def load_transfer_functions(NRN1, NRN2, NTWK):
# NTWK
M = get_connectivity_and_synapses_matrix(NTWK, SI_units=True)
# NRN1
params1 = get_neuron_params(NRN1, SI_units=True)
reformat_syn_parameters(params1, M)
try:
P1 = np.load('data/RS-cell_CONFIG1_fit.npy')
params1['P'] = P1
def TF1(fe, fi, XX):
return TF_my_templateup(fe, fi, XX, *pseq_params(params1))
except IOError:
print('=======================================================')
print('===== fit for NRN1 not available ====================')
print('=======================================================')
# NRN1
params2 = get_neuron_params(NRN2, SI_units=True)
reformat_syn_parameters(params2, M)
try:
P2 = np.load('data/FS-cell_CONFIG1_fit.npy')
params2['P'] = P2
def TF2(fe, fi, XX):
return TF_my_templateup(fe, fi, XX, *pseq_params(params2))
except IOError:
print('=======================================================')
print('===== fit for NRN2 not available ====================')
print('=======================================================')
return TF1, TF2
def build_up_differential_operator_first_order(TF1,
TF2,
NRN1,
NRN2,
NTWK,
T=5e-3):
M = get_connectivity_and_synapses_matrix(NTWK, SI_units=True)
params = get_neuron_params(NRN1, SI_units=True)
reformat_syn_parameters(params, M)
a, b, tauw = params['a'],\
params['b'], params['tauw']
Qe, Te, Ee = params['Qe'], params['Te'], params['Ee']
Qi, Ti, Ei = params['Qi'], params['Ti'], params['Ei']
Gl, Cm, El = params['Gl'], params['Cm'], params['El']
pconnec, Ntot, gei, ext_drive = params['pconnec'], params['Ntot'], params[
'gei'], M[0, 0]['ext_drive']
def A0(V, exc_aff=0, inh_aff=0, pure_exc_aff=0):
return 1. / T * (
TF1(V[0] + exc_aff + pure_exc_aff, V[1] + inh_aff, V[2]) - V[0])
def A1(V, exc_aff=0, inh_aff=0, pure_exc_aff=0, inh_fract=0):
return 1. / T * (TF2(V[0] + exc_aff + inh_fract * pure_exc_aff,
V[1] + inh_aff, V[3]) - V[1])
def A2(V, exc_aff=0, inh_aff=0, pure_exc_aff=0):
fe = (V[0] + exc_aff +
pure_exc_aff) * (1. - gei) * pconnec * Ntot # default is 1 !!
fi = V[1] * gei * pconnec * Ntot
muGe, muGi = Qe * Te * fe, Qi * Ti * fi
muG = Gl + muGe + muGi
muV = (muGe * Ee + muGi * Ei + Gl * El - V[2]) / muG
return (-V[2] / tauw + (b) * V[0] + a * (muV - El) / tauw)
def A3(V, exc_aff=0, inh_aff=0, pure_exc_aff=0):
return (-V[3] / 1.0 + 0. * V[1])
def Diff_OP(V, exc_aff=0, inh_aff=0, pure_exc_aff=0, inh_fract=0):
return np.array([A0(V, exc_aff=exc_aff,inh_aff=inh_aff, pure_exc_aff=pure_exc_aff),\
A1(V, exc_aff=exc_aff, inh_aff=inh_aff, pure_exc_aff=pure_exc_aff,inh_fract=inh_fract),\
A2(V, exc_aff=exc_aff, inh_aff=inh_aff, pure_exc_aff=pure_exc_aff),\
A3(V, exc_aff=exc_aff, inh_aff=inh_aff, pure_exc_aff=pure_exc_aff)])
return Diff_OP
if __name__=='__main__':
print(__doc__)
# starting from an example
from brian2 import *
from cell_library import get_neuron_params
import sys
sys.path.append('../code/')
from my_graph import set_plot
for model, c in zip(['RS-cell', 'FS-cell'], ['g', 'r']):
neurons, eqs = get_membrane_equation(get_neuron_params(model), [],\
return_equations=True)
fig, ax = plt.subplots(figsize=(5,3))
print('------------- NEURON model :', model)
print(eqs)
# V value initialization
neurons.V = -65.*mV
trace = StateMonitor(neurons, 'V', record=0)
spikes = SpikeMonitor(neurons)
run(100 * ms)
neurons.I0 = 200*pA
run(400 * ms)
neurons.I0 = 0*pA
run(200 * ms)
# We draw nicer spikes
V = trace[0].V[:]
def run_mean_field_2order_secondway(NRN1,
NRN2,
NTWK,
T=5e-3,
dt=1e-4,
tstop=2):
amp = 5
t0 = 2.
T1 = 0.01
T2 = 0.2
T = T
dt = dt
tstop = 4 #end of simulation
#take network parameters
M = get_connectivity_and_synapses_matrix(NTWK, SI_units=True)
params = get_neuron_params(NRN1, SI_units=True)
reformat_syn_parameters(params, M)
a, b, tauw = params['a'],\
params['b'], params['tauw']
Qe, Te, Ee = params['Qe'], params['Te'], params['Ee']
Qi, Ti, Ei = params['Qi'], params['Ti'], params['Ei']
Gl, Cm, El = params['Gl'], params['Cm'], params['El']
pconnec, Ntot, gei, ext_drive = params['pconnec'], params['Ntot'], params[
'gei'], M[0, 0]['ext_drive']
#IMPORTANT PARAMETERS
#to change network size
Ntot = 10000
extinp = 2.5
sigma_0 = 10.
filesave = 'mean_field.npy'
Ne = Ntot * (1 - gei)
Ni = Ntot * gei
TF1, TF2 = load_transfer_functions(NRN1, NRN2, NTWK)
t = np.arange(int(tstop / dt)) * dt
fe = 0 * t
fi = 0 * t
ww = 0 * t
v2vec = 0 * t
v3vec = 0 * t
v4vec = 0 * t
extinpnuovo = double_gaussian(t, t0, T1, T2, amp)
ornstein = 0
ornsteinin2 = 0
extinpnoisex1 = 0
extinpnoisex = 0
ornsteinA = 0.0002 #time decay ornstein process
#initial conditions
fecont = .8
ficont = 50
v2 = 0.5
v3 = 0.5
v4 = 0.5
wcont = fecont * b * tauw
def dX_dt_scalar(X, t=0):
exc_aff = extinp * t / t
pure_exc_aff = extinpnuovo
return build_up_differential_operator(TF1, TF2,NRN1, NRN2, NTWK, Ne=Ne, Ni=Ni, T=T)(X,\
exc_aff=exc_aff, pure_exc_aff=pure_exc_aff,inh_fract=inh_fract)
X0 = [1., 30, .5, .5, .5, 1.e-10, 0.]
X = np.zeros((len(t), len(X0)))
for i in range(0, 6):
X[0][i] = X0[i]
inh_fract = 1.
for i in range(len(t) - 1):
stdvexc = np.sqrt(X[i, 0])
stdvinh = np.sqrt(X[i, 1])
if (t[i] < t0):
adjust = extinpnuovo.max()
else:
adjust = 0
exc_aff = extinp
pure_exc_aff = (extinpnuovo[i] + 0 * adjust)
X[i+1,:] = X[i,:] + (t[1]-t[0])*build_up_differential_operator(TF1, TF2,NRN1, NRN2, NTWK, \
Ne=Ne, Ni=Ni)(X[i,:], exc_aff=exc_aff, pure_exc_aff=pure_exc_aff,inh_fract=inh_fract)
for j in range(0, 6):
if X[i + 1][j] < 0:
X[i + 1][j] = 1.e-9
for j in range(0, 6):
if X[i + 1][j] > 175.:
X[i + 1][j] = 175.
X[i + 1, 6] = 0
fe, fi = X[:, 0], X[:, 1]
sfe, sfei, sfi = [np.sqrt(X[:, i]) for i in range(2, 5)]
XXe, XXi = X[:, 5], X[:, 6]
plt.plot(t, fe)
plt.show()
np.save(filesave, [t, fe, fi, sfe, sfei, sfi, XXe, XXi])
print(__doc__)
# starting from an example
# starting from an example
NTWK = 'CONFIG1'
M = get_connectivity_and_synapses_matrix(NTWK, number=2)
NRN_exc = 'HH_RS'
# number of neurons
Ne, Ni = int(M[0, 0]['Ntot'] * (1 - M[0, 0]['gei'])), int(M[0, 0]['Ntot'] *
M[0, 0]['gei'])
print("EEEE", NRN_exc)
exc_neurons, eqs = get_membrane_equation(get_neuron_params(NRN_exc,
number=Ne),
M[:, 0],
return_equations=True)
neuron = NeuronGroup(1,
eqs,
threshold='V > -40*mV',
refractory='V > -40*mV',
method='exponential_euler')
neuron.V = 0.
neuron.p = '.2'
neuron.m = '.2'
neuron.h = '.2'
neuron.n = '.2'