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LIF_numba_parallel-2.py
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LIF_numba_parallel-2.py
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# same as above, but jitted with numba (gives a 2x performance boost)
import time
import numpy as np
from LIFclasses import *
from numba import jit, prange
import matplotlib.pyplot as plt
seed = 20
np.random.seed(seed)
N = 3000
T = 100
p = 0.2
@jit (nopython=True, fastmath=True, nogil=True, parallel=True)
def run_numba():
global N, T, p
dt = 0.01
t = np.arange(0, T, dt)
muII = 0.02
muEE = 0.16
muIE = 0.12
muEI = 0.46
sigJ = 0.3
Jmin = 0.0015
Jmax = 1.75
avg_within_ass = 0.6
refractory_period = 1
AP = np.zeros((N, ))
# equilibrium potentials:
V_E = 0.0
V_I = -80.0 # equilibrium potential for the inhibitory synapse
EL = -65.0 # leakage potential, mV
# critical voltages:
Vth = -55.0 # threshold after which an AP is fired, mV
Vr = -70.0 # reset voltage (after an AP is fired), mV
Vspike = 10.0
U = 0.98
taustf = 50.0
taustd = 250.0
# define neuron types in the network:
neur_type_mask = np.zeros_like(AP)
neur_type_mask[:int(N*0.2)] = 0
neur_type_mask[int(N*0.2):] = 1
# initialize spikes
# exc_id = np.where(neur_type_mask == 1)[0]
# ons = np.random.choice(exc_id, int(0.4*len(exc_id)), replace=False)
# for a in ons:
# AP[a] = 1
ons = np.array([2554, 762, 32, 2065, 2892, 2561, 412, 2760, 431, 2489, 312,
250, 1399, 1385, 1265, 1157, 2899, 2122, 344, 2013, 1665, 2681,
1954, 369, 842, 737, 464, 438, 1972, 1029, 888, 467, 265,
279, 553, 2640, 1111, 1164, 391, 1306, 840, 1619, 1591, 139,
555, 1462, 317, 313, 1497, 2479, 1510, 712, 1050, 2845, 2866,
2808, 569, 448, 2399, 973, 2484, 2030, 2516, 2382, 2753, 14,
640, 18, 627, 748, 1946, 1174, 143, 338, 1235, 45, 961,
216, 2691, 937, 193, 1933, 2041, 307, 1676, 2851, 2673, 759,
1741, 2225, 680, 207, 1286, 1280, 1631, 500, 2163, 2530, 2474,
1347, 2087, 2384, 392, 1260, 1536, 2930, 1522, 1719, 2099, 1155,
2374, 2769, 2098, 181, 2402, 583, 1468, 2265, 852, 2676, 2535,
476, 2170, 2742, 2720, 1093, 2499, 2486, 1531, 2081, 12, 1820,
2409, 49, 895, 326, 2305, 659, 1044, 256, 396, 1871, 685,
1362, 1895, 590, 1049, 644, 155, 815, 335, 389, 1649, 1932,
2339, 2888, 2263, 1355, 2581, 1319, 2498, 632, 1642, 1720, 540,
1561, 1601, 735, 2207, 189, 496, 1173, 1315, 1766, 935, 302,
168, 974, 1239, 2804, 163, 1745, 1892, 986, 269, 1423, 2128,
2829, 998, 642, 743, 1627, 845, 2795, 1414, 2568, 2939, 1690,
1981, 2773, 270, 2989, 1456, 403, 987, 2981, 303, 2381, 2653,
2573, 501, 1086, 1407, 249, 1821, 792, 2033, 33, 1071, 2160,
2869, 1796, 572, 989, 2429, 1969, 1912, 2849, 509, 355, 2836,
2093, 2164, 2460, 1091, 1035, 1316, 1143, 171, 2820, 421, 972,
2400, 2584, 2118, 272, 615, 985, 2847, 1838, 709, 2528, 2838,
323, 328, 542, 2588, 2800, 1872, 1729, 2657, 843, 2822, 13,
673, 2096, 480, 2921, 1628, 56, 940, 641, 766, 1860, 1382,
55, 1753, 2718, 2540, 965, 1212, 948, 1137, 2619, 1476, 2931,
1160, 4, 984, 918, 214, 524, 2044, 1204, 2834, 53, 1682,
351, 2008, 580])
for a in ons:
AP[a] = 1
# taus
tau = np.zeros((N, ))
idx = np.where(neur_type_mask == 0)[0]
for j in idx:
tau[j] = 10
idx = np.where(neur_type_mask == 1)[0]
for j in idx:
tau[j] = 20
tau_ampa = 8
tau_nmda = 100
tau_gaba = 8
V = np.ones((N, )) * EL
# define weights:
w = np.zeros((N, N))
NE = int(N*0.8)
NI = N-NE
# II
for i in range(NI):
for j in range(NI):
if np.random.rand() < p:
w[i,j] = (1.0 + np.random.randn() * sigJ) * muII
if w[i][j] < Jmin:
w[i][j] = Jmin
if w[i][j] > Jmax:
w[i][j] = Jmax
# IE
for i in range(NI,N):
for j in range(NI):
if np.random.rand() < p:
w[i,j] = (1.0 + np.random.randn() * sigJ) * muIE
if w[i][j] < Jmin:
w[i][j] = Jmin;
if w[i][j] > Jmax:
w[i][j] = Jmax
# EI
for i in range(NI):
for j in range(NI,N):
if np.random.rand() < p:
w[i,j] = (1.0 + np.random.randn() * sigJ) * muEI
if w[i][j] < Jmin:
w[i][j] = Jmin;
if w[i][j] > Jmax:
w[i][j] = Jmax
# EE
for i in range(NI,N):
for j in range(NI,N):
if np.random.rand() < p:
w[i,j] = (1.0 + np.random.randn() * sigJ) * muEE
if (i >= 500) & (i < 1000) & (j >= 500) & (j < 1000):
w[i][j] = avg_within_ass * (1.0 + sigJ*np.random.randn())
if (i >= 1000) & (i < 1500) & (j >= 1000) & (j < 1500):
w[i][j] = avg_within_ass * (1.0 + sigJ*np.random.randn())
if (i >= 1500) & (i < 2000) & (j >= 1500) & (j < 2000):
w[i][j] = avg_within_ass * (1.0 + sigJ*np.random.randn())
if (i >= 2000) & (i < 2500) & (j >= 2000) & (j < 2500):
w[i][j] = avg_within_ass * (1.0 + sigJ*np.random.randn())
if (i >= 2500) & (i < 3000) & (j >= 2500) & (j < 3000):
w[i][j] = avg_within_ass * (1.0 + sigJ*np.random.randn())
if w[i][j] < Jmin:
w[i][j] = Jmin;
if w[i][j] > Jmax:
w[i][j] = Jmax
# prohibit self-connections
for i in range(N):
w[i, i] = 0
i = 0
t = np.arange(0, T, dt)
V = V * 0 + EL
EPSILON = 0.001
ampa = np.zeros((N, N))
nmda = np.zeros((N, N))
gaba = np.zeros((N, N))
in_refractory = np.zeros((N, )) # !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! -----------------------------
VV = np.zeros((N, len(t)))
FF = np.zeros((N, len(t)))
DD = np.zeros((N, len(t)))
# AMPA = np.zeros((N, N, len(t)))
# NMDA = np.zeros((N, N, len(t)))
# GABA = np.zeros((N, N, len(t)))
F = np.ones((N, )) * U
D = np.ones((N, ))
dV = np.zeros((N, ))
I_E = np.zeros((N, ))
I_I = np.zeros((N, ))
delayed_spike = np.zeros((N, ))
spike_timer = -np.ones((N, ))
for i in range(len(t)):
if i%120==0:
print(i)
# AP[7] = 1.0
# V[7] = Vspike + 5.0
for ii in prange(N):
if AP[ii] == 1.0:
if (t[i] > 1):
spike_timer[ii] = 0.01 + np.random.rand()*0.2
in_refractory[ii] = refractory_period + np.random.rand()*0.1
else:
spike_timer[ii] = 0.01
in_refractory[ii] = refractory_period;
AP[ii] = 0.0
if np.abs(spike_timer[ii]) < EPSILON:
delayed_spike[ii] = 1.0
else:
delayed_spike[ii] = 0.0
I_E[ii] = 0.0
I_I[ii] = 0.0
for jj in range(N):
# ampa[ii, jj] += (-ampa[ii, jj] / tau_ampa + neur_type_mask[jj] * delayed_spike[jj] * w[ii, jj]) * dt
# nmda[ii, jj] += (-nmda[ii, jj] / tau_nmda + neur_type_mask[jj] * delayed_spike[jj] * w[ii, jj]) * dt
# gaba[ii, jj] += (-gaba[ii, jj] / tau_gaba + (1.0 - neur_type_mask[jj]) * delayed_spike[jj] * w[ii, jj]) * dt
ampa[ii, jj] += (-ampa[ii, jj] / tau_ampa + neur_type_mask[jj] * F[jj] * D[jj] * delayed_spike[jj] * w[ii, jj]) * dt
nmda[ii, jj] += (-nmda[ii, jj] / tau_nmda + neur_type_mask[jj] * F[jj] * D[jj] * delayed_spike[jj] * w[ii, jj]) * dt
gaba[ii, jj] += (-gaba[ii, jj] / tau_gaba + (1.0 - neur_type_mask[jj]) * F[jj] * D[jj] * delayed_spike[jj] * w[ii, jj]) * dt
I_E[ii] += -ampa[ii,jj] * (V[ii] - V_E) - 0.1 * nmda[ii,jj] * (V[ii] - V_E)
I_I[ii] += -gaba[ii,jj] * (V[ii] - V_I)
dV[ii] = (-(V[ii] - EL) / tau[ii] + I_E[ii] + I_I[ii] ) * dt
if V[ii] >= Vspike:
V[ii] = Vr
if in_refractory[ii] > EPSILON:
dV[ii] = 0.0
V[ii] += dV[ii]
if V[ii] > Vth:
V[ii] = Vspike
AP[ii] = 1
# STPonSpike
F[ii] += U * (1.0 - F[ii]);
D[ii] -= D[ii] * F[ii];
VV[ii, i] = V[ii]
# AMPA[ii, :, i] = ampa[ii, :]
# NMDA[ii, :, i] = nmda[ii, :]
# GABA[ii, :, i] = gaba[ii, :]
# STPonNoSpike
F[ii] += dt * (U - F[ii])/taustf
D[ii] += dt * (1.0 - D[ii])/taustd
FF[ii, i] = F[ii]
DD[ii, i] = D[ii]
in_refractory[ii] -= dt
spike_timer[ii] -= dt
AMPA, NMDA, GABA = 0, 0, 0
ons = 0
return w, neur_type_mask, t, AMPA, NMDA, GABA, VV, ons, FF, DD
tt = time.time()
w, neur_type_mask, t, AMPA, NMDA, GABA, VV, ons, FF, DD = run_numba()
print(f'{(time.time() - tt):.2f} s')
x, y = np.where(VV > 0)
plt.plot(y, x, 'bo', ms=3)
# plt.xlim(0, 100)
_ = plt.ylim(0, 3000)
plt.show()
plt.figure(figsize=(5,5))
plt.imshow(w)
plt.show()