Merge pull request #421 from Hjorthmedh/neuromod_redux2

Neuromodulation is now implemented using RxD

README.md

@@ -34,7 +34,7 @@ There are a number of examples for how to create and run networks on github whic
 
 https://github.com/Hjorthmedh/Snudda/tree/master/examples/notebooks
 
-## Command line examle
+## Command line example
 
 Once installed Snudda can also be run from the command line, using the snudda command. Below is a small list of the relevant commands that can be used.
 

examples/notebooks/README.md

@@ -8,6 +8,7 @@ Here is a collection of Jupyter Notebooks, some of the workflows are split over
 * [simple_network_parallel](simple_network_parallel.ipynb) how to use ipyparallel when running Snudda
 * [custom_slice_example](custom_slice_example.ipynb) shows how to create custom slice and define your own connectivity rules for neuron types.
 * [population_unit_network](population_unit_network.ipynb) how to define population units.
+* [population_unit_mesh](population_unit_mesh.ipynb) how to define population units specified by volume meshes.
 * [example_of_density_function](example_of_density_function.ipynb) how to specify density variations using a function of (x,y,z) in a volume.
 * [example_of_neuron_rotations](example_of_neuron_rotations.ipynb) shows how to rotate neurons based on position.
 * [bend_morphologies](bend_morphologies.ipynb) shows how to make the neurons bend the axons and dendrites at the edge of the mesh, to keep them constrained to the volume.

examples/notebooks/neuromodulation/REAME.md

@@ -0,0 +1,13 @@
+# TODO -- Neuromdulation
+
+- Run short simulation of each neuron, without dopamine, then turn on dopamine.
+- dSPN should increase frequency, iSPN should decrease frequency
+
+- Add dopamine cascade for iSPN, how do we model D1 and D2 receptors?
+- How do we treat diffusion of dopamine? (it should be external, axial diffusion)
+- Does cAMP diffuse internally?
+
+- Direct stimulation of dopamine, can we use a dopamine axon?
+- Volymetric transmission, where two cells might read the same spatial concentration.
+
+(- Tripartite synapse)

examples/notebooks/neuromodulation/config/mesh/cube-mesh-5e-05.obj

@@ -0,0 +1,33 @@
+# Generated by create_cube_mesh.py
+# Striatum cube mesh, centre: [0.00475 0.004   0.00775], side: 5e-05
+
+g cube
+
+v 4725.000000 3975.000000 7725.000000
+v 4725.000000 3975.000000 7775.000000
+v 4725.000000 4025.000000 7725.000000
+v 4725.000000 4025.000000 7775.000000
+v 4775.000000 3975.000000 7725.000000
+v 4775.000000 3975.000000 7775.000000
+v 4775.000000 4025.000000 7725.000000
+v 4775.000000 4025.000000 7775.000000
+
+vn  0.0  0.0  1.0
+vn  0.0  0.0 -1.0
+vn  0.0  1.0  0.0
+vn  0.0 -1.0  0.0
+vn  1.0  0.0  0.0
+vn -1.0  0.0  0.0
+
+f  1//2  7//2  5//2
+f  1//2  3//2  7//2
+f  1//6  4//6  3//6
+f  1//6  2//6  4//6
+f  3//3  8//3  7//3
+f  3//3  4//3  8//3
+f  5//5  7//5  8//5
+f  5//5  8//5  6//5
+f  1//4  5//4  6//4
+f  1//4  6//4  2//4
+f  2//1  6//1  8//1
+f  2//1  8//1  4//1

examples/notebooks/neuromodulation/config/network.json

@@ -0,0 +1,12 @@
+{
+    "network_path": "my_network",
+    "snudda_data": "/home/hjorth/HBP/BasalGangliaData/data",
+
+    "random_seed": {
+	"master_seed": 1234	
+    },
+
+    "regions": {
+	"Striatum" : "striatum.json"
+    }	
+}

examples/notebooks/neuromodulation/config/neurons/chin.json

@@ -0,0 +1,18 @@
+{
+    "ChIN": {
+	"neuron_path": {
+	    "ChIN_0": "$SNUDDA_DATA/neurons/striatum/chin/str-chin-e170614_cell6-m17JUL301751_170614_no6_MD_cell_1_x63-v20190710"
+	},
+
+	"neuron_type": "neuron",	    
+	"rotation_mode": "random",
+	"volume_id": "Striatum",
+	"stay_inside_mesh": false,
+	"fraction": 0.011,
+	"axon_density": [
+            "r",
+            "5000*1e12/3*exp(-r/120e-6)",
+            0.00035
+	]
+    }	
+}

examples/notebooks/neuromodulation/config/neurons/dspn.json

@@ -0,0 +1,17 @@
+{
+    "dSPN": {
+	"neuron_path": {
+	    "dSPN_0": "$SNUDDA_DATA/neurons/striatum/dspn/str-dspn-e150602_c1_D1-mWT-0728MSN01-v20211026",
+	    "dSPN_1": "$SNUDDA_DATA/neurons/striatum/dspn/str-dspn-e150917_c10_D1-mWT-P270-20-v20211026",
+	    "dSPN_2": "$SNUDDA_DATA/neurons/striatum/dspn/str-dspn-e150917_c6_D1-m21-6-DE-v20211028",
+	    "dSPN_3": "$SNUDDA_DATA/neurons/striatum/dspn/str-dspn-e150917_c9_D1-mWT-1215MSN03-v20211026"
+	},
+
+	"neuron_type": "neuron",
+	"rotation_mode": "random",
+	"volume_id": "Striatum",
+	"stay_inside_mesh": false,
+	"fraction": 0.475
+    }
+
+}

examples/notebooks/neuromodulation/config/neurons/fs.json

@@ -0,0 +1,16 @@
+{
+    "FS": {
+	"neuron_path": {
+	    "FS_0": "$SNUDDA_DATA/neurons/striatum/fs/str-fs-e160628_FS2-mMTC180800A-IDB-v20210210",
+	    "FS_1": "$SNUDDA_DATA/neurons/striatum/fs/str-fs-e161024_FS16-mDR-rat-Mar-13-08-1-536-R-v20210210",
+	    "FS_2": "$SNUDDA_DATA/neurons/striatum/fs/str-fs-e161205_FS1-mBE104E-v20210209",
+	    "FS_3": "$SNUDDA_DATA/neurons/striatum/fs/str-fs-e161205_FS1-mMTC180800A-IDB-v20210210"
+	},
+
+        "neuron_type": "neuron",
+        "rotation_mode": "random",
+        "volume_id": "Striatum",
+	"stay_inside_mesh": false,
+        "fraction": 0.013
+    }
+}

examples/notebooks/neuromodulation/config/neurons/ispn.json

@@ -0,0 +1,16 @@
+{
+    "iSPN": {
+	"neuron_path": {
+	    "iSPN_0": "$SNUDDA_DATA/neurons/striatum/ispn/str-ispn-e150908_c4_D2-m51-5-DE-v20211026",
+	    "iSPN_1": "$SNUDDA_DATA/neurons/striatum/ispn/str-ispn-e150917_c11_D2-mWT-MSN1-v20211026",
+	    "iSPN_2": "$SNUDDA_DATA/neurons/striatum/ispn/str-ispn-e151123_c1_D2-mWT-P270-09-v20211026",
+	    "iSPN_3": "$SNUDDA_DATA/neurons/striatum/ispn/str-ispn-e160118_c10_D2-m46-3-DE-v20211026"
+	},
+
+        "neuron_type": "neuron",
+        "rotation_mode": "random",
+        "volume_id": "Striatum",
+	"stay_inside_mesh": false,
+        "fraction": 0.475
+    }
+}

examples/notebooks/neuromodulation/config/neurons/lts.json

@@ -0,0 +1,27 @@
+{
+    "LTS": {
+	"neuron_path" : {
+	    "LTS_0" : "$SNUDDA_DATA/neurons/striatum/lts/LTS_180118_morp_9862_updated_20210301",
+	    "LTS_1" : "$SNUDDA_DATA/neurons/striatum/lts/LTS_180118_morp_9862_updated_April2022"	    
+	},
+
+        "neuron_type": "neuron",
+        "rotation_mode": "random",
+        "volume_id": "Striatum",
+	"stay_inside_mesh": false,
+        "fraction": 0.007,
+        "axon_density": [
+            "xyz",
+            "12*3000*1e12*( 0.25*exp(-(((x-200e-6)/100e-6)**2 + ((y-0)/50e-6)**2 + ((z-0)/30e-6)**2)) + 1*exp(-(((x-300e-6)/300e-6)**2 + ((y-0)/15e-6)**2 + ((z-0)/10e-6)**2)) + 1*exp(-(((x-700e-6)/100e-6)**2 + ((y-0)/15e-6)**2 + ((z-0)/15e-6)**2)) )",
+            [
+                -0.0002,
+                0.0009,
+                -0.0001,
+                0.0001,
+                -3e-05,
+                3e-05
+            ]
+        ]
+    }
+}
+

examples/notebooks/neuromodulation/config/pop1.json

@@ -0,0 +1,16 @@
+{
+    "populations": {
+	"method": "radial_density",
+        "centres": [[ 0.00475, 0.004, 0.00775 ],
+		    [ 0.00475, 0.004, 0.00775 ]],
+        "probability_functions": [
+            "(d < 300e-6) * 1",
+            "(d < 300e-6) * 1"
+        ],
+        "unit_id": [ 1, 2 ],
+        "neuron_types": [["dSPN", "iSPN"],
+			 ["dSPN", "iSPN"]],
+        "num_neurons": [ 4000, 4000 ]
+
+    }   
+}

examples/notebooks/neuromodulation/config/striatum.json

@@ -0,0 +1,29 @@
+{
+    "Striatum": {
+	"num_neurons": 10,
+	"volume": {
+	    "mesh_file": "config/mesh/cube-mesh-5e-05.obj",
+	    "d_min": 1.5e-5,
+	    "num_putative_points": 1000,
+	    "random_seed": 123456,
+	    "!density": {
+		"my_neuron": {
+		    "density_function": "abs(x)"
+		}
+	    },
+	    "!neuron_orientation": {
+		"my_neuron": {
+		    "rotation_mode": "vector_field",
+		    "rotation_field_file": "my_rotation_file.json"
+		}
+	    }
+	},
+	"neurons": ["neurons/dspn.json",
+		    "neurons/ispn.json",
+		    "!neurons/fs.json",
+		    "!neurons/chin.json",
+		    "!neurons/lts.json"],
+	"connectivity": ["$SNUDDA_DATA/connectivity/striatum/striatum-connectivity.json"],
+	"!populations": "pop1.json"
+    }
+}

examples/notebooks/neuromodulation/data/DASyn.mod

@@ -0,0 +1,38 @@
+NEURON {
+    POINT_PROCESS DASyn
+    RANGE quanta, tau, open
+}
+UNITS {
+    (mM) = (milli / liter)
+}
+
+PARAMETER {
+    quanta = 1e-4 (mM/ms)
+    tau = 10 (ms)
+}
+
+INITIAL {
+    open = 0
+}
+
+STATE { 
+    open (1)
+}
+
+BREAKPOINT {SOLVE state METHOD cnexp}
+
+DERIVATIVE state { 
+    open' = -open/tau
+}
+
+
+: 33000 dopamine molecules per vesicle : Omiatek, D., Bressler, A.,
+: Cans, AS. et al. The real catecholamine content of secretory vesicles
+: in the CNS revealed by electrochemical cytometry. Sci Rep 3, 1447
+: (2013). https://doi.org/10.1038/srep01447
+
+
+
+NET_RECEIVE(weight) {
+    open = open + weight
+}