jansen_rit_spm12(τ=τ_ii, r=r_ii)  # inhibitory interneurons granular layer
-        @named dp = jansen_rit_spm12(τ=τ_dp, r=r_dp)  # deep pyramidal
+    namespace
+    parts
+    odesystem
+    connector
+    function CanonicalMicroCircuitBlox(;name, namespace=nothing, τ_ss=0.002, τ_sp=0.002, τ_ii=0.016, τ_dp=0.028, r_ss=2.0/3.0, r_sp=2.0/3.0, r_ii=2.0/3.0, r_dp=2.0/3.0)
+        @named ss = JansenRitSPM12(;namespace=namespaced_name(namespace, name), τ=τ_ss, r=r_ss)  # spiny stellate
+        @named sp = JansenRitSPM12(;namespace=namespaced_name(namespace, name), τ=τ_sp, r=r_sp)  # superficial pyramidal
+        @named ii = JansenRitSPM12(;namespace=namespaced_name(namespace, name), τ=τ_ii, r=r_ii)  # inhibitory interneurons granular layer
+        @named dp = JansenRitSPM12(;namespace=namespaced_name(namespace, name), τ=τ_dp, r=r_dp)  # deep pyramidal
 
         g = MetaDiGraph()
-        add_vertex!(g, Dict(:blox => ss, :name => name, :jcn => jcn[1]))
-        add_vertex!(g, Dict(:blox => sp, :name => name, :jcn => jcn[2]))
-        add_vertex!(g, Dict(:blox => ii, :name => name, :jcn => jcn[3]))
-        add_vertex!(g, Dict(:blox => dp, :name => name, :jcn => jcn[4]))
+        sblox_parts = vcat(ss, sp, ii, dp)
+        add_blox!.(Ref(g), sblox_parts)
 
         add_edge!(g, 1, 1, :weight, -800.0)
         add_edge!(g, 2, 1, :weight, -800.0)
@@ -69,14 +48,16 @@ mutable struct CanonicalMicroCircuitBlox <: CompositeBlox
         add_edge!(g, 3, 4, :weight, -400.0)
         add_edge!(g, 4, 4, :weight, -200.0)
 
-        @named odecmc = ODEfromGraph(g)
-        eqs = [
-            x[1] ~ ss.connector
-            x[2] ~ sp.connector
-            x[3] ~ ii.connector
-            x[4] ~ dp.connector
-        ]
-        odesys = extend(ODESystem(eqs, t, name=:connected), odecmc, name=name)
-        new(τ_ss, τ_sp, τ_ii, τ_dp, r_ss, r_sp, r_ii, r_dp, odesys.x, [odesys.ss.x,odesys.sp.x,odesys.ii.x,odesys.dp.x], [odesys.ss.x,odesys.sp.x,odesys.ii.x,odesys.dp.x], odesys.jcn, odesys)
+        # Construct a BloxConnector object from the graph
+        # containing all connection equations from lower levels and this level.
+        bc = connector_from_graph(g)
+        # If a namespace is not provided, assume that this is the highest level
+        # and construct the ODEsystem from the graph.
+        # If there is a higher namespace, construct only a subsystem containing the parts of this level
+        # and propagate the BloxConnector object `bc` to the higher level 
+        # to potentially add more terms to the same connections.
+        sys = isnothing(namespace) ? system_from_graph(g, bc; name) : system_from_parts(sblox_parts; name)
+
+        new(namespace, sblox_parts, sys, bc)
     end
 end
diff --git a/src/blox/connections.jl b/src/blox/connections.jl
index a2756f3d..406929b6 100644
--- a/src/blox/connections.jl
+++ b/src/blox/connections.jl
@@ -178,6 +178,46 @@ function (bc::BloxConnector)(
     bc(asc_out, neurons_in[end]; kwargs...)
 end
 
+function (bc::BloxConnector)(
+    bloxout::CanonicalMicroCircuitBlox,
+    bloxin::CanonicalMicroCircuitBlox;
+    kwargs...
+)
+    sysparts_out = get_blox_parts(bloxout)
+    sysparts_in = get_blox_parts(bloxin)
+
+    wm = get_weightmatrix(kwargs, namespaced_nameof(bloxin), namespaced_nameof(bloxout))
+
+    idxs = findall(!iszero, wm)
+    for idx in idxs
+        bc(sysparts_out[idx[2]], sysparts_in[idx[1]]; weight=wm[idx])
+    end
+end
+
+# define a sigmoid function
+sigmoid(x, r) = one(x) / (one(x) + exp(-r*x))
+
+function (bc::BloxConnector)(
+    bloxout::JansenRitSPM12, 
+    bloxin::JansenRitSPM12; 
+    kwargs...
+)
+    sys_out = get_namespaced_sys(bloxout)
+    sys_in = get_namespaced_sys(bloxin)
+
+    w = generate_weight_param(bloxout, bloxin; kwargs...)
+    push!(bc.weights, w)
+
+    x = namespace_expr(bloxout.output, sys_out)
+    r = namespace_expr(bloxout.params[2], sys_out)
+    push!(bc.weights, r)
+
+    eq = sys_in.jcn ~ sigmoid(x, r)*w
+    
+    accumulate_equation!(bc, eq)
+end
+
+
 function (bc::BloxConnector)(
     bloxout::NeuralMassBlox, 
     bloxin::NeuralMassBlox; 
@@ -308,7 +348,7 @@ function (bc::BloxConnector)(
     kwargs...
 )
     neurons_in = get_exci_neurons(wta_in)
-        
+
     for neuron_postsyn in neurons_in
         bc(neuron_out, neuron_postsyn; kwargs...)
     end
diff --git a/src/blox/neural_mass.jl b/src/blox/neural_mass.jl
index 243f6e2f..ca83bd9f 100644
--- a/src/blox/neural_mass.jl
+++ b/src/blox/neural_mass.jl
@@ -1,125 +1,6 @@
 @parameters t
 D = Differential(t)
 
-mutable struct LinearNeuralMassBlox <: AbstractComponent
-    connector::Num
-    odesystem::ODESystem
-    function LinearNeuralMassBlox(;name)
-        states = @variables x(t) jcn(t)
-        eqs = D(x) ~ jcn
-        odesys = ODESystem(eqs, t, states, []; name=name)
-        new(odesys.x, odesys)
-    end
-end
-
-mutable struct HarmonicOscillatorBlox <: NeuralMassBlox
-    # all parameters are Num as to allow symbolic expressions
-    connector::Num
-    noDetail::Vector{Num}
-    detail::Vector{Num}
-    initial::Dict{Num, Tuple{Float64, Float64}}
-    odesystem::ODESystem
-    function HarmonicOscillatorBlox(;name, ω=25*(2*pi), ζ=1.0, k=625*(2*pi), h=35.0)
-        params = progress_scope(ω, ζ, k, h)
-        params = compileparameterlist(ω=params[1], ζ=params[2], k=params[3], h=params[4])
-        sts    = @variables x(t)=1.0 y(t)=1.0 jcn(t)=0.0
-        ω, ζ, k, h = params
-        eqs    = [D(x) ~ y-(2*ω*ζ*x)+ k*(2/π)*(atan((jcn)/h))
-                  D(y) ~ -(ω^2)*x]
-        odesys = ODESystem(eqs, t, sts, params; name=name)
-        new(odesys.x,[odesys.x],[odesys.x,odesys.y],
-            Dict(odesys.x => (-1.0,1.0), odesys.y => (-1.0,1.0)),
-            odesys)
-    end
-end
-# this assignment is temporary until all the code is changed to the new name
-const harmonic_oscillator = HarmonicOscillatorBlox
-
-# This is for later to connect the icons to the different blox
-# function gui.icon(Type::HarmonicOscillatorBlox)
-#    return HarmonicOscillatorImage
-
-mutable struct JansenRitCBlox <: NeuralMassBlox
-    connector::Num
-    noDetail::Vector{Num}
-    detail::Vector{Num}
-    initial::Dict{Num, Tuple{Float64, Float64}}
-    odesystem::ODESystem
-    function JansenRitCBlox(;name, τ=0.001, H=20.0, λ=5.0, r=0.15)
-        params = progress_scope(τ, H, λ, r)
-        params = compileparameterlist(τ=params[1], H=params[2], λ=params[3], r=params[4])
-        sts    = @variables x(t)=1.0 y(t)=1.0 jcn(t)=0.0
-        τ, H, λ, r = params
-        eqs    = [D(x) ~ y - ((2/τ)*x),
-                D(y) ~ -x/(τ*τ) + (H/τ)*((2*λ)/(1 + exp(-r*(jcn))) - λ)]
-        odesys = ODESystem(eqs, t, sts, params; name=name)
-        new(odesys.x,[odesys.x],[odesys.x,odesys.y],
-            Dict(odesys.x => (-1.0,1.0), odesys.y => (-1.0,1.0)),
-            odesys)
-    end
-end
-# this assignment is temporary until all the code is changed to the new name
-const jansen_ritC = JansenRitCBlox
-
-mutable struct  JansenRitSCBlox <: NeuralMassBlox
-    connector::Num
-    noDetail::Vector{Num}
-    detail::Vector{Num}
-    initial::Dict{Num, Tuple{Float64, Float64}}
-    odesystem::ODESystem
-    function JansenRitSCBlox(;name, τ=0.014, H=20.0, λ=400.0, r=0.1)
-        params = progress_scope(τ, H, λ, r)
-        params = compileparameterlist(τ=params[1], H=params[2], λ=params[3], r=params[4])
-        sts    = @variables x(t)=1.0 y(t)=1.0 jcn(t)=0.0
-        τ, H, λ, r = params
-        eqs    = [D(x) ~ y - ((2/τ)*x),
-                  D(y) ~ -x/(τ*τ) + (H/τ)*((2*λ)/(1 + exp(-r*(jcn))) - λ)]
-        odesys = ODESystem(eqs, t, sts, params; name=name)
-        new(odesys.x,[odesys.x],[odesys.x,odesys.y],
-            Dict(odesys.x => (-1.0,1.0), odesys.y => (-1.0,1.0)),
-            odesys)
-    end
-end
-# this assignment is temporary until all the code is changed to the new name
-const jansen_ritSC = JansenRitSCBlox
-
-mutable struct WilsonCowanBlox <: NeuralMassBlox
-    τ_E::Num
-    τ_I::Num
-    a_E::Num
-    a_I::Num
-    c_EE::Num
-    c_EI::Num
-    c_IE::Num
-    c_II::Num
-    θ_E::Num
-    θ_I::Num
-    η::Num
-    connector::Num
-    noDetail::Vector{Num}
-    detail::Vector{Num}
-    odesystem::ODESystem
-    function WilsonCowanBlox(;name,
-                          τ_E=1.0,
-                          τ_I=1.0,
-                          a_E=1.2,
-                          a_I=2.0,
-                          c_EE=5.0,
-                          c_IE=6.0,
-                          c_EI=10.0,
-                          c_II=1.0,
-                          θ_E=2.0,
-                          θ_I=3.5,
-                          η=1.0)
-        params = @parameters τ_E=τ_E τ_I=τ_I a_E=a_E a_I=a_I c_EE=c_EE c_IE=c_IE c_EI=c_EI c_II=c_II θ_E=θ_E θ_I=θ_I η=η
-        sts    = @variables E(t)=1.0 I(t)=1.0 jcn(t)=0.0 P(t)=0.0
-        eqs    = [D(E) ~ -E/τ_E + 1/(1 + exp(-a_E*(c_EE*E - c_IE*I - θ_E + P + η*(jcn)))),
-                  D(I) ~ -I/τ_I + 1/(1 + exp(-a_I*(c_EI*E - c_II*I - θ_I)))]
-        odesys = ODESystem(eqs, t, sts, params; name=name)
-        new(τ_E,τ_I,a_E, a_I, c_EE, c_IE, c_EI, c_II, θ_E, θ_I, η, odesys.E, [odesys.E],[odesys.E, odesys.I],odesys)
-    end
-end
-
 mutable struct NextGenerationBlox <: NeuralMassBlox
     C::Num
     Δ::Num
@@ -199,105 +80,27 @@ end
 # this assignment is temporary until all the code is changed to the new name
 const next_generation = NextGenerationBlox
 
-mutable struct LarterBreakspearBlox <: NeuralMassBlox
-    C::Num
-    δ_VZ::Num
-    T_Ca::Num
-    δ_Ca::Num
-    g_Ca::Num
-    V_Ca::Num
-    T_K::Num
-    δ_K::Num
-    g_K::Num
-    V_K::Num
-    T_Na::Num
-    δ_Na::Num
-    g_Na::Num
-    V_Na::Num
-    V_L::Num
-    g_L::Num
-    V_T::Num
-    Z_T::Num
-    Q_Vmax::Num
-    Q_Zmax::Num
-    IS::Num
-    a_ee::Num
-    a_ei::Num
-    a_ie::Num
-    a_ne::Num
-    a_ni::Num
-    b::Num
-    τ_K::Num
-    ϕ::Num
-    r_NMDA::Num
-    connector::Num
-    noDetail::Vector{Num}
-    detail::Vector{Num}
-    initial::Dict{Num, Tuple{Float64, Float64}}
-    odesystem::ODESystem
-    function LarterBreakspearBlox(;name,
-                          T_Ca=-0.01,
-                          δ_Ca=0.15,
-                          g_Ca=1.0,
-                          V_Ca=1.0,
-                          T_K=0.0,
-                          δ_K=0.3,
-                          g_K=2.0,
-                          V_K=-0.7,
-                          T_Na=0.3,
-                          δ_Na=0.15,
-                          g_Na=6.7,
-                          V_Na=0.53,
-                          V_L=-0.5,
-                          g_L=0.5,
-                          V_T=0.0,
-                          Z_T=0.0,
-                          δ_VZ=0.61,
-                          Q_Vmax=1.0,
-                          Q_Zmax=1.0,
-                          IS = 0.3,
-                          a_ee=0.36,
-                          a_ei=2.0,
-                          a_ie=2.0,
-                          a_ne=1.0,
-                          a_ni=0.4,
-                          b=0.1,
-                          τ_K=1.0,
-                          ϕ=0.7,
-                          r_NMDA=0.25,
-                          C=0.35)
-        params = @parameters C=C δ_VZ=δ_VZ T_Ca=T_Ca δ_Ca=δ_Ca g_Ca=g_Ca V_Ca=V_Ca T_K=T_K δ_K=δ_K g_K=g_K V_K=V_K T_Na=T_Na δ_Na=δ_Na g_Na=g_Na V_Na=V_Na V_L=V_L g_L=g_L V_T=V_T Z_T=Z_T Q_Vmax=Q_Vmax Q_Zmax=Q_Zmax IS=IS a_ee=a_ee a_ei=a_ei a_ie=a_ie a_ne=a_ne a_ni=a_ni b=b τ_K=τ_K ϕ=ϕ r_NMDA=r_NMDA
-        sts    = @variables V(t)=0.5 Z(t)=0.5 W(t)=0.5 jcn(t)=0.0 Q_V(t) Q_Z(t) m_Ca(t) m_Na(t) m_K(t)
-
-        eqs    = [D(V) ~ -(g_Ca + (1 - C) * r_NMDA * a_ee * Q_V + C * r_NMDA * a_ee * jcn) * m_Ca * (V-V_Ca) -
-                          g_K * W * (V - V_K) - g_L * (V - V_L) -
-                          (g_Na * m_Na + (1 - C) * a_ee * Q_V + C * a_ee * jcn) * (V-V_Na) -
-                          a_ie * Z * Q_Z + a_ne * IS,
-                  D(Z) ~ b * (a_ni * IS + a_ei * V * Q_V),
-                  D(W) ~ ϕ * (m_K - W) / τ_K,
-                  Q_V ~ 0.5*Q_Vmax*(1 + tanh((V-V_T)/δ_VZ)),
-                  Q_Z ~ 0.5*Q_Zmax*(1 + tanh((Z-Z_T)/δ_VZ)),
-                  m_Ca ~  0.5*(1 + tanh((V-T_Ca)/δ_Ca)),
-                  m_Na ~  0.5*(1 + tanh((V-T_Na)/δ_Na)),
-                  m_K ~  0.5*(1 + tanh((V-T_K)/δ_K))]
-        odesys = ODESystem(eqs, t, sts, params; name=name)
-        new(C, δ_VZ, T_Ca, δ_Ca, g_Ca, V_Ca, T_K, δ_K, g_K, V_K, T_Na, δ_Na, g_Na,V_Na, V_L,
-        g_L, V_T, Z_T, Q_Vmax, Q_Zmax, IS, a_ee, a_ei, a_ie, a_ne, a_ni, b, τ_K, ϕ, r_NMDA,
-        0.5*Q_Vmax*(1 + tanh((odesys.V-V_T)/δ_VZ)),
-        [odesys.V],[odesys.V, odesys.Z, odesys.W],
-        Dict(odesys.V => (-1.0,1.0), odesys.Z => (-1.0,1.0), odesys.W => (0.0,1.0)),
-        odesys)
-    end
-end
-
-"""
-New versions of blox begin here!
 """
+    LinearNeuralMass(name, namespace)
 
+Create standard linear neural mass blox with a single internal state.
+There are no parameters in this blox.
+This is a blox of the sort used for spectral DCM modeling.
+The formal definition of this blox is:
 
+
+```math
+\\frac{d}{dx} = \\sum{jcn}
+```
+
+where ``jcn``` is any input to the blox.
+
+
+Arguments:
+- name: Options containing specification about deterministic.
+- namespace: Additional namespace above name if needed for inheritance.
 """
-Units note: no units because no parameters :)
-"""
+
 struct LinearNeuralMass <: NeuralMassBlox
     output
     jcn
@@ -312,8 +115,25 @@ struct LinearNeuralMass <: NeuralMassBlox
 end
 
 """
-Units note: Frequency should be tuned by user.
-Updated with additional factors to make ms.
+    HarmonicOscillator(name, namespace, ω, ζ, k, h)
+
+    Create a harmonic oscillator blox with the specified parameters.
+    The formal definition of this blox is:
+
+```math
+\\frac{dx}{dt} = y-(2*\\omega*\\zeta*x)+ k*(2/\\pi)*(atan((\\sum{jcn})/h)
+\\frac{dy}{dt} = -(\\omega^2)*x
+```
+    where ``jcn`` is any input to the blox.
+    
+
+Arguments:
+- name: Name given to ODESystem object within the blox.
+- namespace: Additional namespace above name if needed for inheritance.
+- ω: Base frequency. Note the default value is scaled to give oscillations in milliseconds to match other blocks.
+- ζ: Damping ratio.
+- k: Gain.
+- h: Threshold.
 """
 struct HarmonicOscillator <: NeuralMassBlox
     params
@@ -322,8 +142,8 @@ struct HarmonicOscillator <: NeuralMassBlox
     odesystem
     namespace
     function HarmonicOscillator(;name, namespace=nothing, ω=25*(2*pi)*0.001, ζ=1.0, k=625*(2*pi), h=35.0)
-        p = progress_scope(ω, ζ, k, h)
-        p = compileparameterlist(ω=p[1], ζ=p[2], k=p[3], h=p[4])
+        # p = progress_scope(ω, ζ, k, h)
+        p = paramscoping(ω=ω, ζ=ζ, k=k, h=h)
         sts    = @variables x(t)=1.0 [output=true] y(t)=1.0 jcn(t)=0.0 [input=true]
         ω, ζ, k, h = p
         eqs    = [D(x) ~ y-(2*ω*ζ*x)+ k*(2/π)*(atan((jcn)/h))
@@ -334,10 +154,31 @@ struct HarmonicOscillator <: NeuralMassBlox
 end
 
 """
-Units note: all units from the original Parkinson's paper EXCEPT τ. 
-The original delays were in seconds, so multiplied to be consistent with other blocks in ms.
+    JansenRit(name, namespace, τ, H, λ, r, cortical)
+
+    Create a Jansen Rit blox as described in Liu et al.
+    The formal definition of this blox is:
+
+```math
+\\frac{dx}{dt} = y-\\frac{2}{\\tau}x
+\\frac{dy}{dt} = -\\frac{x}{\\tau^2} + \\frac{H}{\\tau} [\\frac{2\\lambda}{1+\\text{exp}(-r*\\sum{jcn})} - \\lambda]
+```
+
+where ``jcn`` is any input to the blox.
+
+Arguments:
+- name: Name given to ODESystem object within the blox.
+- namespace: Additional namespace above name if needed for inheritance.
+- τ: Time constant. This is changed from the original source as the time constant was in seconds, while all our blocks are in milliseconds.
+- H: See equation for use.
+- λ: See equation for use.
+- r: See equation for use.
+- cortical: Boolean to determine whether to use cortical or subcortical parameters. Specifying any of the parameters above will override this.
+
+Citations:
+1. Liu C, Zhou C, Wang J, Fietkiewicz C, Loparo KA. The role of coupling connections in a model of the cortico-basal ganglia-thalamocortical neural loop for the generation of beta oscillations. Neural Netw. 2020 Mar;123:381-392. doi: 10.1016/j.neunet.2019.12.021.
+
 """
-# Constructing a new Jansen Rit blox to handle both delays and non-delays, along with default parameter inputs
 struct JansenRit <: NeuralMassBlox
     params
     output
@@ -357,8 +198,8 @@ struct JansenRit <: NeuralMassBlox
         λ = isnothing(λ) ? (cortical ? 5.0 : 400.0) : λ
         r = isnothing(r) ? (cortical ? 0.15 : 0.1) : r
 
-        p = progress_scope(τ, H, λ, r)
-        p = compileparameterlist(τ=p[1], H=p[2], λ=p[3], r=p[4])
+        # p = progress_scope(τ, H, λ, r)
+        p = paramscoping(τ=τ, H=H, λ=λ, r=r)
         τ, H, λ, r = p
         sts = @variables x(..)=1.0 [output=true] y(t)=1.0 jcn(t)=0.0 [input=true] 
         eqs = [D(x(t)) ~ y - ((2/τ)*x(t)),
@@ -371,8 +212,21 @@ struct JansenRit <: NeuralMassBlox
 end
 
 """
-Units note: Unclear where the defaults come from (close but not quite Wilson-Cowan referenced in TVB and elsewhere).
-They're on the same order of magnitude as the original parameters which are in ms, so good to go for now.
+    WilsonCown(name, namespace, τ_E, τ_I, a_E, a_I, c_EE, c_IE, c_EI, c_II, θ_E, θ_I, η)
+
+    Create a standard Wilson Cowan blox.
+    The formal definition of this blox is:
+
+```math
+\\frac{dE}{dt} = \\frac{-E}{\\tau_E} + \\frac{1}{1 + \\text{exp}(-a_E*(c_{EE}*E - c_{IE}*I - \\theta_E + \\eta*(\\sum{jcn}))}
+\\frac{dI}{dt} = \\frac{-I}{\\tau_I} + \\frac{1}{1 + exp(-a_I*(c_{EI}*E - c_{II}*I - \\theta_I)}
+```
+where ``jcn`` is any input to the blox.
+
+Arguments:
+- name: Name given to ODESystem object within the blox.
+- namespace: Additional namespace above name if needed for inheritance.
+- Others: See equation for use.
 """
 struct WilsonCowan <: NeuralMassBlox
     params
@@ -394,8 +248,8 @@ struct WilsonCowan <: NeuralMassBlox
                         θ_I=3.5,
                         η=1.0
     )
-        p = progress_scope(τ_E, τ_I, a_E, a_I, c_EE, c_IE, c_EI, c_II, θ_E, θ_I, η)
-        p = compileparameterlist(τ_E=p[1], τ_I=p[2], a_E=p[3], a_I=p[4], c_EE=p[5], c_IE=p[6], c_EI=p[7], c_II=p[8], θ_E=p[9], θ_I=p[10], η=p[11])
+        # p = progress_scope(τ_E, τ_I, a_E, a_I, c_EE, c_IE, c_EI, c_II, θ_E, θ_I, η)
+        p = paramscoping(τ_E=τ_E, τ_I=τ_I, a_E=a_E, a_I=a_I, c_EE=c_EE, c_IE=c_IE, c_EI=c_EI, c_II=c_II, θ_E=θ_E, θ_I=θ_I, η=η)
         τ_E, τ_I, a_E, a_I, c_EE, c_IE, c_EI, c_II, θ_E, θ_I, η = p
         sts = @variables E(t)=1.0 [output=true] I(t)=1.0 jcn(t)=0.0 [input=true] #P(t)=0.0
         eqs = [D(E) ~ -E/τ_E + 1/(1 + exp(-a_E*(c_EE*E - c_IE*I - θ_E + η*(jcn)))), #old form: D(E) ~ -E/τ_E + 1/(1 + exp(-a_E*(c_EE*E - c_IE*I - θ_E + P + η*(jcn)))),
@@ -406,7 +260,21 @@ struct WilsonCowan <: NeuralMassBlox
 end
 
 """
-Units note: From Yamashita et al. paper, designed to be in ms. Good to go for now.
+    LarterBreakspear(name, namespace, ...)
+
+    Create a Larter Breakspear blox described in Endo et al. For a full list of the parameters used see the reference.
+    If you need to modify the parameters, see Chesebro et al. and van Nieuwenhuizen et al. for physiological ranges.
+
+Arguments:
+- name: Name given to ODESystem object within the blox.
+- namespace: Additional namespace above name if needed for inheritance.
+- Other parameters: See reference for full list. Note that parameters are scaled so that units of time are in milliseconds.
+
+Citations:
+1. Endo H, Hiroe N, Yamashita O. Evaluation of Resting Spatio-Temporal Dynamics of a Neural Mass Model Using Resting fMRI Connectivity and EEG Microstates. Front Comput Neurosci. 2020 Jan 17;13:91. doi: 10.3389/fncom.2019.00091.
+2. Chesebro AG, Mujica-Parodi LR, Weistuch C. Ion gradient-driven bifurcations of a multi-scale neuronal model. Chaos Solitons Fractals. 2023 Feb;167:113120. doi: 10.1016/j.chaos.2023.113120. 
+3. van Nieuwenhuizen, H, Chesebro, AG, Polis, C, Clarke, K, Strey, HH, Weistuch, C, Mujica-Parodi, LR. Ketosis regulates K+ ion channels, strengthening brain-wide signaling disrupted by age. Preprint. bioRxiv 2023.05.10.540257; doi: https://doi.org/10.1101/2023.05.10.540257. 
+
 """
 struct LarterBreakspear <: NeuralMassBlox
     params
@@ -448,8 +316,8 @@ struct LarterBreakspear <: NeuralMassBlox
                         r_NMDA=0.25,
                         C=0.35
     )
-        p = progress_scope(C, δ_VZ, T_Ca, δ_Ca, g_Ca, V_Ca, T_K, δ_K, g_K, V_K, T_Na, δ_Na, g_Na, V_Na, V_L, g_L, V_T, Z_T, Q_Vmax, Q_Zmax, IS, a_ee, a_ei, a_ie, a_ne, a_ni, b, τ_K, ϕ,r_NMDA)
-        p = compileparameterlist(C=p[1], δ_VZ=p[2], T_Ca=p[3], δ_Ca=p[4], g_Ca=p[5], V_Ca=p[6], T_K=p[7], δ_K=p[8], g_K=p[9], V_K=p[10], T_Na=p[11], δ_Na=p[12], g_Na=p[13],V_Na=p[14], V_L=p[15], g_L=p[16], V_T=p[17], Z_T=p[18], Q_Vmax=p[19], Q_Zmax=p[20], IS=p[21], a_ee=p[22], a_ei=p[23], a_ie=p[24], a_ne=p[25], a_ni=p[26], b=p[27], τ_K=p[28], ϕ=p[29], r_NMDA=p[30])
+        # p = progress_scope(C, δ_VZ, T_Ca, δ_Ca, g_Ca, V_Ca, T_K, δ_K, g_K, V_K, T_Na, δ_Na, g_Na, V_Na, V_L, g_L, V_T, Z_T, Q_Vmax, Q_Zmax, IS, a_ee, a_ei, a_ie, a_ne, a_ni, b, τ_K, ϕ,r_NMDA)
+        p = paramscoping(C=C, δ_VZ=δ_VZ, T_Ca=T_Ca, δ_Ca=δ_Ca, g_Ca=g_Ca, V_Ca=V_Ca, T_K=T_K, δ_K=δ_K, g_K=g_K, V_K=V_K, T_Na=T_Na, δ_Na=δ_Na, g_Na=g_Na, V_Na=V_Na, V_L=V_L, g_L=g_L, V_T=V_T, Z_T=Z_T, Q_Vmax=Q_Vmax, Q_Zmax=Q_Zmax, IS=IS, a_ee=a_ee, a_ei=a_ei, a_ie=a_ie, a_ne=a_ne, a_ni=a_ni, b=b, τ_K=τ_K, ϕ=ϕ, r_NMDA=r_NMDA)
         C, δ_VZ, T_Ca, δ_Ca, g_Ca, V_Ca, T_K, δ_K, g_K, V_K, T_Na, δ_Na, g_Na,V_Na, V_L, g_L, V_T, Z_T, Q_Vmax, Q_Zmax, IS, a_ee, a_ei, a_ie, a_ne, a_ni, b, τ_K, ϕ, r_NMDA = p
         
         sts = @variables V(t)=0.5 Z(t)=0.5 W(t)=0.5 jcn(t)=0.0 [input=true] Q_V(t) [output=true] Q_Z(t) m_Ca(t) m_Na(t) m_K(t)
diff --git a/src/blox/neuron_models.jl b/src/blox/neuron_models.jl
index f7c88b4b..71b973fe 100644
--- a/src/blox/neuron_models.jl
+++ b/src/blox/neuron_models.jl
@@ -33,30 +33,6 @@ mutable struct QIFNeuronBlox <: AbstractNeuronBlox
 	end
 end
 
-"""
-thetaneuron has the following parameters:
-    η:     Constant drive
-    α_inv: Time to peak of spike
-    k:     All-to-all coupling strength
-and the following variables:
-    θ(t):   Theta neuron state
-    g(t):   Synaptic current
-    jcn(t): Synaptic input
-and returns:
-    an ODE System
-"""
-function theta_neuron(;name, η=η, α_inv=α_inv, k=k)
-
-    params = @parameters η=η α_inv=α_inv k=k
-    sts    = @variables θ(t)=0.0 g(t)=0.0 jcn(t)=0.0
-    
-    eqs = [D(θ) ~ 1-cos(θ) + (1+cos(θ))*(η + k*g),
-          D(g) ~ α_inv*(jcn - g)]
-
-    return ODESystem(eqs, t, sts, params; name=name)
-
-end
-
 # Leaky Integrate and Fire neuron with synaptic dynamics
 mutable struct IFNeuronBlox <: AbstractNeuronBlox
     # all parameters are Num as to allow symbolic expressions
diff --git a/src/datafitting/spectralDCM.jl b/src/datafitting/spectralDCM.jl
index 0d68d622..bf847229 100644
--- a/src/datafitting/spectralDCM.jl
+++ b/src/datafitting/spectralDCM.jl
@@ -13,19 +13,22 @@ spm_logdet       : mimick SPM12's way to compute the logarithm of the determinan
 variationalbayes : main routine that computes the variational Bayes estimate of model parameters
 """
 
-using ForwardDiff
-using ForwardDiff: Dual
-using ForwardDiff: Partials
-using LinearAlgebra: Eigen
-using LinearAlgebra
-# using ToeplitzMatrices
-using ExponentialUtilities
-
-ForwardDiff.can_dual(::Type{Complex{Float64}}) = true
-using ChainRules: _eigen_norm_phase_fwd!
 tagtype(::Dual{T,V,N}) where {T,V,N} = T
 
+"""
+    function LinearAlgebra.eigen(M::Matrix{Dual{T, P, np}}) where {T, P, np}
+    
+    Dispatch of LinearAlgebra.eigen for dual matrices with complex numbers. Make the eigenvalue decomposition 
+    amenable to automatic differentiation. To do so compute the analytical derivative of eigenvalues
+    and eigenvectors. 
+
+    Arguments:
+    - `M`: matrix of type Dual of which to compute the eigenvalue decomposition. 
+
+    Returns:
+    - `Eigen(evals, evecs)`: eigenvalue decomposition returned as type LinearAlgebra.Eigen
 
+"""
 function LinearAlgebra.eigen(M::Matrix{Dual{T, P, np}}) where {T, P, np}
     nd = size(M, 1)
     A = (p->p.value).(M)
@@ -182,12 +185,21 @@ end
     return y
 end
 
+"""
+    function matlab_norm(A, p)
+
+    Simple helper function to implement the norm of a matrix that is equivalent to the one given in MATLAB for order=1, 2, Inf. 
+    This is needed for the reproduction of the exact same results of SPM12.
 
-function matlab_norm(A, p)
+    Arguments:
+    - `A`: matrix
+    - `p`: order of norm
+"""
+function matlab_norm(M, p)
     if p == 1
-        return maximum(vec(sum(abs.(A),dims=1)))
+        return maximum(vec(sum(abs.(M),dims=1)))
     elseif p == Inf
-        return maximum(vec(sum(abs.(A),dims=2)))
+        return maximum(vec(sum(abs.(M),dims=2)))
     elseif p == 2
         print("Not implemented yet!\n")
         return NaN
@@ -206,15 +218,23 @@ function csd_Q(csd)
             end
         end
     end
-    Q = inv(Q .+ matlab_norm(Q, 1)/32*la.Matrix(la.I, size(Q)))   # TODO: MATLAB's and Julia's norm function are different! Reconciliate?
+    Q = inv(Q .+ matlab_norm(Q, 1)/32*Matrix(I, size(Q)))   # TODO: MATLAB's and Julia's norm function are different! Reconciliate?
     return Q
 end
 
+"""
+    function spm_logdet(M)
+
+    SPM12 style implementation of the logarithm of the determinant of a matrix.
+
+    Arguments:
+    - `M`: matrix
+"""
 function spm_logdet(M)
     TOL = 1e-16
-    s = la.diag(M)
+    s = diag(M)
     if sum(abs.(s)) != sum(abs.(M[:]))
-        ~, s, ~ = la.svd(M)
+        ~, s, ~ = svd(M)
     end
     return sum(log.(s[(s .> TOL) .& (s .< TOL^-1)]))
 end
@@ -228,6 +248,14 @@ mutable struct vb_state
     Σθ::Matrix{Float64}
 end
 
+"""
+    vecparam(param::OrderedDict{Any, Any})
+
+    Function to flatten an ordered dictionary of model parameters and return a simple list of parameter values.
+
+    Arguments:
+    - `param`: dictionary of model parameters (may contain numbers and lists of numbers)
+"""
 function vecparam(param::OrderedDict{Any, Any})
     flatparam = Float64[]
     for v in values(param)
@@ -242,22 +270,22 @@ function vecparam(param::OrderedDict{Any, Any})
     return flatparam
 end
 
-function unvecparam(vals, param::OrderedDict{Any,Any})
-    iter = 1
-    paramnewvals = copy(param)
-    for (k, v) in param
-        if (typeof(v) <: Array)
-            paramnewvals[k] = vals[iter:iter+length(v)-1]
-            iter += length(v)
-        else
-            paramnewvals[k] = vals[iter]
-            iter += 1
-        end
-    end
-    return paramnewvals
-end
-
-
+"""
+    variationalbayes(idx_A, y, derivatives, w, V, p, priors, niter)
+
+    Computes parameter estimation using variational Laplace that is to a large extend equivalent to the SPM12 implementation
+    and provides the exact same values.
+
+    Arguments:
+    - `idx_A`: indices of connection weight parameter matrix A in model Jacobian
+    - `y`: empirical cross-spectral density (input data)
+    - `derivatives`: jacobian of model as well as gradient of observer function
+    - `w`: fequencies at which to estimate cross-spectral densities
+    - `V`: projection matrix from full parameter space to reduced space that removes parameters with zero variance prior
+    - `p`: order of multivariate autoregressive model for estimation of cross-spectral densities from data
+    - `priors`: Bayesian priors, mean and variance thereof. Laplace approximation assumes Gaussian distributions
+    - `niter`: number of iterations of the optimization procedure
+"""
 @views function variationalbayes(idx_A, y, derivatives, w, V, p, priors, niter)
     # extract priors
     Πθ_pr = priors[:Σ][:Πθ_pr]
@@ -290,7 +318,7 @@ end
     local ϵ_λ, iΣ, Σλ, Σθ, dFdpp, dFdp
     for k = 1:niter
         state.iter = k
-        dfdp = ForwardDiff.jacobian(f_prep, μθ_po) * V
+        dfdp = jacobian(f_prep, μθ_po) * V
         norm_dfdp = matlab_norm(dfdp, Inf);
         revert = isnan(norm_dfdp) || norm_dfdp > exp(32);
 
@@ -311,7 +339,7 @@ end
 
                 # J_test = JacVec(f_prep, μθ_po)
                 # dfdp = stack(J_test*v for v in eachcol(V))
-                dfdp = ForwardDiff.jacobian(f_prep, μθ_po) * V
+                dfdp = jacobian(f_prep, μθ_po) * V
 
                 # check for stability
                 norm_dfdp = matlab_norm(dfdp, Inf);
@@ -441,26 +469,28 @@ end
 end
 
 """
-Performs a variational inference to fit a cross spectral density. Current implementation provides a Variational Laplace fit. 
-(ToDo: generalize to different VI algorithms)
-
-Input:
-- data             : Dataframe with column names corresponding to the regions of measurement.
-- neuraldynmodel   : MTK model, it is an ODESystem or a System (haven't tested with System yet).
-- observationmodel : MTK model that defines measurement function (ex. bold signal). 
-                     Current implementation limits to one measurement functional form for all regions.
-- initcond         : Dictionary of initial conditions, numerical values for all states
-- csdsetup         : Dictionary of parameters required for the computation of the cross spectral density
--- dt              : sampling interval
--- freq            : frequencies at which to evaluate the CSD
--- p               : order parameter of the multivariate autoregression model
-- params           : Dataframe of parameters with the following columns:
--- name            : corresponds to MTK model name
--- mean            : corresponds to prior mean value
--- variance        : corresponds to the prior variances
-- hyperparams      : Dataframe of parameters with the following columns:
--- Πλ_pr           : prior precision matrix for λ hyperparameter(s)
--- μλ_pr           : prior mean(s) for λ hyperparameter(s)
+    spectralVI(data, neuraldynmodel, observationmodel, initcond, csdsetup, params, hyperparams)
+
+    Interface function to performs variational inference to fit model parameters to empirical cross spectral density.
+    The current implementation provides a Variational Laplace fit (see function above `variationalbayes`).
+
+    Arguments:
+    - `data`             : Dataframe with column names corresponding to the regions of measurement.
+    - `neuraldynmodel`   : MTK model, it is an ODESystem or a System (haven't tested with System yet).
+    - `observationmodel` : MTK model that defines measurement function (ex. bold signal). 
+                           Current implementation limits to one measurement functional form for all regions.
+    - `initcond`         : Dictionary of initial conditions, numerical values for all states
+    - `csdsetup`         : Dictionary of parameters required for the computation of the cross spectral density
+    -- `dt`              : sampling interval
+    -- `freq`            : frequencies at which to evaluate the CSD
+    -- `p`               : order parameter of the multivariate autoregression model
+    - `params`           : Dataframe of parameters with the following columns:
+    -- `name`            : corresponds to MTK model name
+    -- `mean`            : corresponds to prior mean value
+    -- `variance`        : corresponds to the prior variances
+    - `hyperparams`      : Dataframe of parameters with the following columns:
+    -- `Πλ_pr`           : prior precision matrix for λ hyperparameter(s)
+    -- `μλ_pr`           : prior mean(s) for λ hyperparameter(s)
 """
 function spectralVI(data, neuraldynmodel, observationmodel, initcond, csdsetup, params, hyperparams)
     # compute cross-spectral density
diff --git a/src/gui/GUI.jl b/src/gui/GUI.jl
index 6b630b1e..4f8c1d94 100644
--- a/src/gui/GUI.jl
+++ b/src/gui/GUI.jl
@@ -61,21 +61,6 @@ const MENU = "menu"
 const NODE = "node"
 const LINK = "link"
 
-# function arguments(::Type{Neuroblox.Merger})
-#   OrderedDict(
-#   )
-# end
-
-# function arguments(::Type{Neuroblox.LinHemo})
-#   OrderedDict(
-#   )
-# end
-
-# function arguments(::Type{Neuroblox.Hemodynamics})
-#   OrderedDict(
-#   )
-# end
-
 function arguments(::Type{Neuroblox.ImageStimulus})
   OrderedDict(
     :height => NCAD(15, INTEGER, 1, 100,[], false),
@@ -110,44 +95,6 @@ function outputs(::Type{Neuroblox.WinnerTakeAllBlox})
   ["out1","out2","out3","out4","out5"]
 end
 
-# function arguments(::Type{Neuroblox.LinearNeuralMassBlox})
-#   OrderedDict(
-#     :τ => NCAD(0.01, NUMBER, 1.0, 10.0,[]),
-#   )
-# end
-
-# function arguments(::Type{Neuroblox.LinearNeuralMass})
-#   OrderedDict(
-#     :τ => NCAD(0.01, NUMBER, 1.0, 10.0,[]),
-#   )
-# end
-
-#TODO: create connector compatible stochastic blox
-# function arguments(::Type{Neuroblox.OUBlox})
-#   OrderedDict(
-#     :μ => NCAD(0.0, NUMBER, -100.0, 100.0,[]),
-#     :τ => NCAD(1.0, NUMBER, 0.00001,10,[]),
-#     :σ => NCAD(1.0, NUMBER, 0.00001,10,[])
-#   )
-# end
-
-# function arguments(::Type{Neuroblox.OUCouplingBlox})
-#   OrderedDict(
-#     :μ => NCAD(0.0, NUMBER, -100.0, 100.0,[]),
-#     :τ => NCAD(1.0, NUMBER, 0.00001,10,[]),
-#     :σ => NCAD(1.0, NUMBER, 0.00001,10,[])
-#   )
-# end
-
-# function arguments(::Type{Neuroblox.HarmonicOscillatorBlox})
-#   OrderedDict(
-#     :ω => NCAD(25*(2*pi), NUMBER, (2*pi), 150*(2*pi),[]),
-#     :ζ => NCAD(1.0, NUMBER, -1.0, 1.0,[]),
-#     :k => NCAD(625*(2*pi), NUMBER, (2*pi), 22500*(2*pi),[]),
-#     :h => NCAD(35.0, NUMBER, 0.01, 90.0,[])
-#   )
-# end
-
 function arguments(::Type{Neuroblox.HarmonicOscillator})
   OrderedDict(
     #:measurement => NCAD("Measurement", MENU, 1 , 4 ,["none","fMRI", "EEG", "LFP"]),
@@ -163,24 +110,6 @@ function plotdetail(::Type{Neuroblox.HarmonicOscillator})
   OrderedDict(:detail => ["x","y"], :nodetail => ["x"])
 end
 
-# function arguments(::Type{Neuroblox.JansenRitCBlox})
-#   OrderedDict(
-#     :τ => NCAD(0.001, NUMBER, 0.001, 2.0,[]),
-#     :H => NCAD(20.0, NUMBER, 0.0, 500.0,[]),
-#     :λ => NCAD(5.0, NUMBER, 1.0, 25.0,[]),
-#     :r => NCAD(0.15, NUMBER, 0.1, 5.0,[])
-#   )
-# end
-
-# function arguments(::Type{Neuroblox.JansenRitSCBlox})
-#   OrderedDict(
-#     :τ => NCAD(0.014, NUMBER, 0.001, 0.1,[]),
-#     :H => NCAD(20.0, NUMBER, 0.0, 500.0,[]),
-#     :λ => NCAD(400.0, NUMBER, 20.0, 500.0,[]),
-#     :r => NCAD(0.1, NUMBER, 0.1, 5.0,[])
-#   )
-# end
-
 function arguments(::Type{Neuroblox.JansenRit})
   OrderedDict(
     :τ => NCAD(0.014, NUMBER, 0.001, 0.1,[],true),
@@ -194,22 +123,6 @@ function plotdetail(::Type{Neuroblox.JansenRit})
   OrderedDict(:detail => ["x","y"], :nodetail => ["x"])
 end
 
-# function arguments(::Type{Neuroblox.WilsonCowanBlox})
-#   OrderedDict(
-#     :τ_E => NCAD(1.0, NUMBER, 1.0, 100.0,[]),
-#     :τ_I => NCAD(1.0, NUMBER, 1.0, 100.0,[]),
-#     :a_E => NCAD(1.2, NUMBER, 1.0, 100.0,[]),
-#     :a_I => NCAD(2.0, NUMBER, 1.0, 100.0,[]),
-#     :c_EE => NCAD(5.0, NUMBER, 1.0, 100.0,[]),
-#     :c_EI => NCAD(10.0, NUMBER, 1.0, 100.0,[]),
-#     :c_IE => NCAD(6.0, NUMBER, 1.0, 100.0,[]),
-#     :c_II => NCAD(1.0, NUMBER, 1.0, 100.0,[]),
-#     :θ_E => NCAD(2.0, NUMBER, 1.0, 100.0,[]),
-#     :θ_I => NCAD(3.5, NUMBER, 1.0, 100.0,[]),
-#     :η => NCAD(1.0, NUMBER, 1.0, 100.0,[])
-#   )
-# end
-
 function arguments(::Type{Neuroblox.WilsonCowan})
   OrderedDict(
     :τ_E => NCAD(1.0, NUMBER, 1.0, 100.0,[],true),
@@ -230,39 +143,6 @@ function plotdetail(::Type{Neuroblox.WilsonCowan})
   OrderedDict(:detail => ["E","I"], :nodetail => ["E"])
 end
 
-# function arguments(::Type{Neuroblox.LarterBreakspearBlox})
-#   OrderedDict(
-#     :C => NCAD(0.35, NUMBER, 0.0, 1.0,[]),
-#     :δ_VZ => NCAD(0.61, NUMBER, 0.1, 2.0,[]),
-#     :T_Ca => NCAD(-0.01, NUMBER, 0.02, -0.04,[]),
-#     :δ_Ca => NCAD(0.15, NUMBER, 0.1, 0.2,[]),
-#     :g_Ca => NCAD(1.0, NUMBER, 0.96, 1.01,[]), #tested in Jolien's work/similar to V_Ca in Anthony's paper
-#     :V_Ca => NCAD(1.0, NUMBER, 0.96, 1.01,[]), #limits established by bifurcation
-#     :T_K => NCAD(0.0, NUMBER, -0.05, 0.05,[]),
-#     :δ_K => NCAD(0.3, NUMBER,0.25, 0.35,[]),
-#     :g_K => NCAD(2.0, NUMBER, 1.95, 2.05,[]),  #tested in Jolien's work
-#     :V_K => NCAD(-0.7, NUMBER, -0.8, -0.6,[]),  #limits established by bifurcation
-#     :T_Na => NCAD(0.3, NUMBER, 0.25, 0.35,[]),
-#     :δ_Na => NCAD(0.15, NUMBER, 0.1, 0.2,[]),
-#     :g_Na => NCAD(6.7, NUMBER, 6.6, 6.8,[]),   #tested in Botond and Jolien's work
-#     :V_Na => NCAD(0.53, NUMBER, 0.41, 0.59,[]), #limits established by bifurcation
-#     :V_L => NCAD(-0.5, NUMBER, -0.6, -0.4,[]),
-#     :g_L => NCAD(0.5, NUMBER, 0.4, 0.6,[]),
-#     :V_T => NCAD(0.0, NUMBER, -0.05, 0.05,[]),
-#     :Z_T => NCAD(0.0, NUMBER, -0.05, 0.05,[]),
-#     :IS => NCAD(0.3, NUMBER, 0.0, 1.0,[]),
-#     :a_ee => NCAD(0.36, NUMBER, 0.33, 0.39,[]), #tested in Botond and Jolien's work
-#     :a_ei => NCAD(2.0, NUMBER, 1.95, 2.05,[]), #tested in Botond and Jolien's work
-#     :a_ie => NCAD(2.0, NUMBER, 1.95, 2.05,[]), #testing in Jolien's work
-#     :a_ne => NCAD(1.0, NUMBER, 0.95, 1.05,[]),
-#     :a_ni => NCAD(0.4, NUMBER, 0.3, 0.5,[]),
-#     :b => NCAD(0.1, NUMBER, 0.05, 0.15,[]),
-#     :τ_K => NCAD(1.0, NUMBER, 0.8, 1.2,[]), #shouldn't be varied, but useful in bifurcations to "harshen" the potassium landscape
-#     :ϕ => NCAD(0.7, NUMBER, 0.6, 0.8,[]),
-#     :r_NMDA => NCAD( 0.25, NUMBER, 0.2, 0.3,[]) #tested in Botond's work
-#   )
-# end
-
 function arguments(::Type{Neuroblox.LarterBreakspear})
   OrderedDict(
     :C => NCAD(0.35, NUMBER, 0.0, 1.0,[],true),
@@ -300,17 +180,6 @@ function plotdetail(::Type{Neuroblox.LarterBreakspear})
   OrderedDict(:detail => ["V","Z","W"], :nodetail => ["V"])
 end
 
-# function arguments(::Type{Neuroblox.NextGenerationBlox})
-#   OrderedDict(
-#     :C => NCAD(30.0, NUMBER, 1.0, 50.0,[]),
-#     :Δ => NCAD(1.0, NUMBER, 0.01, 100.0,[]),
-#     :η_0 => NCAD(5.0, NUMBER, 0.01, 20.0,[]),
-#     :v_syn => NCAD(-10.0, NUMBER, -20.0, 0.0,[]),
-#     :alpha_inv => NCAD(35.0, NUMBER, 0.01, 10.0,[]),
-#     :k => NCAD(0.105, NUMBER, 0.01, 2.0,[])
-#   )
-# end
-
 function arguments(::Type{Neuroblox.NextGenerationEIBlox})
   OrderedDict(
     :Cₑ => NCAD(52.0, NUMBER, 1.0, 50.0,[],true),
@@ -366,28 +235,6 @@ function info_link(::Type{Neuroblox.HebbianPlasticity})
   Dict(:link => "https://www.neuroblox.org")
 end
 
-#TODO: create connector compatible CMC
-# function arguments(::Type{Neuroblox.CanonicalMicroCircuitBlox})
-#   OrderedDict(
-#     :τ_ss => NCAD(0.002, NUMBER, 0.0001, 0.1,[]),
-#     :τ_sp => NCAD(0.002, NUMBER, 0.0001, 0.1,[]),
-#     :τ_ii => NCAD(0.016, NUMBER, 0.0001, 0.1,[]),
-#     :τ_dp => NCAD(0.028, NUMBER, 0.0001, 0.1,[]),
-#     :r_ss => NCAD(2.0/3.0, NUMBER, 0.1, 5.0,[]),
-#     :r_sp => NCAD(2.0/3.0, NUMBER, 0.1, 5.0,[]),
-#     :r_ii => NCAD(2.0/3.0, NUMBER, 0.1, 5.0,[]),
-#     :r_dp => NCAD(2.0/3.0, NUMBER, 0.1, 5.0,[])
-#   )
-# end
-
-# function inputs(::Type{Neuroblox.CanonicalMicroCircuitBlox})
-#   ["in_ss","in_sp","in_ii","in_sp"]
-# end
-
-# function outputs(::Type{Neuroblox.CanonicalMicroCircuitBlox})
-#   ["out_ss","out_sp","out_ii","out_sp"]
-# end
-
 function arguments(::Type{Neuroblox.Thalamus})
   OrderedDict(
     :N_exci => NCAD(25, INTEGER, 1, 100,[],true),
@@ -467,38 +314,6 @@ function arguments(::Type{Neuroblox.GreedyPolicy})
   )
 end
 
-#TODO: create connector compatible CMC
-# function arguments(::Type{Neuroblox.IFNeuronBlox}) #TODO: add correct settings for the arguments
-#   OrderedDict(
-#     :C => NCAD(30.0, NUMBER, 1.0, 50.0,[]),
-#     :E_syn => NCAD(1.0, NUMBER, 0.01, 100.0,[]),
-#     :G_syn => NCAD(5.0, NUMBER, 0.01, 20.0,[]),
-# 	  :I_in => NCAD(-10.0, NUMBER, -20.0, 0.0,[]),
-# 	  :freq => NCAD(35.0, NUMBER, 0.01, 10.0,[]),
-# 	  :phase => NCAD(0.105, NUMBER, 0.01, 2.0,[]),
-# 	  :τ => NCAD(0.105, NUMBER, 0.01, 2.0,[])
-#   )
-# end
-
-# function arguments(::Type{Neuroblox.QIFNeuronBlox}) #TODO: add correct settings for the arguments
-#   OrderedDict(
-#     :E_syn => NCAD(1.0, NUMBER, 0.01, 100.0,[]),
-#     :G_syn => NCAD(5.0, NUMBER, 0.01, 20.0,[]),
-# 	  :w => NCAD(-10.0, NUMBER, -20.0, 0.0,[]),
-# 	  :τ => NCAD(0.105, NUMBER, 0.01, 2.0,[])
-#   )
-# end
-
-# function arguments(::Type{Neuroblox.LIFNeuronBlox}) #TODO: add correct settings for the arguments
-#   OrderedDict(
-#     :I_in => NCAD(0.0, NUMBER, -5.0, 5.0,[]),
-#     :V_L => NCAD(-70.0, NUMBER, -90, -10,[]),
-#     :τ => NCAD(10.0, NUMBER, 0.01, 100.0,[]),
-#     :R => NCAD(100.0, NUMBER, 0.01, 200.0,[]),
-#     :θ => NCAD(-10.0, NUMBER, -20, 20.0,[]),
-#   )
-# end
-
 function arguments(::Type{Neuroblox.HHNeuronExciBlox}) #TODO: add correct settings for the arguments
   OrderedDict(
     :t_spike_window => NCAD(90.0, NUMBER, 0.1, 100.0,[],true),
diff --git a/src/measurementmodels/fmri.jl b/src/measurementmodels/fmri.jl
index 50dd2328..cd6c6fd2 100644
--- a/src/measurementmodels/fmri.jl
+++ b/src/measurementmodels/fmri.jl
@@ -8,20 +8,33 @@ boldsignal   : computes BOLD signal
 """
 
 """
-### Input variables ###
-name : name of ODE system
-jcn  : neural activity
-s    : vascular signal
-lnf  : logarithm of rCBF
-lnν  : logarithm of venous volume
-lnq  : logarithm of deoxyhemoglobin (dHb)
-
-### Parameter ###
-lnκ  : logarithmic prefactor to signal decay H[1], set to 0 for standard parameter value.
-lnτ  : logarithmic prefactor to transit time H[3], set to 0 for standard parameter value.
-
-### Return variables ###
-returns an ODESystem of the biophysical model for the hemodynamics
+    BalloonModel(;name, namespace=nothing, lnκ=0.0, lnτ=0.0)
+
+    Create a balloon model blox which computes the hemodynamic responses to some underlying neuronal activity
+    The formal definition of this blox is:
+ """
+    # ```math
+    #     \\frac{ds}{dt} = \\text{jcn} - \\kappa s - \\gamma (u-1)
+    #     \\frac{du}{dt} = s
+    #     \\frac{d\\nu}{dt} = u - v^{1/\\alpha}
+    #     \\frac{dq}{dt} = u E(u, E_0)/E_0 - v^{1/\\alpha} q/v
+    # ```
+"""
+    where ``jcn`` is any input to the blox (represents the neuronal activity)
+
+Arguments:
+- `name`: Name given to `ODESystem` object within the blox.
+- `namespace`: Additional namespace above `name` if needed for inheritance.
+- `lnκ`: logarithmic prefactor to signal decay H[1], set to 0 for standard parameter value.
+- `lnτ`: logarithmic prefactor to transit time H[3], set to 0 for standard parameter value.
+
+NB: the prefix ln of the variables u, ν, q as well as the parameters κ, τ denotes their transformation into logarithmic space
+to enforce their positivity. This transformation is considered in the derivates of the model equations below. 
+
+Citations:
+1. Stephan K E, Weiskopf N, Drysdale P M, Robinson P A, and Friston K J. Comparing Hemodynamic Models with DCM. NeuroImage 38, no. 3 (2007): 387–401. doi: 10.1016/j.neuroimage.2007.07.040
+2. Hofmann D, Chesebro A G, Rackauckas C, Mujica-Parodi L R, Friston K J, Edelman A, and Strey H H. Leveraging Julia's Automated Differentiation and Symbolic Computation to Increase Spectral DCM Flexibility and Speed, 2023. doi: 10.1101/2023.10.27.564407
+
 """
 struct BalloonModel <: ObserverBlox
     params
@@ -39,17 +52,17 @@ struct BalloonModel <: ObserverBlox
         =#
 
         H = [0.64, 0.32, 2.00, 0.32, 0.4]
-        p = progress_scope(lnκ, lnτ)  # progress scope if needed
-        p = compileparameterlist(lnκ=p[1], lnτ=p[2])  # finally compile all parameters
-        lnκ, lnτ = p  # assign the modified parameters
+        # p = progress_scope(lnκ, lnτ)                  # progress scope if needed
+        p = paramscoping(lnκ=lnκ, lnτ=lnτ)  # finally compile all parameters
+        lnκ, lnτ = p                                  # assign the modified parameters
         
-        sts = @variables s(t)=1.0 lnf(t)=1.0 lnν(t)=1.0 [output=true, description="hemodynamic_observer"] lnq(t)=1.0 [output=true, description="hemodynamic_observer"] jcn(t)=0.0 [input=true]
+        sts = @variables s(t)=1.0 lnu(t)=1.0 lnν(t)=1.0 [output=true, description="hemodynamic_observer"] lnq(t)=1.0 [output=true, description="hemodynamic_observer"] jcn(t)=0.0 [input=true]
 
         eqs = [
-            D(s)   ~ jcn - H[1]*exp(lnκ)*s - H[2]*(exp(lnf) - 1),
-            D(lnf) ~ s / exp(lnf),
-            D(lnν) ~ (exp(lnf) - exp(lnν)^(H[4]^-1)) / (H[3]*exp(lnτ)*exp(lnν)),
-            D(lnq) ~ (exp(lnf)/exp(lnq)*((1 - (1 - H[5])^(exp(lnf)^-1))/H[5]) - exp(lnν)^(H[4]^-1 - 1))/(H[3]*exp(lnτ))
+            D(s)   ~ jcn - H[1]*exp(lnκ)*s - H[2]*(exp(lnu) - 1),
+            D(lnu) ~ s / exp(lnu),
+            D(lnν) ~ (exp(lnu) - exp(lnν)^(H[4]^-1)) / (H[3]*exp(lnτ)*exp(lnν)),
+            D(lnq) ~ (exp(lnu)/exp(lnq)*((1 - (1 - H[5])^(exp(lnu)^-1))/H[5]) - exp(lnν)^(H[4]^-1 - 1))/(H[3]*exp(lnτ))
         ]
         sys = System(eqs, name=name)
         new(p, Num(0), sts[5], sys, namespace)
@@ -57,22 +70,28 @@ struct BalloonModel <: ObserverBlox
 end
 
 
-
 """
-BOLD signal model as described in: 
+    boldsignal(;name, lnϵ=0.0)
+
+    Bold signal observer function. This requires connection to the variables ν and q of a balloon model.
+    The formal definition of this blox is:
+ """
+    # ```math
+    #     \\lambda(\\nu, q) = V_0 \\left[ k_1 (1-q) + k_2 \\left( 1 - \\frac{q}{v} \\right) + k_3 (1-v)\\right]
+    # ```
+"""
 
-Stephan KE, Weiskopf N, Drysdale PM, Robinson PA, Friston KJ (2007)
-Comparing hemodynamic models with DCM. NeuroImage 38: 387-401.
+Arguments:
+- `name`: Name given to `ODESystem` object within the blox.
+- lnϵ  : logarithm of ratio of intra- to extra-vascular signal
 
-### Input variables ###
-lnν  : logarithm of venous volume
-lnq  : logarithm of deoxyhemoglobin (dHb)
+NB: the prefix ln of the variables ν, q as well as the parameters ϵ denotes their transformation into logarithmic space
+to enforce their positivity.
 
-### Parameter ###
-lnϵ  : logarithm of ratio of intra- to extra-vascular signal
+Citations:
+1. Stephan K E, Weiskopf N, Drysdale P M, Robinson P A, and Friston K J. Comparing Hemodynamic Models with DCM. NeuroImage 38, no. 3 (2007): 387–401. doi: 10.1016/j.neuroimage.2007.07.040
+2. Hofmann D, Chesebro A G, Rackauckas C, Mujica-Parodi L R, Friston K J, Edelman A, and Strey H H. Leveraging Julia's Automated Differentiation and Symbolic Computation to Increase Spectral DCM Flexibility and Speed, 2023. doi: 10.1101/2023.10.27.564407
 
-### Return variables ###
-returns ODESystem
 """
 function boldsignal(;name, lnϵ=0.0)
     # NB: Biophysical constants for 1.5T scanners
@@ -87,7 +106,7 @@ function boldsignal(;name, lnϵ=0.0)
     nu0 = 40.3
     # resting oxygen extraction fraction
     E0  = 0.4
-    # -Coefficients in BOLD signal model
+    # Coefficients in BOLD signal model
     k1  = 4.3*nu0*E0*TE
 
     params = @parameters lnϵ=lnϵ
diff --git a/src/utilities/bold_methods.jl b/src/utilities/bold_methods.jl
new file mode 100644
index 00000000..8dd2a62c
--- /dev/null
+++ b/src/utilities/bold_methods.jl
@@ -0,0 +1,45 @@
+# Simulate the BOLD signal.
+
+# This is the version of the Balloon-Windkessel model used in Endo et al. (2020), and subsequently in Antal et al. (2023) and van Niuewenhuizen et al. (2023).
+# As it is only a convolution after the simulation of the neural activity, it is not a block for inclusion in the ODESystem.
+
+"""
+Simulated BOLD response to input
+
+"""
+
+function boldsignal_endo_balloon(t, y, TR=800, dt_orig=1)
+
+    # Parameters are from Endo et al. (2020).
+    κ = 0.65;
+    α = 0.32;
+    τ = 0.98;
+    ρ = 0.34;
+    V_0 = 0.02;
+    γ = 0.41;
+
+    bold = zeros(size(y));
+    
+    for roi in 1:length(y[:, 1])
+        s = ones(1, length(y[1, :])).*0.1;
+        f = ones(size(s)).*0.1;
+        v = ones(size(s)).*0.1;
+        q = ones(size(s)).*0.1;
+
+        for i in 2:length(y[1, :])
+            z = y[roi, i];
+            dt = t[i]-t[i-1];
+            ds = z - (κ*s[i-1])-(γ*(f[i-1]-1));
+            df = s[i-1];
+            dv = (f[i-1]-(v[i-1]^(1/α)))/τ;
+            dq = (((f[i-1]*(1-(1-(ρ^(1/f[i-1])))))/ρ)-(((v[i-1]^(1/α))*(q[i-1]))/v[i-1]))/τ;
+            s[i] = s[i-1] + ds*dt;
+            f[i] = f[i-1] + df*dt;
+            v[i] = v[i-1] + dv*dt;
+            q[i] = q[i-1] + dq*dt;
+        end
+        bold[roi, :] = V_0 .* ((7 .* ρ .* (1 .- q ./ v)) .+ ((2 .* ρ .- 0.2) .* (1 .-v )));
+    end
+    
+    return (bold[:, 1:Int(round(TR/dt_orig)):end])
+end
\ No newline at end of file
diff --git a/src/utilities/spectral_tools.jl b/src/utilities/spectral_tools.jl
index 5ed4dc7b..12153775 100644
--- a/src/utilities/spectral_tools.jl
+++ b/src/utilities/spectral_tools.jl
@@ -182,7 +182,7 @@ function mar2csd(mar, freqs, sf)
     nf = length(w)
 	csd = zeros(ComplexF64, nf, nd, nd)
 	for i = 1:nf
-		af_tmp = la.I
+		af_tmp = I
 		for k = 1:p
 			af_tmp = af_tmp + A[k] * exp(-im * k * w[i])
 		end
@@ -203,7 +203,7 @@ function mar2csd(mar, freqs)
     nf = length(w)
 	csd = zeros(eltype(Σ), nf, nd, nd)
 	for i = 1:nf
-		af_tmp = la.I
+		af_tmp = I
 		for k = 1:p
 			af_tmp = af_tmp + A[k] * exp(-im * k * w[i])
 		end
@@ -276,7 +276,7 @@ function csd2mar(csd, w, dt, p)
     for i = 1:m
         for j = 1:m
             A[((i-1)*p+1):i*p, j] = ccf[(1:p) .+ 1, i, j]
-            B[((i-1)*p+1):i*p, ((j-1)*p+1):j*p] = tm.Toeplitz(ccf[1:p, i, j], vcat(ccf[1,i,j], ccf[2:p, j, i]))  # SymmetricToeplitz(ccf[1:p, i, j])
+            B[((i-1)*p+1):i*p, ((j-1)*p+1):j*p] = Toeplitz(ccf[1:p, i, j], vcat(ccf[1,i,j], ccf[2:p, j, i]))  # SymmetricToeplitz(ccf[1:p, i, j])
         end
     end
     a = B\A
diff --git a/test/components.jl b/test/components.jl
index 359559d4..97e6133a 100644
--- a/test/components.jl
+++ b/test/components.jl
@@ -136,84 +136,31 @@ end
     @test sol.retcode == ReturnCode.Success
 end
 
-
-
-@testset "Canonical micro circuit" begin
-    """
-    Canonical micro circuit tests 
-
-    first create a single canonical micro circuit and simulate. Then create a two region model and connect two
-    to form the circuit that is given in Figure 4 of Bastos et al. 2015.
-    """
-    # canonical micro circuit based on single Jansen-Rit blox
-    @named singleregion = CanonicalMicroCircuitBlox()
-    eqs = []
-    for bi in singleregion.bloxinput
-        push!(eqs, bi ~ 0)
-    end
-    @named singleregionODE = ODESystem(eqs, systems=[singleregion.odesystem])
-    singleregionODE = structural_simplify(singleregionODE)
-    sol = simulate(singleregionODE, [], (0.0, 10.0), [])
-    # TODO: it would be nicer if this was without the singleregion namespace...
-    @test sol[!,"singleregion₊dp₊x(t)"][end] + sol[!,"singleregion₊ii₊x(t)"][end] ≈ -5.159425345927338
-end
-
-@testset "Canonical & Janset-Rit network" begin
+@testset "Canonical Micro Circuit network" begin
     # connect multiple canonical micro circuits according to Figure 4 in Bastos et al. 2015
-    @named r1 = CanonicalMicroCircuitBlox()
-    @named r2 = CanonicalMicroCircuitBlox()
-    @named jr = JansenRitCBlox()
+    global_ns = :g # global namespace
+    @named r1 = CanonicalMicroCircuitBlox(;namespace=global_ns)
+    @named r2 = CanonicalMicroCircuitBlox(;namespace=global_ns)
 
     g = MetaDiGraph()
-    add_vertex!(g, Dict(:blox => r1)) # V1 (see fig. 4 in Bastos et al. 2015)
-    add_vertex!(g, Dict(:blox => r2)) # V4 (see fig. 4 in Bastos et al. 2015)
+    add_blox!.(Ref(g), [r1, r2])
+
     add_edge!(g, 1, 2, :weightmatrix, [0 1 0 0; # superficial pyramidal to spiny stellate
-                                    0 0 0 0;
-                                    0 0 0 0;
-                                    0 1 0 0]) # superficial pyramidal to deep pyramidal
+                                       0 0 0 0;
+                                       0 0 0 0;
+                                       0 1 0 0]) # superficial pyramidal to deep pyramidal
     # define connections from column (source) to row (sink)
     add_edge!(g, 2, 1, :weightmatrix, [0 0 0  0; 
-                                    0 0 0 -1;
-                                    0 0 0 -1;
-                                    0 0 0  0])
-
-    @named cmc_network = ODEfromGraph(g)
-    cmc_network = structural_simplify(cmc_network)
-
-    sol = simulate(cmc_network, [], (0.0, 10.0), [])
-    @test sum(sol[end, 2:end]) ≈ -4827.086868187682
-
-    # now add a Neural mass model with one output and one input
-    add_vertex!(g, Dict(:blox => jr))
-    add_edge!(g, 3, 1, :weightmatrix, [0; 0; 0; 1])
-    add_edge!(g, 1, 3, :weightmatrix, [[1 0 0 0];])
-    add_edge!(g, 3, 3, :weight, -1)
-
-    @named cmc_network2 = ODEfromGraph(g)
-    cmc_network2 = structural_simplify(cmc_network2)
-    sol = simulate(cmc_network2, [], (0.0, 10.0), [])
-    @test sum(sol[end, 2:end]) ≈ -4823.399802568824
+                                       0 0 0 -1;
+                                       0 0 0 -1;
+                                       0 0 0  0])
+    sys = system_from_graph(g; name=global_ns)
+    sys_simpl =structural_simplify(sys)
 
-    # now connect canonical micro circuits with symbolic weight matrices
-    g = MetaDiGraph()
-    add_vertex!(g, Dict(:blox => r1))
-    add_vertex!(g, Dict(:blox => r2))
-
-    A_forward = [0 1 0 0;
-                0 0 0 0;
-                0 0 0 0;
-                0 1 0 0]
-    A_backward = [0 0 0  0;
-                0 0 0 -1;
-                0 0 0 -1;
-                0 0 0  0]
-    @parameters wm_forward[1:length(A_forward)] = vec(A_forward) 
-    add_edge!(g, 1, 2, :weightmatrix, reshape(wm_forward, 4, 4))
-    @parameters wm_backward[1:length(A_backward)] = vec(A_backward) 
-    add_edge!(g, 2, 1, :weightmatrix, reshape(wm_backward, 4, 4))
-
-    @named cmc_network = ODEfromGraph(g)
-    cmc_network = structural_simplify(cmc_network)
+    prob = ODEProblem(sys_simpl, [], (0, 10))
+    sol = solve(prob, Vern7(), saveat=0.1)
+    sum(sol[end, 2:end])
+    @test sol.retcode == ReturnCode.Success
 end
 
 @testset "Next Generation Neural Mass" begin
@@ -237,38 +184,6 @@ end
     @test norm.(R[length(R)]) < 0.1
 end
 
-@testset "Theta network" begin
-    """
-    thetaneuron.jl test
-
-    Test approach: generate a network of theta neurons and connect them through an all-to-all
-    adjacency matrix via a spike function. Then compute the real part of the Kuramoto order parameter.
-    The average of this parameter should be close to zero as synchrony varies in the network from a positive
-    to a negative amplitude. 
-    """
-
-    # Generate Theta Network
-    network = [] 
-    N = 50
-    for i = 1:N
-        η  = rand(Cauchy(1.0, 0.05)) # Constant Drive
-        @named neuron = Neuroblox.theta_neuron(name=Symbol("neuron$i"), η=η, α_inv=1.0, k=-2.0)
-        push!(network, neuron)
-    end
-
-    # Create Circuit
-    adj_matrix = (1/N)*ones(N,N)
-    n = 3
-    a_n = 2.0^n*(factorial(n)^2.0)/(factorial(2*n))
-    @named theta_circuit = LinearConnections(sys=network, adj_matrix=adj_matrix, connector=[a_n*(1-cos(neuron.θ))^n for neuron in network])
-
-    sim_dur = 2.0
-    sol = Neuroblox.simulate(structural_simplify(theta_circuit), [], (0.0, sim_dur), [])
-    R = real(exp.(im*sol[!, "neuron1₊θ(t)"]))
-
-    @test abs(Statistics.mean(R)) < 0.7
-end
-
 @testset "QIF synaptic network" begin
     """
     qif_neuron.jl and synaptic_network.jl test
@@ -312,39 +227,6 @@ end
     @test sol.t[end] == sim_dur
 end
 
-# Are we keeping this one?
-@testset "LIF network" begin
-"""
-network of LIFs test
-"""
-# N = 6   # 6 neurons
-
-# # neuron properties
-# I_in = ones(N);             # same input current to all
-# τ = 5*collect(1:N);         # increasing membrane time constant
-# # synaptic properties 
-# syn_amp = 0.4*ones(N, N); # synaptic amplitudes
-# syn_τ = 5*ones(N)
-# nrn_network=[]
-# nrn_spiketimes=[]
-
-# for i = 1:N
-#     nn = LIFNeuronBlox(name=Symbol("lif$i"), I_in=I_in[i], τ=τ[i])
-#     push!(nrn_network, nn.odesystem)
-# 	push!(nrn_spiketimes, nn.odesystem.st)
-# end
-
-# # connect the neurons
-# @named syn_net = spikeconnections(sys=nrn_network, psp_amplitude=syn_amp, τ=syn_τ, spiketimes=nrn_spiketimes)
-
-# sim_dur =  50.0
-# prob = ODEProblem(structural_simplify(syn_net), [], (0.0, sim_dur), [])
-# sol = solve(prob, AutoVern7(Rodas4())) #pass keyword arguments to solver
-
-# @test length(sol.prob.p[end]) == 5
-# @test length(sol.prob.p[21]) == 11
-end
-
 @testset "Van der Pol" begin
 @named VdP = van_der_pol()
 
@@ -371,18 +253,18 @@ sol = solve(prob_ou,alg_hints = [:stiff])
 @test std(sol[1,:]) > 0.0 # there should be variance
 end
 
-@testset "OUBlox & Janset-Rit network" begin
-@named ou1 = OUBlox()
-@named jr = JansenRitCBlox()
-sys = [ou1.odesystem, jr.odesystem]
-eqs = [sys[1].jcn ~ 0.0, sys[2].jcn ~ sys[1].x]
-@named ou1connected = compose(System(eqs;name=:connected),sys)
-ousimpl = structural_simplify(ou1connected)
-prob_oujr = SDEProblem(ousimpl,[],(0.0, 2.0))
-sol = solve(prob_oujr, alg_hints = [:stiff])
-@test sol.retcode == SciMLBase.ReturnCode.Success
-@test std(sol[2,:]) > 0.0 # there should be variance
-end
+# @testset "OUBlox & Janset-Rit network" begin
+# @named ou1 = OUBlox()
+# @named jr = JansenRitCBlox()
+# sys = [ou1.odesystem, jr.odesystem]
+# eqs = [sys[1].jcn ~ 0.0, sys[2].jcn ~ sys[1].x]
+# @named ou1connected = compose(System(eqs;name=:connected),sys)
+# ousimpl = structural_simplify(ou1connected)
+# prob_oujr = SDEProblem(ousimpl,[],(0.0, 2.0))
+# sol = solve(prob_oujr, alg_hints = [:stiff])
+# @test sol.retcode == SciMLBase.ReturnCode.Success
+# @test std(sol[2,:]) > 0.0 # there should be variance
+# end
 
 @testset "OUBlox-OUCouplingBlox network" begin
     @named ou1 = OUBlox()
@@ -416,39 +298,39 @@ sol = solve(prob_ouconnect)
 @test cor(sol[1,:],sol[2,:]) < 0.2 # Pearson correlation should be negative or small
 end
 
-@testset "Time-series output" begin
-    phase_int = phase_inter(0:3,[0.0,1.0,2.0,1.0])
-    phase_cos_out(ω,t) = phase_cos_blox(ω,t,phase_int)
-    phase_sin_out(ω,t) = phase_sin_blox(ω,t,phase_int)
-    @test phase_cos_out(0.1,2.5)≈0.9689124217106447
-    @test phase_sin_out(0.1,2.5)≈0.24740395925452294
-
-    # now test how to connect this time series to a neural mass blox
-    @named Str2 = jansen_ritC(τ=0.0022, H=20, λ=300, r=0.3)
-    @parameters phase_input = 0
-
-    sys = [Str2.odesystem]
-    eqs = [sys[1].jcn ~ phase_input]
-    @named phase_system = ODESystem(eqs,systems=sys)
-    phase_system_simpl = structural_simplify(phase_system)
-    phase_ode = ODEProblem(phase_system_simpl,[],(0,3.0),[])
-
-    # create callback functions
-    # we always want to update phase_input to be our phase_cos_out(t)
-    condition = function (u,t,integrator)
-        true
-    end
+# @testset "Time-series output" begin
+#     phase_int = phase_inter(0:3,[0.0,1.0,2.0,1.0])
+#     phase_cos_out(ω,t) = phase_cos_blox(ω,t,phase_int)
+#     phase_sin_out(ω,t) = phase_sin_blox(ω,t,phase_int)
+#     @test phase_cos_out(0.1,2.5)≈0.9689124217106447
+#     @test phase_sin_out(0.1,2.5)≈0.24740395925452294
 
-    function affect!(integrator)
-        integrator.p[1] = phase_cos_out(10*pi,integrator.t)
-    end
+#     # now test how to connect this time series to a neural mass blox
+#     @named Str2 = jansen_ritC(τ=0.0022, H=20, λ=300, r=0.3)
+#     @parameters phase_input = 0
 
-    cb = DiscreteCallback(condition,affect!)
+#     sys = [Str2.odesystem]
+#     eqs = [sys[1].jcn ~ phase_input]
+#     @named phase_system = ODESystem(eqs,systems=sys)
+#     phase_system_simpl = structural_simplify(phase_system)
+#     phase_ode = ODEProblem(phase_system_simpl,[],(0,3.0),[])
 
-    sol = solve(phase_ode,Tsit5(),callback=cb)
-    @test sol.retcode == SciMLBase.ReturnCode.Success
-    @test sol[2,:][5] ≈ 13.49728948607267
-end
+#     # create callback functions
+#     # we always want to update phase_input to be our phase_cos_out(t)
+#     condition = function (u,t,integrator)
+#         true
+#     end
+
+#     function affect!(integrator)
+#         integrator.p[1] = phase_cos_out(10*pi,integrator.t)
+#     end
+
+#     cb = DiscreteCallback(condition,affect!)
+
+#     sol = solve(phase_ode,Tsit5(),callback=cb)
+#     @test sol.retcode == SciMLBase.ReturnCode.Success
+#     @test sol[2,:][5] ≈ 13.49728948607267
+# end
 
 @testset "HH Neuron excitatory & inhibitory network" begin
 nn1 = HHNeuronExciBlox(name=Symbol("nrn1"), I_bg=3, freq=4; t_spike_window=0.1)
diff --git a/test/jansen_rit_component_tests.jl b/test/jansen_rit_component_tests.jl
index 8f77e5c0..a809737a 100644
--- a/test/jansen_rit_component_tests.jl
+++ b/test/jansen_rit_component_tests.jl
@@ -1,23 +1,19 @@
-"""
-DEPRECATED
-
-This file uses the old blocks before timing update to be consistent. Use jansen_rit_component_tests_new_timing.jl instead.
-"""
-
 using Neuroblox, DifferentialEquations, DataFrames, Test, Distributions, Statistics, LinearAlgebra, Graphs, MetaGraphs, Random
 
+τ_factor = 1000 #needed because the old units were in seconds, and we need ms to be consistent
+
 # Create Regions
-@named Str = jansen_ritC(τ=0.0022, H=20, λ=300, r=0.3)
-@named GPe = jansen_ritC(τ=0.04, H=20, λ=400, r=0.1)
-@named STN = jansen_ritC(τ=0.01, H=20, λ=500, r=0.1)
-@named GPi = jansen_ritSC(τ=0.014, H=20, λ=400, r=0.1)
-@named Th  = jansen_ritSC(τ=0.002, H=10, λ=20, r=5)
-@named EI  = jansen_ritSC(τ=0.01, H=20, λ=5, r=5)
-@named PY  = jansen_ritSC(τ=0.001, H=20, λ=5, r=0.15)
-@named II  = jansen_ritSC(τ=2.0, H=60, λ=5, r=5)
+@named Str = jansen_ritC(τ=0.0022*τ_factor, H=20, λ=300, r=0.3)
+@named gpe = jansen_ritC(τ=0.04*τ_factor, H=20, λ=400, r=0.1)
+@named stn = jansen_ritC(τ=0.01*τ_factor, H=20, λ=500, r=0.1)
+@named gpi = jansen_ritSC(τ=0.014*τ_factor, H=20, λ=400, r=0.1)
+@named Th  = jansen_ritSC(τ=0.002*τ_factor, H=10, λ=20, r=5)
+@named EI  = jansen_ritSC(τ=0.01*τ_factor, H=20, λ=5, r=5)
+@named PY  = jansen_ritSC(τ=0.001*τ_factor, H=20, λ=5, r=0.15)
+@named II  = jansen_ritSC(τ=2.0*τ_factor, H=60, λ=5, r=5)
 
 # Connect Regions through Adjacency Matrix
-blox = [Str, GPe, STN, GPi, Th, EI, PY, II]
+blox = [Str, gpe, stn, gpi, Th, EI, PY, II]
 sys = [s.odesystem for s in blox]
 connect = [s.connector for s in blox]
 
@@ -34,10 +30,10 @@ adj_matrix_lin = [0 0 0 0 0 0 0 0;
 
 @named CBGTC_Circuit_lin = LinearConnections(sys=sys, adj_matrix=adj_matrix_lin, connector=connect)
 
-sim_dur = 10.0 # Simulate for 10 Seconds
+sim_dur = 2000.0 # Simulate for 2 seconds
 mysys = structural_simplify(CBGTC_Circuit_lin)
-sol = simulate(mysys, [], (0.0, sim_dur), [], Vern7(); saveat=0.001)
-@test sol[!, "GPi₊x(t)"][4] ≈ -0.48421810231972134
+sol = simulate(mysys, [], (0.0, sim_dur), [], Vern7(); saveat=1)
+@test sol[!, "gpi₊x(t)"][4] ≈ -2219.2560209502685 #updated to new value in ms
 
 """
 Testing new Jansen-Rit blox
@@ -50,15 +46,15 @@ same thing as the old simulate call with AutoVern7(Rodas4() since there are no d
 """
 
 # test new Jansen-Rit blox
-@named Str = JansenRit(τ=0.0022, H=20, λ=300, r=0.3)
-@named GPe = JansenRit(τ=0.04, cortical=false) # all default subcortical except τ
-@named STN = JansenRit(τ=0.01, H=20, λ=500, r=0.1)
-@named GPi = JansenRit(cortical=false) # default parameters subcortical Jansen Rit blox
-@named Th  = JansenRit(τ=0.002, H=10, λ=20, r=5)
-@named EI  = JansenRit(τ=0.01, H=20, λ=5, r=5)
+@named Str = JansenRit(τ=0.0022*τ_factor, H=20, λ=300, r=0.3)
+@named gpe = JansenRit(τ=0.04*τ_factor, cortical=false) # all default subcortical except τ
+@named stn = JansenRit(τ=0.01*τ_factor, H=20, λ=500, r=0.1)
+@named gpi = JansenRit(cortical=false) # default parameters subcortical Jansen Rit blox
+@named Th  = JansenRit(τ=0.002*τ_factor, H=10, λ=20, r=5)
+@named EI  = JansenRit(τ=0.01*τ_factor, H=20, λ=5, r=5)
 @named PY  = JansenRit(cortical=true) # default parameters cortical Jansen Rit blox
-@named II  = JansenRit(τ=2.0, H=60, λ=5, r=5)
-blox = [Str, GPe, STN, GPi, Th, EI, PY, II]
+@named II  = JansenRit(τ=2.0*τ_factor, H=60, λ=5, r=5)
+blox = [Str, gpe, stn, gpi, Th, EI, PY, II]
 
 # test graphs
 g = MetaDiGraph()
@@ -99,16 +95,16 @@ add_edge!(g, 8, 8, Dict(:weight => 3.3*C_Cor))
 
 @named final_system = system_from_graph(g, params)
 final_delays = graph_delays(g)
-sim_dur = 10.0 # Simulate for 10 Seconds
+sim_dur = 2000.0 # Simulate for 2 Seconds
 final_system_sys = structural_simplify(final_system)
 prob = DDEProblem(final_system_sys,
     [],
     (0.0, sim_dur),
     constant_lags = final_delays)
 alg = MethodOfSteps(Vern7())
-sol_dde_no_delays = solve(prob, alg, saveat=0.001)
+sol_dde_no_delays = solve(prob, alg, saveat=1)
 sol2 = DataFrame(sol_dde_no_delays)
-@test isapprox(sol2[!, "GPi₊x(t)"][500:1000], sol[!, "GPi₊x(t)"][500:1000], rtol=1e-8)
+@test isapprox(sol2[!, "gpi₊x(t)"][500:1000], sol[!, "gpi₊x(t)"][500:1000], rtol=1e-8)
 
 
 # Alternative version using adjacency matrix
@@ -118,13 +114,13 @@ create_adjacency_edges!(g2, adj_matrix_lin)
 
 @named final_system = system_from_graph(g2, params)
 final_delays = graph_delays(g2)
-sim_dur = 600.0 # Simulate for 10 Seconds
+sim_dur = 2000.0 # Simulate for 2 Seconds
 final_system_sys = structural_simplify(final_system)
 prob = DDEProblem(final_system_sys,
     [],
     (0.0, sim_dur),
     constant_lags = final_delays)
 alg = MethodOfSteps(Vern7())
-sol_dde_no_delays = solve(prob, alg, saveat=0.001)
+sol_dde_no_delays = solve(prob, alg, saveat=1)
 sol3 = DataFrame(sol_dde_no_delays)
-@test isapprox(sol3[!, "GPi₊x(t)"][500:1000], sol[!, "GPi₊x(t)"][500:1000], rtol=1e-8)
\ No newline at end of file
+@test isapprox(sol3[!, "gpi₊x(t)"][500:1000], sol[!, "gpi₊x(t)"][500:1000], rtol=1e-8)
\ No newline at end of file
diff --git a/test/jansen_rit_component_tests_new_timing.jl b/test/jansen_rit_component_tests_new_timing.jl
deleted file mode 100644
index a809737a..00000000
--- a/test/jansen_rit_component_tests_new_timing.jl
+++ /dev/null
@@ -1,126 +0,0 @@
-using Neuroblox, DifferentialEquations, DataFrames, Test, Distributions, Statistics, LinearAlgebra, Graphs, MetaGraphs, Random
-
-τ_factor = 1000 #needed because the old units were in seconds, and we need ms to be consistent
-
-# Create Regions
-@named Str = jansen_ritC(τ=0.0022*τ_factor, H=20, λ=300, r=0.3)
-@named gpe = jansen_ritC(τ=0.04*τ_factor, H=20, λ=400, r=0.1)
-@named stn = jansen_ritC(τ=0.01*τ_factor, H=20, λ=500, r=0.1)
-@named gpi = jansen_ritSC(τ=0.014*τ_factor, H=20, λ=400, r=0.1)
-@named Th  = jansen_ritSC(τ=0.002*τ_factor, H=10, λ=20, r=5)
-@named EI  = jansen_ritSC(τ=0.01*τ_factor, H=20, λ=5, r=5)
-@named PY  = jansen_ritSC(τ=0.001*τ_factor, H=20, λ=5, r=0.15)
-@named II  = jansen_ritSC(τ=2.0*τ_factor, H=60, λ=5, r=5)
-
-# Connect Regions through Adjacency Matrix
-blox = [Str, gpe, stn, gpi, Th, EI, PY, II]
-sys = [s.odesystem for s in blox]
-connect = [s.connector for s in blox]
-
-@parameters C_Cor=60 C_BG_Th=60 C_Cor_BG_Th=5 C_BG_Th_Cor=5
-
-adj_matrix_lin = [0 0 0 0 0 0 0 0;
-            -0.5*C_BG_Th -0.5*C_BG_Th C_BG_Th 0 0 0 0 0;
-            0 -0.5*C_BG_Th 0 0 0 0 C_Cor_BG_Th 0;
-            0 -0.5*C_BG_Th C_BG_Th 0 0 0 0 0;
-            0 0 0 -0.5*C_BG_Th 0 0 0 0;
-            0 0 0 0 C_BG_Th_Cor 0 6*C_Cor 0;
-            0 0 0 0 0 4.8*C_Cor 0 -1.5*C_Cor;
-            0 0 0 0 0 0 1.5*C_Cor 3.3*C_Cor]
-
-@named CBGTC_Circuit_lin = LinearConnections(sys=sys, adj_matrix=adj_matrix_lin, connector=connect)
-
-sim_dur = 2000.0 # Simulate for 2 seconds
-mysys = structural_simplify(CBGTC_Circuit_lin)
-sol = simulate(mysys, [], (0.0, sim_dur), [], Vern7(); saveat=1)
-@test sol[!, "gpi₊x(t)"][4] ≈ -2219.2560209502685 #updated to new value in ms
-
-"""
-Testing new Jansen-Rit blox
-
-This sets up the exact same system as above, but using the JansenRit with different flags cortical/subcortical flags.
-
-The purpose of this test is to make sure that setting up everything using System rather than ODESystem works as expected.
-It also shows test code for the new system_from_graph calls and handling delays when everything is 0 (MethodOfSteps returns the 
-same thing as the old simulate call with AutoVern7(Rodas4() since there are no delays.)
-"""
-
-# test new Jansen-Rit blox
-@named Str = JansenRit(τ=0.0022*τ_factor, H=20, λ=300, r=0.3)
-@named gpe = JansenRit(τ=0.04*τ_factor, cortical=false) # all default subcortical except τ
-@named stn = JansenRit(τ=0.01*τ_factor, H=20, λ=500, r=0.1)
-@named gpi = JansenRit(cortical=false) # default parameters subcortical Jansen Rit blox
-@named Th  = JansenRit(τ=0.002*τ_factor, H=10, λ=20, r=5)
-@named EI  = JansenRit(τ=0.01*τ_factor, H=20, λ=5, r=5)
-@named PY  = JansenRit(cortical=true) # default parameters cortical Jansen Rit blox
-@named II  = JansenRit(τ=2.0*τ_factor, H=60, λ=5, r=5)
-blox = [Str, gpe, stn, gpi, Th, EI, PY, II]
-
-# test graphs
-g = MetaDiGraph()
-add_blox!.(Ref(g), blox)
-
-# Store parameters to be passed later on
-params = @parameters C_Cor=60 C_BG_Th=60 C_Cor_BG_Th=5 C_BG_Th_Cor=5
-
-add_edge!(g, 2, 1, Dict(:weight => -0.5*C_BG_Th))
-add_edge!(g, 2, 2, Dict(:weight => -0.5*C_BG_Th))
-add_edge!(g, 2, 3, Dict(:weight => C_BG_Th))
-add_edge!(g, 3, 2, Dict(:weight => -0.5*C_BG_Th))
-add_edge!(g, 3, 7, Dict(:weight => C_Cor_BG_Th))
-add_edge!(g, 4, 2, Dict(:weight => -0.5*C_BG_Th))
-add_edge!(g, 4, 3, Dict(:weight => C_BG_Th))
-add_edge!(g, 5, 4, Dict(:weight => -0.5*C_BG_Th))
-add_edge!(g, 6, 5, Dict(:weight => C_BG_Th_Cor))
-add_edge!(g, 6, 7, Dict(:weight => 6*C_Cor))
-add_edge!(g, 7, 6, Dict(:weight => 4.8*C_Cor))
-add_edge!(g, 7, 8, Dict(:weight => -1.5*C_Cor))
-add_edge!(g, 8, 7, Dict(:weight => 1.5*C_Cor))
-add_edge!(g, 8, 8, Dict(:weight => 3.3*C_Cor))
-
-# add_edge!(g, 2, 1, Dict(:weight => -0.5*60, :delay => 0.01))
-# add_edge!(g, 2, 2, Dict(:weight => -0.5*60, :delay => 0.01))
-# add_edge!(g, 2, 3, Dict(:weight => 60, :delay => 0.01))
-# add_edge!(g, 3, 2, Dict(:weight => -0.5*60, :delay => 0.01))
-# add_edge!(g, 3, 7, Dict(:weight => 5, :delay => 0.01))
-# add_edge!(g, 4, 2, Dict(:weight => -0.5*60, :delay => 0.01))
-# add_edge!(g, 4, 3, Dict(:weight => 60, :delay => 0.01))
-# add_edge!(g, 5, 4, Dict(:weight => -0.5*60, :delay => 0.01))
-# add_edge!(g, 6, 5, Dict(:weight => 5, :delay => 0.01))
-# add_edge!(g, 6, 7, Dict(:weight => 6*60, :delay => 0.01))
-# add_edge!(g, 7, 6, Dict(:weight => 4.8*60, :delay => 0.01))
-# add_edge!(g, 7, 8, Dict(:weight => -1.5*60, :delay => 0.01))
-# add_edge!(g, 8, 7, Dict(:weight => 1.5*60, :delay => 0.01))
-# add_edge!(g, 8, 8, Dict(:weight => 3.3*60, :delay => 0.01))
-
-@named final_system = system_from_graph(g, params)
-final_delays = graph_delays(g)
-sim_dur = 2000.0 # Simulate for 2 Seconds
-final_system_sys = structural_simplify(final_system)
-prob = DDEProblem(final_system_sys,
-    [],
-    (0.0, sim_dur),
-    constant_lags = final_delays)
-alg = MethodOfSteps(Vern7())
-sol_dde_no_delays = solve(prob, alg, saveat=1)
-sol2 = DataFrame(sol_dde_no_delays)
-@test isapprox(sol2[!, "gpi₊x(t)"][500:1000], sol[!, "gpi₊x(t)"][500:1000], rtol=1e-8)
-
-
-# Alternative version using adjacency matrix
-g2 = MetaDiGraph()
-add_blox!.(Ref(g2), blox)
-create_adjacency_edges!(g2, adj_matrix_lin)
-
-@named final_system = system_from_graph(g2, params)
-final_delays = graph_delays(g2)
-sim_dur = 2000.0 # Simulate for 2 Seconds
-final_system_sys = structural_simplify(final_system)
-prob = DDEProblem(final_system_sys,
-    [],
-    (0.0, sim_dur),
-    constant_lags = final_delays)
-alg = MethodOfSteps(Vern7())
-sol_dde_no_delays = solve(prob, alg, saveat=1)
-sol3 = DataFrame(sol_dde_no_delays)
-@test isapprox(sol3[!, "gpi₊x(t)"][500:1000], sol[!, "gpi₊x(t)"][500:1000], rtol=1e-8)
\ No newline at end of file
diff --git a/test/jansen_rit_hemodynamic_tests.jl b/test/jansen_rit_hemodynamic_tests.jl
deleted file mode 100644
index 42c796e3..00000000
--- a/test/jansen_rit_hemodynamic_tests.jl
+++ /dev/null
@@ -1,43 +0,0 @@
-using Neuroblox, DifferentialEquations, DataFrames, Test, Distributions, Statistics, LinearAlgebra, Graphs, MetaGraphs, Random
-
-# Store parameters to be passed later on
-params = @parameters C_Cor=60 C_BG_Th=60 C_Cor_BG_Th=5 C_BG_Th_Cor=5
-
-adj_matrix_lin = [0 0 0 0 0 0 0 0 1;
-            -0.5*C_BG_Th -0.5*C_BG_Th C_BG_Th 0 0 0 0 0 0;
-            0 -0.5*C_BG_Th 0 0 0 0 C_Cor_BG_Th 0 0;
-            0 -0.5*C_BG_Th C_BG_Th 0 0 0 0 0 0;
-            0 0 0 -0.5*C_BG_Th 0 0 0 0 0;
-            0 0 0 0 C_BG_Th_Cor 0 6*C_Cor 0 0;
-            0 0 0 0 0 4.8*C_Cor 0 -1.5*C_Cor 0;
-            0 0 0 0 0 0 1.5*C_Cor 3.3*C_Cor 0;
-            0 0 0 0 0 0 0 0 0]
-
-# test new Jansen-Rit blox
-@named Str = JansenRit(τ=0.0022, H=20, λ=300, r=0.3)
-@named GPe = JansenRit(τ=0.04, cortical=false) # all default subcortical except τ
-@named STN = JansenRit(τ=0.01, H=20, λ=500, r=0.1)
-@named GPi = JansenRit(cortical=false) # default parameters subcortical Jansen Rit blox
-@named Th  = JansenRit(τ=0.002, H=10, λ=20, r=5)
-@named EI  = JansenRit(τ=0.01, H=20, λ=5, r=5)
-@named PY  = JansenRit(cortical=true) # default parameters cortical Jansen Rit blox
-@named II  = JansenRit(τ=2.0, H=60, λ=5, r=5)
-@named hemo = AlternativeBalloonModel()
-blox = [Str, GPe, STN, GPi, Th, EI, PY, II, hemo]
-
-# Alternative version using adjacency matrix
-g = MetaDiGraph()
-add_blox!.(Ref(g), blox)
-create_adjacency_edges!(g, adj_matrix_lin)
-
-@named final_system = system_from_graph(g, params)
-final_delays = graph_delays(g)
-sim_dur = 600.0 # Simulate for 10 Seconds
-final_system_sys = structural_simplify(final_system)
-prob = DDEProblem(final_system_sys,
-    [],
-    (0.0, sim_dur),
-    constant_lags = final_delays)
-alg = MethodOfSteps(Vern7())
-sol_dde_no_delays = solve(prob, alg, saveat=0.001)
-sol3 = DataFrame(sol_dde_no_delays)
\ No newline at end of file
diff --git a/test/old_component_tests/jansen_rit_tests.jl b/test/old_component_tests/jansen_rit_tests.jl
deleted file mode 100644
index 289820aa..00000000
--- a/test/old_component_tests/jansen_rit_tests.jl
+++ /dev/null
@@ -1,79 +0,0 @@
-using Neuroblox, DifferentialEquations, DataFrames, Test, Distributions, Statistics, LinearAlgebra, Graphs, MetaGraphs, Random
-
-"""
-Tests formerly in components.jl
-"""
-# Create Regions
-@named Str = jansen_ritC(τ=0.0022, H=20, λ=300, r=0.3)
-@named GPe = jansen_ritC(τ=0.04, H=20, λ=400, r=0.1)
-@named STN = jansen_ritC(τ=0.01, H=20, λ=500, r=0.1)
-@named GPi = jansen_ritSC(τ=0.014, H=20, λ=400, r=0.1)
-@named Th  = jansen_ritSC(τ=0.002, H=10, λ=20, r=5)
-@named EI  = jansen_ritSC(τ=0.01, H=20, λ=5, r=5)
-@named PY  = jansen_ritSC(τ=0.001, H=20, λ=5, r=0.15)
-@named II  = jansen_ritSC(τ=2.0, H=60, λ=5, r=5)
-
-# Connect Regions through Adjacency Matrix
-blox = [Str, GPe, STN, GPi, Th, EI, PY, II]
-sys = [s.odesystem for s in blox]
-connect = [s.connector for s in blox]
-
-@parameters C_Cor=60 C_BG_Th=60 C_Cor_BG_Th=5 C_BG_Th_Cor=5
-
-adj_matrix_lin = [0 0 0 0 0 0 0 0;
-            -0.5*C_BG_Th -0.5*C_BG_Th C_BG_Th 0 0 0 0 0;
-            0 -0.5*C_BG_Th 0 0 0 0 C_Cor_BG_Th 0;
-            0 -0.5*C_BG_Th C_BG_Th 0 0 0 0 0;
-            0 0 0 -0.5*C_BG_Th 0 0 0 0;
-            0 0 0 0 C_BG_Th_Cor 0 6*C_Cor 0;
-            0 0 0 0 0 4.8*C_Cor 0 -1.5*C_Cor;
-            0 0 0 0 0 0 1.5*C_Cor 3.3*C_Cor]
-
-@named CBGTC_Circuit_lin = LinearConnections(sys=sys, adj_matrix=adj_matrix_lin, connector=connect)
-
-sim_dur = 10.0 # Simulate for 10 Seconds
-mysys = structural_simplify(CBGTC_Circuit_lin)
-sol = simulate(mysys, [], (0.0, sim_dur), [], Vern7(); saveat=0.001)
-@test sol[!, "GPi₊x(t)"][4] ≈ -0.48421810231972134
-
-sol = simulate(mysys, random_initials(mysys,blox),(0.0, sim_dur), [])
-@test size(sol)[2] == 17 # make sure that all the states are simulated (16 + timestamp)
-
-"""
-Components Test for Cortical-Subcortical Jansen-Rit blox
-    Cortical: PFC (Just Pyramidal Cells (PY), no Exc. Interneurons or Inh. Interneurons)
-    Subcortical: Basal Ganglia (GPe, STN, GPi) + Thalamus
-"""
-
-# Create Regions
-@named GPe       = JansenRitCBlox(τ=0.04, H=20, λ=400, r=0.1)
-@named STN       = JansenRitCBlox(τ=0.01, H=20, λ=500, r=0.1)
-@named GPi       = JansenRitCBlox(τ=0.014, H=20, λ=400, r=0.1)
-@named Thalamus  = JansenRitSCBlox(τ=0.002, H=10, λ=20, r=5)
-@named PFC       = JansenRitSCBlox(τ=0.001, H=20, λ=5, r=0.15)
-
-# Connect Regions through Adjacency Matrix
-blox = [GPe, STN, GPi, Thalamus, PFC]
-sys = [s.odesystem for s in blox]
-connect = [s.connector for s in blox]
-
-@parameters C_Cor=60 C_BG_Th=60 C_Cor_BG_Th=5 C_BG_Th_Cor=5
-
-adj_matrix_lin = [0 C_BG_Th 0 0 0;
-            -0.5*C_BG_Th 0 0 0 C_Cor_BG_Th;
-            -0.5*C_BG_Th C_BG_Th 0 0 0;
-            0 0 -0.5*C_BG_Th 0 0;
-            0 0 0 C_BG_Th_Cor 0]
-
-@named CBGTC_Circuit_lin = LinearConnections(sys=sys, adj_matrix=adj_matrix_lin, connector=connect)
-sim_dur = 10.0 # Simulate for 10 Seconds
-mysys = structural_simplify(CBGTC_Circuit_lin)
-sol = simulate(mysys, [], (0.0, sim_dur), [])
-
-"""
-New tests for JansenRit blox
-
-These are to make sure the new JansenRit blox works identically to the former one.
-"""
-
-# see jansen_rit_component_tests.jl
\ No newline at end of file
diff --git a/test/old_component_tests/larter_breakspear_tests.jl b/test/old_component_tests/larter_breakspear_tests.jl
deleted file mode 100644
index ea622124..00000000
--- a/test/old_component_tests/larter_breakspear_tests.jl
+++ /dev/null
@@ -1,21 +0,0 @@
-using Neuroblox, DifferentialEquations, DataFrames, Test, Distributions, Statistics, LinearAlgebra, Graphs, MetaGraphs, Random
-
-"""
-Tests formerly in components.jl
-"""
-
-"""
-Larter-Breakspear model test
-"""
-@named lb = LarterBreakspearBlox()
-sys = [lb.odesystem]
-eqs = [sys[1].jcn ~ 0]
-@named lb_connect = ODESystem(eqs,systems=sys)
-lb_simpl = structural_simplify(lb_connect)
-
-@test length(states(lb_simpl)) == 3
-
-prob = ODEProblem(lb_simpl,[0.5,0.5,0.5],(0,10.0),[])
-sol = solve(prob,Tsit5())
-
-@test sol[1,10] ≈ -0.6246710908910991
\ No newline at end of file
diff --git a/test/old_component_tests/wilson_cowan_tests.jl b/test/old_component_tests/wilson_cowan_tests.jl
deleted file mode 100644
index 2f6ae827..00000000
--- a/test/old_component_tests/wilson_cowan_tests.jl
+++ /dev/null
@@ -1,21 +0,0 @@
-using Neuroblox, DifferentialEquations, DataFrames, Test, Distributions, Statistics, LinearAlgebra, Graphs, MetaGraphs, Random
-
-
-"""
-Tests formerly in components.jl
-"""
-
-"""
-wilson_cowan test
-
-Test for Wilson-Cowan model
-"""
-sim_dur = 10.0 #unsure where the sim_dur came from originally because it wasn't part of the test, so this is arbitrary
-@named WC = WilsonCowanBlox()
-sys = [WC.odesystem]
-eqs = [sys[1].jcn ~ 0.0, sys[1].P ~ 0.0]
-@named WC_sys = ODESystem(eqs,systems=sys)
-WC_sys_s = structural_simplify(WC_sys)
-prob = ODEProblem(WC_sys_s, [], (0,sim_dur), [])
-sol = solve(prob,AutoVern7(Rodas4()),saveat=0.01)
-#@test sol[1,end] ≈ 0.17513685727060388
\ No newline at end of file
diff --git a/test/runtests.jl b/test/runtests.jl
index 0bebe41d..7bab03ca 100644
--- a/test/runtests.jl
+++ b/test/runtests.jl
@@ -9,7 +9,7 @@ using SafeTestsets
 # @time @safetestset "Neural Signal Measurement Models Tests" begin include("measurementmodels.jl") end
 # @time @safetestset "Spectral Utilities Tests" begin include("spectraltools.jl") end
 @time @safetestset "Data Fitting Tests" begin include("datafitting.jl") end 
-@time @safetestset "ODE from Graph and simulate" begin include("graph_to_dataframe.jl") end
+#@time @safetestset "ODE from Graph and simulate" begin include("graph_to_dataframe.jl") end #This no longer works because it's built on the old blocks
 @time @safetestset "Learning Tests" begin include("learning.jl") end
 @time @safetestset "Control Tests" begin include("controllers.jl") end
 @time @safetestset "Source Tests" begin include("source_components.jl") end
@@ -20,9 +20,6 @@ using SafeTestsets
 # @time @safetestset "Fitting Tests" begin include("fitting.jl") end
 # @time @safetestset "Bayesian Fitting Tests" begin include("bayesian_fitting.jl") end
 
-# Extra tests for comparison across blox versions
-#@time @safetestset "New Jansen-Rit Tests" begin include("jansen_rit_component_tests_new_timing.jl") end
-#@time @safetestset "Old Jansen-Rit Tests" begin include("old_component_tests/jansen_rit_tests.jl") end
-#@time @safetestset "Old Wilson-Cowan Tests" begin include("old_component_tests/wilson_cowan_tests.jl") end 
-#@time @safetestset "New Larter-Breakspear Tests" begin include("new_LB_blox.jl") end
-#@time @safetestset "Old Larter-Breakspear Tests" begin include("old_component_tests/larter_breakspear_tests.jl") end
+# Extra tests illustrating use - could be removed once examples are written
+#@time @safetestset "New Jansen-Rit Tests" begin include("jansen_rit_component_tests.jl") end
+#@time @safetestset "New Larter-Breakspear Tests" begin include("new_LB_blox.jl") end
\ No newline at end of file
diff --git a/test/temp_test.jl b/test/temp_test.jl
deleted file mode 100644
index b332e252..00000000
--- a/test/temp_test.jl
+++ /dev/null
@@ -1,17 +0,0 @@
-using Neuroblox, DifferentialEquations, DataFrames, Test, Distributions, Statistics, LinearAlgebra, Graphs, MetaGraphs, Random
-
-@named LB1 = LarterBreakspear()
-@named LB2 = LarterBreakspear()
-
-adj = [0 1; 1 0]
-g = MetaDiGraph()
-add_blox!.(Ref(g), [LB1, LB2])
-create_adjacency_edges!(g, adj)
-
-@named sys = system_from_graph(g)
-sys = structural_simplify(sys)
-
-sim_dur = 1e2
-prob = ODEProblem(sys, [], (0.0, sim_dur), [])
-sol = solve(prob, AutoVern7(Rodas4()), saveat=0.1)
-@test sol.retcode == ReturnCode.Success
\ No newline at end of file