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Differentiale static and time-dependent inputs in WC and WW model #241

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Sep 11, 2023
Merged

Differentiale static and time-dependent inputs in WC and WW model #241

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618d88b
differentiate static and time-dependent inputs
lenasal Aug 30, 2023
ceedfae
update example
lenasal Aug 30, 2023
e8a3a96
revert example change
lenasal Aug 30, 2023
6cae278
Merge branch 'neurolib-dev:master' into master
lenasal Aug 30, 2023
1ae3e1b
update aln model with correct input_vars
lenasal Sep 5, 2023
208729d
Merge branch 'neurolib-dev:master' into master
lenasal Sep 7, 2023
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6 changes: 4 additions & 2 deletions neurolib/models/wc/loadDefaultParams.py
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Original file line number Diff line number Diff line change
Expand Up @@ -67,8 +67,10 @@ def loadDefaultParams(Cmat=None, Dmat=None, seed=None):
params.mu_inh = 3.0 # inhibitory firing threshold

# values of the external inputs
params.exc_ext = 0 # baseline external input to E
params.inh_ext = 0 # baseline external input to I
params.exc_ext_baseline = 0 # baseline external input to E (static)
params.inh_ext_baseline = 0 # baseline external input to I (static)
params.exc_ext = 0 # time-dependent external input to E
params.inh_ext = 0 # time-dependent external input to I

# ------------------------------------------------------------------------

Expand Down
21 changes: 14 additions & 7 deletions neurolib/models/wc/timeIntegration.py
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Expand Up @@ -80,6 +80,9 @@ def timeIntegration(params):
excs = np.zeros((N, startind + len(t)))
inhs = np.zeros((N, startind + len(t)))

exc_ext_baseline = params["exc_ext_baseline"]
inh_ext_baseline = params["inh_ext_baseline"]

exc_ext = mu.adjustArrayShape(params["exc_ext"], excs)
inh_ext = mu.adjustArrayShape(params["inh_ext"], inhs)

Expand Down Expand Up @@ -125,6 +128,8 @@ def timeIntegration(params):
inhs,
exc_input_d,
inh_input_d,
exc_ext_baseline,
inh_ext_baseline,
exc_ext,
inh_ext,
tau_exc,
Expand Down Expand Up @@ -162,6 +167,8 @@ def timeIntegration_njit_elementwise(
inhs,
exc_input_d,
inh_input_d,
exc_ext_baseline,
inh_ext_baseline,
exc_ext,
inh_ext,
tau_exc,
Expand Down Expand Up @@ -192,10 +199,8 @@ def S_I(x):
return 1.0 / (1.0 + np.exp(-a_inh * (x - mu_inh)))

for i in range(startind, startind + len(t)):

# loop through all the nodes
for no in range(N):

# To save memory, noise is saved in the activity array
noise_exc[no] = excs[no, i]
noise_inh[no] = inhs[no, i]
Expand All @@ -217,8 +222,9 @@ def S_I(x):
c_excexc * excs[no, i - 1] # input from within the excitatory population
- c_inhexc * inhs[no, i - 1] # input from the inhibitory population
+ exc_input_d[no] # input from other nodes
+ exc_ext[no, i - 1]
) # external input
+ exc_ext_baseline # baseline external input (static)
+ exc_ext[no, i - 1] # time-dependent external input
)
+ exc_ou[no] # ou noise
)
)
Expand All @@ -231,8 +237,9 @@ def S_I(x):
* S_I(
c_excinh * excs[no, i - 1] # input from the excitatory population
- c_inhinh * inhs[no, i - 1] # input from within the inhibitory population
+ inh_ext[no, i - 1]
) # external input
+ inh_ext_baseline # baseline external input (static)
+ inh_ext[no, i - 1] # time-dependent external input
)
+ inh_ou[no] # ou noise
)
)
Expand All @@ -246,7 +253,7 @@ def S_I(x):
excs[no, i] = 1.0
if excs[no, i] < 0.0:
excs[no, i] = 0.0

if inhs[no, i] > 1.0:
inhs[no, i] = 1.0
if inhs[no, i] < 0.0:
Expand Down
6 changes: 4 additions & 2 deletions neurolib/models/ww/loadDefaultParams.py
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Original file line number Diff line number Diff line change
Expand Up @@ -65,14 +65,16 @@ def loadDefaultParams(Cmat=None, Dmat=None, seed=None):
params.tau_exc = 100.0 # ms
params.gamma_exc = 0.641
params.w_exc = 1.0
params.exc_current = 0.382 # nA
params.exc_current_baseline = 0.382 # nA, baseline external input current (static)
params.exc_current = 0 # time-dependent external input current to E

params.a_inh = 0.615 # nC^-1
params.b_inh = 0.177 # kHz
params.d_inh = 87.0 # ms
params.tau_inh = 10.0 # ms
params.w_inh = 0.7
params.inh_current = 0.382 # nA
params.inh_current_baseline = 0.382 # nA, baseline external input current (static)
params.inh_current = 0 # time-dependent external input current to E

params.J_NMDA = 0.15 # nA, excitatory synaptic coupling
params.J_I = 1.0 # nA, inhibitory synaptic coupling
Expand Down
23 changes: 17 additions & 6 deletions neurolib/models/ww/timeIntegration.py
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Original file line number Diff line number Diff line change
Expand Up @@ -25,14 +25,14 @@ def timeIntegration(params):
tau_exc = params["tau_exc"]
gamma_exc = params["gamma_exc"]
w_exc = params["w_exc"]
exc_current = params["exc_current"]
exc_current_baseline = params["exc_current_baseline"]

a_inh = params["a_inh"]
b_inh = params["b_inh"]
d_inh = params["d_inh"]
tau_inh = params["tau_exc"]
w_inh = params["w_inh"]
inh_current = params["inh_current"]
inh_current_baseline = params["inh_current_baseline"]

J_NMDA = params["J_NMDA"]
J_I = params["J_I"]
Expand Down Expand Up @@ -102,6 +102,9 @@ def timeIntegration(params):
r_exc = np.zeros((N, startind + len(t)))
r_inh = np.zeros((N, startind + len(t)))

exc_current = mu.adjustArrayShape(params["exc_current"], r_exc)
inh_current = mu.adjustArrayShape(params["inh_current"], r_inh)

# ------------------------------------------------------------------------
# Set initial values
# if initial values are just a Nx1 array
Expand Down Expand Up @@ -152,12 +155,14 @@ def timeIntegration(params):
gamma_exc,
w_exc,
exc_current,
exc_current_baseline,
a_inh,
b_inh,
d_inh,
tau_inh,
w_inh,
inh_current,
inh_current_baseline,
J_NMDA,
J_I,
w_ee,
Expand Down Expand Up @@ -197,12 +202,14 @@ def timeIntegration_njit_elementwise(
gamma_exc,
w_exc,
exc_current,
exc_current_baseline,
a_inh,
b_inh,
d_inh,
tau_inh,
w_inh,
inh_current,
inh_current_baseline,
J_NMDA,
J_I,
w_ee,
Expand Down Expand Up @@ -232,16 +239,15 @@ def timeIntegration_njit_elementwise(
r = (a * I - b) / (1.0 - exp(-d * (a * I - b)))

"""

# firing rate transfer function
def r(I, a, b, d):
return (a * I - b) / (1.0 - np.exp(-d * (a * I - b)))

### integrate ODE system:
for i in range(startind, startind + len(t)):

# loop through all the nodes
for no in range(N):

# To save memory, noise is saved in the activity array
noise_se[no] = ses[no, i]
noise_si[no] = sis[no, i]
Expand All @@ -257,8 +263,13 @@ def r(I, a, b, d):
se = ses[no, i - 1]
si = sis[no, i - 1]

I_exc = w_exc * exc_current + w_ee * J_NMDA * se - J_I * si + J_NMDA * ses_input_d[no]
I_inh = w_inh * inh_current + J_NMDA * se - si
I_exc = (
w_exc * (exc_current_baseline + exc_current[no, i - 1])
+ w_ee * J_NMDA * se
- J_I * si
+ J_NMDA * ses_input_d[no]
)
I_inh = w_inh * (inh_current_baseline + inh_current[no, i - 1]) + J_NMDA * se - si

r_exc[no, i] = r(I_exc, a_exc, b_exc, d_exc)
r_inh[no, i] = r(I_inh, a_inh, b_inh, d_inh)
Expand Down
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