def test_simulation_theory(self):
reset_brian2()
t = 3000 * ms
dt = 0.01 * ms
n = 100
poisson = MFPoissonPopulation(n, n * 10 * Hz)
pop = MFLinearPopulation(
n, {
PP.GM: 10 * nsiemens,
PP.VL: 0 * mV,
PP.CM: 5 * nfarad,
PP.VTHR: 0 * mV,
PP.VRES: 0 * mV,
PP.TAU_RP: 15 * ms
})
syn = MFLinearNMDAInput(
poisson, pop, {
IP.GM: 10 * nsiemens,
IP.VREV: 0 * mV,
IP.TAU: 20 * ms,
IP.TAU_NMDA: 30 * ms,
IP.BETA: 1,
IP.GAMMA: 1,
})
system = MFSystem(pop, poisson)
solver = MFSolver.rates_voltages(system, solver='mse')
solver.run()
theory = syn.g_dyn() / syn.origin.n
m = StateMonitor(syn.brian2, syn.post_variable_name, record=range(100))
defaultclock.dt = dt
net = system.collect_brian2_network(m)
net.run(t)
stable_t = int(t / dt * 0.1)
simulation = m.__getattr__(syn.post_variable_name)[:, stable_t:]
simulation_mean = np.mean(simulation)
print(simulation)
assert np.isclose(theory, simulation_mean, rtol=0.5, atol=0.5)
def test_simulation_theory(self):
reset_brian2()
t = 3000 * ms
dt = 0.01 * ms
n = 100
alpha = 0.5
poisson = MFPoissonPopulation(n, n * 10 * Hz)
pop = MFLinearPopulation(
n, {
PP.GM: 10 * nsiemens,
PP.VL: 0 * mV,
PP.CM: 5 * nfarad,
PP.VTHR: 0 * mV,
PP.VRES: 0 * mV,
PP.TAU_RP: 15 * ms
})
syn = MFLinear3TSInput(
poisson, pop, {
IP.GM: 10 * nsiemens,
IP.VREV: 0 * mvolt,
IP.TAU: 0 * ms,
IP.TAU_RISE: 2 * ms,
IP.TAU_D1: 20 * ms,
IP.TAU_D2: 30 * ms,
IP.ALPHA: alpha
})
system = MFSystem(pop, poisson)
solver = MFSolver.rates_voltages(system, solver='mse')
solver.run()
theory = syn.g_dyn() / syn.origin.n
print(syn.brian2)
m1 = StateMonitor(syn.brian2,
syn.post_variable_name[0],
record=range(100))
m2 = StateMonitor(syn.brian2,
syn.post_variable_name[1],
record=range(100))
m3 = StateMonitor(syn.brian2,
syn.post_variable_name[2],
record=range(100))
m4 = StateMonitor(syn.brian2,
syn.post_variable_name[3],
record=range(100))
defaultclock.dt = dt
net = system.collect_brian2_network(m1, m2, m3, m4)
net.run(t)
stable_t = int(t / dt * 0.1)
simulation_1 = m1.__getattr__(syn.post_variable_name[0])[:, stable_t:]
simulation_mean_1 = np.mean(simulation_1)
simulation_2 = m2.__getattr__(syn.post_variable_name[1])[:, stable_t:]
simulation_mean_2 = np.mean(simulation_2)
simulation_3 = m3.__getattr__(syn.post_variable_name[2])[:, stable_t:]
simulation_mean_3 = np.mean(simulation_3)
simulation_4 = m4.__getattr__(syn.post_variable_name[3])[:, stable_t:]
simulation_mean_4 = np.mean(simulation_4)
simulation_mean = alpha * simulation_mean_1 + (
1 - alpha) * simulation_mean_2 + -alpha * simulation_mean_3 - (
1 - alpha) * simulation_mean_4
assert np.isclose(theory, simulation_mean, rtol=0.5, atol=0.5)
