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example_DRN.py
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example_DRN.py
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import numpy as np
import matplotlib.pyplot as plt
import Neurapse
from Neurapse.Synapses import *
from Neurapse.utils.SpikeTrains import *
from Neurapse.Networks import *
from Neurapse.utils.CURRENTS import *
#usage : DRN_Const problem 2 and 3
def DRN_Const_driver(N, exci_frac, connect_frac):
N_exci = int(N*exci_frac)
N_inhi = N - N_exci
# Constructing the network
exci_neuron_id = range(N_exci)
inhi_neuron_id = range(N_exci, N)
Fanout = []
W = []
Tau = []
w0 = 3000
gamma = 0.77 #for problem 3
gamma = 1# for problem 2
for i in exci_neuron_id:
a = []
for z in range(N):
if z!=i:
a.append(z)
tempFL = []
tempWL = []
tempTL = []
for j in range(int(connect_frac*N)):
t = np.random.choice(a)
tempFL.append(t)
tempWL.append(gamma*w0)
tempTL.append(np.random.uniform(1e-3, 20e-3))
W.append(tempWL)
Fanout.append(tempFL)
Tau.append(tempTL)
for i in inhi_neuron_id:
tempFL = []
tempWL = []
tempTL = []
for j in range(int(connect_frac*N)):
t = np.random.choice(exci_neuron_id)
tempFL.append(t)
tempWL.append(-w0)
tempTL.append(1e-3)
W.append(tempWL)
Fanout.append(tempFL)
Tau.append(tempTL)
print('Fanout: ', Fanout)
print('W: ', W)
print('Tau: ', Tau)
A = DRN_Const(Fanout, W, Tau_d=Tau, Tau=15e-3, I0=1e-12, N=N, T=20000, delta_t=1e-4)
# Creating poisson spike trains as inputs for first 25 neurons
# Note: Originally this was meant for poisson potential spikes,
# here using as current
T = 20000
Tau = 15e-3
delta_t = 1e-4
n_out = 25
I0 = 1e-12
ST = POISSON_SPIKE_TRAIN(T=int(T*delta_t), delta_t=delta_t, lamb=100, n_out=n_out)
V_poi_spikes = ST.V_train[:,:-1]
# print(V_poi_spikes.shape)
reference_alpha = np.zeros(T)
for t in range(3*int(Tau//delta_t)):
reference_alpha[t] = np.exp(-t*delta_t/Tau) - np.exp(-4*t*delta_t/Tau)
# plt.plot(reference_alpha)
# plt.show()
I_poi = np.zeros(shape=(n_out, T))
for idx in range(n_out):
V_sp = V_poi_spikes[idx,:].reshape(1,-1)
print(V_sp.shape)
for t,v in enumerate(V_sp[0,:]):
if v>0:
t1 = t
t2 = min(t+int(3*Tau//delta_t), T)
I_poi[idx, t1:t2] += w0*I0*reference_alpha[0:t2-t1]
# for idx in range(n_out):
# plt.plot(I_poi[idx,:])
# plt.show()
I_app = np.zeros(shape=(N, T))
I_app[0:n_out, :] = I_poi
El = -70e-3
V_thresh = 20e-3
V0 = El*np.ones(shape=(N,1))
A.compute(I_app=I_app, V0=V0, delta_t=delta_t)
V_response = A.V_collector
I_app_feed = A.I_app_collector #same as I_app
I_synapse_feed = A.I_synapse_feed
exci_spike_instants = get_spike_instants_from_neuron(
V_response[exci_neuron_id,:],
V_thresh
)
inhi_spike_instants = get_spike_instants_from_neuron(
V_response[inhi_neuron_id,:],
V_thresh
)
# print(exci_spike_instants)
# print(inhi_spike_instants)
colorCodes = np.array(
[[0,0,0]]*N_exci
+
[[0,0,1]]*N_inhi
)
plt.eventplot(exci_spike_instants + inhi_spike_instants, color=colorCodes, lineoffsets=2)
plt.show()
# for i in range(N):
# plt.plot(I_synapse_feed[i, :])
# plt.show()
# Ret and Rit
Ret = []
Rit = []
for l in exci_spike_instants:
Ret = Ret+list(l)
for l in inhi_spike_instants:
Rit = Rit+list(l)
Ret_sorted = sorted(Ret)
Rit_sorted = sorted(Rit)
t0 = 100
plt.figure(figsize=(25, 25))
plt.subplot(2,1,1)
plt.hist(Ret_sorted, bins=int(T/t0))
plt.xlabel('time')
plt.ylabel('freq Ret')
plt.subplot(2,1,2)
plt.hist(Rit_sorted, bins=int(T/t0))
plt.xlabel('time')
plt.ylabel('freq Rit')
plt.show()
#usage : DRN_Plastic problem 4 and 5
def DRN_Plastic_driver(N, exci_frac, connect_frac):
N_exci = int(N*exci_frac)
N_inhi = N - N_exci
# Constructing the network
exci_neuron_id = range(N_exci)
inhi_neuron_id = range(N_exci, N)
Fanout = []
W = []
Tau = []
w0 = 3000
# gamma = 1 #for problem 4
gamma = 0.4 # for problem 5
for i in exci_neuron_id:
a = []
for z in range(N):
if z!=i:
a.append(z)
tempFL = []
tempWL = []
tempTL = []
for j in range(int(connect_frac*N)):
t = np.random.choice(a)
tempFL.append(t)
tempWL.append(gamma*w0)
tempTL.append(np.random.uniform(1e-3, 20e-3))
W.append(tempWL)
Fanout.append(tempFL)
Tau.append(tempTL)
for i in inhi_neuron_id:
tempFL = []
tempWL = []
tempTL = []
for j in range(int(connect_frac*N)):
t = np.random.choice(exci_neuron_id)
tempFL.append(t)
tempWL.append(-w0)
tempTL.append(1e-3)
W.append(tempWL)
Fanout.append(tempFL)
Tau.append(tempTL)
print('Fanout: ', Fanout)
print('W: ', W)
print('Tau: ', Tau)
A = DRN_Plastic(Fanout, W, Tau_d=Tau, Tau=15e-3, Tau_l=20e-3, I0=1e-12, A_up=0.01, A_dn=-0.07, N=N, N_exci=N_exci, T=20000, delta_t=1e-4, gamma=gamma)
# Creating poisson spike trains as inputs for first 25 neurons
# Note: Originally this was meant for poisson potential spikes,
# here using as current
T = 20000
Tau = 15e-3
delta_t = 1e-4
n_out = 25
I0 = 1e-12
ST = POISSON_SPIKE_TRAIN(T=int(T*delta_t), delta_t=delta_t, lamb=100, n_out=n_out)
V_poi_spikes = ST.V_train[:,:-1]
# print(V_poi_spikes.shape)
reference_alpha = np.zeros(T)
for t in range(3*int(Tau//delta_t)):
reference_alpha[t] = np.exp(-t*delta_t/Tau) - np.exp(-4*t*delta_t/Tau)
# plt.plot(reference_alpha)
# plt.show()
I_poi = np.zeros(shape=(n_out, T))
for idx in range(n_out):
V_sp = V_poi_spikes[idx,:].reshape(1,-1)
print(V_sp.shape)
for t,v in enumerate(V_sp[0,:]):
if v>0:
t1 = t
t2 = min(t+int(3*Tau//delta_t), T)
I_poi[idx, t1:t2] += w0*I0*reference_alpha[0:t2-t1]
# for idx in range(n_out):
# plt.plot(I_poi[idx,:])
# plt.show()
I_app = np.zeros(shape=(N, T))
I_app[0:n_out, :] = I_poi
El = -70e-3
V_thresh = 20e-3
V0 = El*np.ones(shape=(N,1))
A.compute(I_app=I_app, V0=V0, delta_t=delta_t)
V_response = A.V_collector
I_app_feed = A.I_app_collector #same as I_app
I_synapse_feed = A.I_synapse_feed
avg_weights = A.avg_weights
plt.plot(avg_weights)
plt.xlabel('time steps')
plt.ylabel('mean excitatory synapse weights')
plt.show()
exci_spike_instants = get_spike_instants_from_neuron(
V_response[exci_neuron_id,:],
V_thresh
)
inhi_spike_instants = get_spike_instants_from_neuron(
V_response[inhi_neuron_id,:],
V_thresh
)
colorCodes = np.array(
[[0,0,0]]*N_exci
+
[[0,0,1]]*N_inhi
)
plt.eventplot(exci_spike_instants + inhi_spike_instants, color=colorCodes, lineoffsets=2)
plt.show()
# Ret and Rit
Ret = []
Rit = []
for l in exci_spike_instants:
Ret = Ret+list(l)
for l in inhi_spike_instants:
Rit = Rit+list(l)
Ret_sorted = sorted(Ret)
Rit_sorted = sorted(Rit)
t0 = 100
plt.figure(figsize=(25, 25))
plt.subplot(2,1,1)
plt.hist(Ret_sorted, bins=int(T/t0))
plt.xlabel('time')
plt.ylabel('freq Ret')
plt.subplot(2,1,2)
plt.hist(Rit_sorted, bins=int(T/t0))
plt.xlabel('time')
plt.ylabel('freq Rit')
plt.show()
# DRN_const_driver(N=200, exci_frac=0.8, connect_frac=0.1)
DRN_Plastic_driver(N=200, exci_frac=0.8, connect_frac=0.1)