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Wang_2002_decision_making_spiking.py
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Wang_2002_decision_making_spiking.py
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# -*- coding: utf-8 -*-
import brainpy as bp
import brainpy.math as bm
import matplotlib.pyplot as plt
print(bp.__version__)
# bm.set_platform('cpu')
class PoissonStim(bp.NeuGroup):
def __init__(self, size, freq_mean, freq_var, t_interval):
super(PoissonStim, self).__init__(size=size)
# parameters
self.freq_mean = freq_mean
self.freq_var = freq_var
self.t_interval = t_interval
# variables
self.freq = bp.init.variable_(bm.zeros, 1, self.mode)
self.freq_t_last_change = bp.init.variable_(lambda s: bm.ones(s) * -1e7, 1, self.mode)
self.spike = bp.init.variable_(lambda s: bm.zeros(s, dtype=bool), self.varshape, self.mode)
self.rng = bm.random.RandomState()
def reset_state(self, batch_size=None):
self.freq.value = bp.init.variable_(bm.zeros, 1, batch_size)
self.freq_t_last_change.value = bp.init.variable_(lambda s: bm.ones(s) * -1e7, 1, batch_size)
self.spike.value = bp.init.variable_(lambda s: bm.zeros(s, dtype=bool), self.varshape, batch_size)
def update(self):
t = bp.share['t']
dt = bp.share['dt']
in_interval = bm.logical_and(pre_stimulus_period < t, t < pre_stimulus_period + stimulus_period)
in_interval = bm.ones_like(self.freq, dtype=bool) * in_interval
prev_freq = bm.where(in_interval, self.freq, 0.)
in_interval = bm.logical_and(in_interval, (t - self.freq_t_last_change) >= self.t_interval)
self.freq.value = bm.where(in_interval, self.rng.normal(self.freq_mean, self.freq_var, self.freq.shape), prev_freq)
self.freq_t_last_change.value = bm.where(in_interval, t, self.freq_t_last_change)
shape = (self.spike.shape[:1] + self.varshape) if isinstance(self.mode, bm.BatchingMode) else self.varshape
self.spike.value = self.rng.random(shape) < self.freq * dt / 1000.
class DecisionMaking(bp.Network):
def __init__(self, scale=1., mu0=40., coherence=25.6, f=0.15):
super(DecisionMaking, self).__init__()
num_exc = int(1600 * scale)
num_inh = int(400 * scale)
num_A = int(f * num_exc)
num_B = int(f * num_exc)
num_N = num_exc - num_A - num_B
print(f'Total network size: {num_exc + num_inh}')
poisson_freq = 2400. # Hz
w_pos = 1.7
w_neg = 1. - f * (w_pos - 1.) / (1. - f)
g_ext2E_AMPA = 2.1 # nS
g_ext2I_AMPA = 1.62 # nS
g_E2E_AMPA = 0.05 / scale # nS
g_E2I_AMPA = 0.04 / scale # nS
g_E2E_NMDA = 0.165 / scale # nS
g_E2I_NMDA = 0.13 / scale # nS
g_I2E_GABAa = 1.3 / scale # nS
g_I2I_GABAa = 1.0 / scale # nS
ampa_par = dict(delay_step=int(0.5 / bm.get_dt()), tau=2.0)
gaba_par = dict(delay_step=int(0.5 / bm.get_dt()), tau=5.0)
nmda_par = dict(delay_step=int(0.5 / bm.get_dt()), tau_decay=100, tau_rise=2., a=0.5)
# E neurons/pyramid neurons
A = bp.neurons.LIF(num_A, V_rest=-70., V_reset=-55., V_th=-50., tau=20., R=0.04,
tau_ref=2., V_initializer=bp.init.OneInit(-70.))
B = bp.neurons.LIF(num_B, V_rest=-70., V_reset=-55., V_th=-50., tau=20., R=0.04,
tau_ref=2., V_initializer=bp.init.OneInit(-70.))
N = bp.neurons.LIF(num_N, V_rest=-70., V_reset=-55., V_th=-50., tau=20., R=0.04,
tau_ref=2., V_initializer=bp.init.OneInit(-70.))
# I neurons/interneurons
I = bp.neurons.LIF(num_inh, V_rest=-70., V_reset=-55., V_th=-50., tau=10., R=0.05,
tau_ref=1., V_initializer=bp.init.OneInit(-70.))
# poisson stimulus
IA = PoissonStim(num_A, freq_var=10., t_interval=50., freq_mean=mu0 + mu0 / 100. * coherence)
IB = PoissonStim(num_B, freq_var=10., t_interval=50., freq_mean=mu0 - mu0 / 100. * coherence)
# noise neurons
self.noise_B = bp.neurons.PoissonGroup(num_B, freqs=poisson_freq)
self.noise_A = bp.neurons.PoissonGroup(num_A, freqs=poisson_freq)
self.noise_N = bp.neurons.PoissonGroup(num_N, freqs=poisson_freq)
self.noise_I = bp.neurons.PoissonGroup(num_inh, freqs=poisson_freq)
# define external inputs
self.IA2A = bp.synapses.Exponential(IA, A, bp.conn.One2One(), g_max=g_ext2E_AMPA,
output=bp.synouts.COBA(E=0.), **ampa_par)
self.IB2B = bp.synapses.Exponential(IB, B, bp.conn.One2One(), g_max=g_ext2E_AMPA,
output=bp.synouts.COBA(E=0.), **ampa_par)
# define E->E/I conn
self.N2B_AMPA = bp.synapses.Exponential(N, B, bp.conn.All2All(), g_max=g_E2E_AMPA * w_neg,
output=bp.synouts.COBA(E=0.), **ampa_par)
self.N2A_AMPA = bp.synapses.Exponential(N, A, bp.conn.All2All(), g_max=g_E2E_AMPA * w_neg,
output=bp.synouts.COBA(E=0.), **ampa_par)
self.N2N_AMPA = bp.synapses.Exponential(N, N, bp.conn.All2All(), g_max=g_E2E_AMPA,
output=bp.synouts.COBA(E=0.), **ampa_par)
self.N2I_AMPA = bp.synapses.Exponential(N, I, bp.conn.All2All(), g_max=g_E2I_AMPA,
output=bp.synouts.COBA(E=0.), **ampa_par)
self.N2B_NMDA = bp.synapses.NMDA(N, B, bp.conn.All2All(), g_max=g_E2E_NMDA * w_neg,
output=bp.synouts.MgBlock(E=0., cc_Mg=1.), **nmda_par)
self.N2A_NMDA = bp.synapses.NMDA(N, A, bp.conn.All2All(), g_max=g_E2E_NMDA * w_neg,
output=bp.synouts.MgBlock(E=0., cc_Mg=1.), **nmda_par)
self.N2N_NMDA = bp.synapses.NMDA(N, N, bp.conn.All2All(), g_max=g_E2E_NMDA,
output=bp.synouts.MgBlock(E=0., cc_Mg=1.), **nmda_par)
self.N2I_NMDA = bp.synapses.NMDA(N, I, bp.conn.All2All(), g_max=g_E2I_NMDA,
output=bp.synouts.MgBlock(E=0., cc_Mg=1.), **nmda_par)
self.B2B_AMPA = bp.synapses.Exponential(B, B, bp.conn.All2All(), g_max=g_E2E_AMPA * w_pos,
output=bp.synouts.COBA(E=0.), **ampa_par)
self.B2A_AMPA = bp.synapses.Exponential(B, A, bp.conn.All2All(), g_max=g_E2E_AMPA * w_neg,
output=bp.synouts.COBA(E=0.), **ampa_par)
self.B2N_AMPA = bp.synapses.Exponential(B, N, bp.conn.All2All(), g_max=g_E2E_AMPA,
output=bp.synouts.COBA(E=0.), **ampa_par)
self.B2I_AMPA = bp.synapses.Exponential(B, I, bp.conn.All2All(), g_max=g_E2I_AMPA,
output=bp.synouts.COBA(E=0.), **ampa_par)
self.B2B_NMDA = bp.synapses.NMDA(B, B, bp.conn.All2All(), g_max=g_E2E_NMDA * w_pos,
output=bp.synouts.MgBlock(E=0., cc_Mg=1.), **nmda_par)
self.B2A_NMDA = bp.synapses.NMDA(B, A, bp.conn.All2All(), g_max=g_E2E_NMDA * w_neg,
output=bp.synouts.MgBlock(E=0., cc_Mg=1.), **nmda_par)
self.B2N_NMDA = bp.synapses.NMDA(B, N, bp.conn.All2All(), g_max=g_E2E_NMDA,
output=bp.synouts.MgBlock(E=0., cc_Mg=1.), **nmda_par)
self.B2I_NMDA = bp.synapses.NMDA(B, I, bp.conn.All2All(), g_max=g_E2I_NMDA,
output=bp.synouts.MgBlock(E=0., cc_Mg=1.), **nmda_par)
self.A2B_AMPA = bp.synapses.Exponential(A, B, bp.conn.All2All(), g_max=g_E2E_AMPA * w_neg,
output=bp.synouts.COBA(E=0.), **ampa_par)
self.A2A_AMPA = bp.synapses.Exponential(A, A, bp.conn.All2All(), g_max=g_E2E_AMPA * w_pos,
output=bp.synouts.COBA(E=0.), **ampa_par)
self.A2N_AMPA = bp.synapses.Exponential(A, N, bp.conn.All2All(), g_max=g_E2E_AMPA,
output=bp.synouts.COBA(E=0.), **ampa_par)
self.A2I_AMPA = bp.synapses.Exponential(A, I, bp.conn.All2All(), g_max=g_E2I_AMPA,
output=bp.synouts.COBA(E=0.), **ampa_par)
self.A2B_NMDA = bp.synapses.NMDA(A, B, bp.conn.All2All(), g_max=g_E2E_NMDA * w_neg,
output=bp.synouts.MgBlock(E=0., cc_Mg=1.), **nmda_par)
self.A2A_NMDA = bp.synapses.NMDA(A, A, bp.conn.All2All(), g_max=g_E2E_NMDA * w_pos,
output=bp.synouts.MgBlock(E=0., cc_Mg=1.), **nmda_par)
self.A2N_NMDA = bp.synapses.NMDA(A, N, bp.conn.All2All(), g_max=g_E2E_NMDA,
output=bp.synouts.MgBlock(E=0., cc_Mg=1.), **nmda_par)
self.A2I_NMDA = bp.synapses.NMDA(A, I, bp.conn.All2All(), g_max=g_E2I_NMDA,
output=bp.synouts.MgBlock(E=0., cc_Mg=1.), **nmda_par)
# define I->E/I conn
self.I2B = bp.synapses.Exponential(I, B, bp.conn.All2All(), g_max=g_I2E_GABAa,
output=bp.synouts.COBA(E=-70.), **gaba_par)
self.I2A = bp.synapses.Exponential(I, A, bp.conn.All2All(), g_max=g_I2E_GABAa,
output=bp.synouts.COBA(E=-70.), **gaba_par)
self.I2N = bp.synapses.Exponential(I, N, bp.conn.All2All(), g_max=g_I2E_GABAa,
output=bp.synouts.COBA(E=-70.), **gaba_par)
self.I2I = bp.synapses.Exponential(I, I, bp.conn.All2All(), g_max=g_I2I_GABAa,
output=bp.synouts.COBA(E=-70.), **gaba_par)
# define external projections
self.noise2B = bp.synapses.Exponential(self.noise_B, B, bp.conn.One2One(), g_max=g_ext2E_AMPA,
output=bp.synouts.COBA(E=0.), **ampa_par)
self.noise2A = bp.synapses.Exponential(self.noise_A, A, bp.conn.One2One(), g_max=g_ext2E_AMPA,
output=bp.synouts.COBA(E=0.), **ampa_par)
self.noise2N = bp.synapses.Exponential(self.noise_N, N, bp.conn.One2One(), g_max=g_ext2E_AMPA,
output=bp.synouts.COBA(E=0.), **ampa_par)
self.noise2I = bp.synapses.Exponential(self.noise_I, I, bp.conn.One2One(), g_max=g_ext2I_AMPA,
output=bp.synouts.COBA(E=0.), **ampa_par)
# nodes
self.B = B
self.A = A
self.N = N
self.I = I
self.IA = IA
self.IB = IB
def visualize_raster(ax, mon, t_start=0., title=None):
bp.visualize.raster_plot(mon['ts'], mon['A.spike'], markersize=1, ax=ax, color='', label="Group A")
bp.visualize.raster_plot(mon['ts'], mon['B.spike'], markersize=1, ax=ax, color='', label="Group B")
if title:
ax.set_title(title)
ax.set_ylabel("Neuron Index")
ax.set_xlim(t_start, total_period + 1)
ax.axvline(pre_stimulus_period, linestyle='dashed')
ax.axvline(pre_stimulus_period + stimulus_period, linestyle='dashed')
ax.axvline(pre_stimulus_period + stimulus_period + delay_period, linestyle='dashed')
ax.legend()
def visualize_results(axes, mon, t_start=0., title=None):
ax = axes[0]
bp.visualize.raster_plot(mon['ts'], mon['A.spike'], markersize=1, ax=ax)
if title:
ax.set_title(title)
ax.set_ylabel("Group A")
ax.set_xlim(t_start, total_period + 1)
ax.axvline(pre_stimulus_period, linestyle='dashed')
ax.axvline(pre_stimulus_period + stimulus_period, linestyle='dashed')
ax.axvline(pre_stimulus_period + stimulus_period + delay_period, linestyle='dashed')
ax = axes[1]
bp.visualize.raster_plot(mon['ts'], mon['B.spike'], markersize=1, ax=ax)
ax.set_ylabel("Group B")
ax.set_xlim(t_start, total_period + 1)
ax.axvline(pre_stimulus_period, linestyle='dashed')
ax.axvline(pre_stimulus_period + stimulus_period, linestyle='dashed')
ax.axvline(pre_stimulus_period + stimulus_period + delay_period, linestyle='dashed')
ax = axes[2]
rateA = bp.measure.firing_rate(mon['A.spike'], width=10.)
rateB = bp.measure.firing_rate(mon['B.spike'], width=10.)
ax.plot(mon['ts'], rateA, label="Group A")
ax.plot(mon['ts'], rateB, label="Group B")
ax.set_ylabel('Population activity [Hz]')
ax.set_xlim(t_start, total_period + 1)
ax.axvline(pre_stimulus_period, linestyle='dashed')
ax.axvline(pre_stimulus_period + stimulus_period, linestyle='dashed')
ax.axvline(pre_stimulus_period + stimulus_period + delay_period, linestyle='dashed')
ax.legend()
ax = axes[3]
ax.plot(mon['ts'], mon['IA.freq'], label="group A")
ax.plot(mon['ts'], mon['IB.freq'], label="group B")
ax.set_ylabel("Input activity [Hz]")
ax.set_xlim(t_start, total_period + 1)
ax.axvline(pre_stimulus_period, linestyle='dashed')
ax.axvline(pre_stimulus_period + stimulus_period, linestyle='dashed')
ax.axvline(pre_stimulus_period + stimulus_period + delay_period, linestyle='dashed')
ax.legend()
ax.set_xlabel("Time [ms]")
pre_stimulus_period = 100.
stimulus_period = 1000.
delay_period = 500.
total_period = pre_stimulus_period + stimulus_period + delay_period
def single_run():
net = DecisionMaking(scale=1., coherence=-80., mu0=50.)
runner = bp.DSRunner(
net, monitors=['A.spike', 'B.spike', 'IA.freq', 'IB.freq']
)
runner.run(total_period)
fig, gs = bp.visualize.get_figure(4, 1, 3, 10)
axes = [fig.add_subplot(gs[i, 0]) for i in range(4)]
visualize_results(axes, mon=runner.mon)
plt.show()
def batching_run():
num_row, num_col = 3, 4
num_batch = 12
coherence = bm.expand_dims(bm.linspace(-100, 100., num_batch), 1)
with bm.environment(mode=bm.BatchingMode(batch_size=num_batch)):
net = DecisionMaking(scale=1., coherence=coherence, mu0=20.)
runner = bp.DSRunner(
net, monitors=['A.spike', 'B.spike', 'IA.freq', 'IB.freq'], data_first_axis='B'
)
runner.run(total_period)
coherence = bm.as_numpy(coherence)
fig, gs = bp.visualize.get_figure(num_row, num_col, 3, 4)
for i in range(num_row):
for j in range(num_col):
idx = i * num_col + j
if idx < num_batch:
mon = {'A.spike': runner.mon['A.spike'][idx],
'B.spike': runner.mon['B.spike'][idx],
'IA.freq': runner.mon['IA.freq'][idx],
'IB.freq': runner.mon['IB.freq'][idx],
'ts': runner.mon['ts']}
ax = fig.add_subplot(gs[i, j])
visualize_raster(ax, mon=mon, title=f'coherence={coherence[idx, 0]}%')
plt.show()
if __name__ == '__main__':
single_run()
batching_run()