def test_store_restore_magic():
source = NeuronGroup(10, '''dv/dt = rates : 1
rates : Hz''', threshold='v>1', reset='v=0')
source.rates = 'i*100*Hz'
target = NeuronGroup(10, 'v:1')
synapses = Synapses(source, target, model='w:1', pre='v+=w', connect='i==j')
synapses.w = 'i*1.0'
synapses.delay = 'i*ms'
state_mon = StateMonitor(target, 'v', record=True)
spike_mon = SpikeMonitor(source)
store() # default time slot
run(10*ms)
store('second')
run(10*ms)
v_values = state_mon.v[:, :]
spike_indices, spike_times = spike_mon.it_
restore() # Go back to beginning
assert magic_network.t == 0*ms
run(20*ms)
assert defaultclock.t == 20*ms
assert_equal(v_values, state_mon.v[:, :])
assert_equal(spike_indices, spike_mon.i[:])
assert_equal(spike_times, spike_mon.t_[:])
# Go back to middle
restore('second')
assert magic_network.t == 10*ms
run(10*ms)
assert defaultclock.t == 20*ms
assert_equal(v_values, state_mon.v[:, :])
assert_equal(spike_indices, spike_mon.i[:])
assert_equal(spike_times, spike_mon.t_[:])
def test_store_restore_magic_to_file():
filename = tempfile.mktemp(suffix='state', prefix='brian_test')
source = NeuronGroup(10,
'''dv/dt = rates : 1
rates : Hz''',
threshold='v>1',
reset='v=0')
source.rates = 'i*100*Hz'
target = NeuronGroup(10, 'v:1')
synapses = Synapses(source,
target,
model='w:1',
pre='v+=w',
connect='i==j')
synapses.w = 'i*1.0'
synapses.delay = 'i*ms'
state_mon = StateMonitor(target, 'v', record=True)
spike_mon = SpikeMonitor(source)
store(filename=filename) # default time slot
run(10 * ms)
store('second', filename=filename)
run(10 * ms)
v_values = state_mon.v[:, :]
spike_indices, spike_times = spike_mon.it_
restore(filename=filename) # Go back to beginning
assert magic_network.t == 0 * ms
run(20 * ms)
assert defaultclock.t == 20 * ms
assert_equal(v_values, state_mon.v[:, :])
assert_equal(spike_indices, spike_mon.i[:])
assert_equal(spike_times, spike_mon.t_[:])
# Go back to middle
restore('second', filename=filename)
assert magic_network.t == 10 * ms
run(10 * ms)
assert defaultclock.t == 20 * ms
assert_equal(v_values, state_mon.v[:, :])
assert_equal(spike_indices, spike_mon.i[:])
assert_equal(spike_times, spike_mon.t_[:])
try:
os.remove(filename)
except OSError:
pass
def test_filterbankgroup_restart():
sound1 = tone(1 * kHz, .1 * second)
sound2 = whitenoise(.1 * second)
sound = sound1 + sound2
sound = sound.ramp()
cf = erbspace(20 * Hz, 20 * kHz, 3000)
cochlea = Gammatone(sound, cf)
# Half-wave rectification and compression [x]^(1/3)
ihc = FunctionFilterbank(cochlea, lambda x: 3 * np.clip(x, 0, np.inf) ** (1.0 / 3.0))
# Leaky integrate-and-fire model with noise and refractoriness
eqs = '''
dv/dt = (I-v)/(1*ms)+0.2*xi*(2/(1*ms))**.5 : 1 (unless refractory)
I : 1
'''
anf = FilterbankGroup(ihc, 'I', eqs, reset='v=0', threshold='v>1', refractory=5*ms, method='euler')
M = SpikeMonitor(anf)
store()
run(sound.duration)
restore()
run(sound.duration)
def test_store_restore_magic_to_file():
filename = tempfile.mktemp(suffix='state', prefix='brian_test')
source = NeuronGroup(10, '''dv/dt = rates : 1
rates : Hz''', threshold='v>1', reset='v=0')
source.rates = 'i*100*Hz'
target = NeuronGroup(10, 'v:1')
synapses = Synapses(source, target, model='w:1', on_pre='v+=w')
synapses.connect(j='i')
synapses.w = 'i*1.0'
synapses.delay = 'i*ms'
state_mon = StateMonitor(target, 'v', record=True)
spike_mon = SpikeMonitor(source)
store(filename=filename) # default time slot
run(10*ms)
store('second', filename=filename)
run(10*ms)
v_values = state_mon.v[:, :]
spike_indices, spike_times = spike_mon.it_
restore(filename=filename) # Go back to beginning
assert magic_network.t == 0*ms
run(20*ms)
assert defaultclock.t == 20*ms
assert_equal(v_values, state_mon.v[:, :])
assert_equal(spike_indices, spike_mon.i[:])
assert_equal(spike_times, spike_mon.t_[:])
# Go back to middle
restore('second', filename=filename)
assert magic_network.t == 10*ms
run(10*ms)
assert defaultclock.t == 20*ms
assert_equal(v_values, state_mon.v[:, :])
assert_equal(spike_indices, spike_mon.i[:])
assert_equal(spike_times, spike_mon.t_[:])
try:
os.remove(filename)
except OSError:
pass
def main2(plot=True):
# Optimizing the code from main1 by only making the networks just once before the loop
# running multiple separate simulations
bs.start_scope()
num_inputs = 100
input_rate = 10 * bs.Hz
weight = 0.1
# range of time constants
tau_range = bs.linspace(start=1, stop=10, num=30) * bs.ms
# storing output rates
output_rates = []
P = bs.PoissonGroup(num_inputs, rates=input_rate)
eqs = """
dv/dt = -v/tau : 1
"""
G = bs.NeuronGroup(1, eqs, threshold='v>1', reset='v=0', method='exact')
# connect the external spike source with the neurons
# instead of connecting the same neurons like in previous examples
S = bs.Synapses(P, G, on_pre='v += weight')
S.connect()
# visualise_connectivity(S)
spike_monitor = bs.SpikeMonitor(source=G)
bs.store() # stores the current state of the network
for tau in tau_range:
# calling this allows for resetting of the network to its original state
bs.restore()
bs.run(1 * bs.second)
n_spikes = spike_monitor.num_spikes
output_rates.append(n_spikes / bs.second)
if plot:
plt.clf()
plt.plot(tau_range / bs.ms, output_rates)
plt.xlabel(r'$\tau$ (ms)')
plt.ylabel('Firing Rate (spikes/s)')
plt.show()
def simulation1():
bs.start_scope()
rates = [10, 20, 30]
bs.store()
train1 = generate_gaussian_spike_train(mean=2.5, std=0.5)
train2 = generate_gaussian_spike_train(mean=3, std=1.2)
# exit(0)
train_len = train1.shape[0]
# train_len = 10
total_time = None
total_spikes = None
for i in range(train_len):
r1 = train1[i]
r2 = train2[i]
place_cell = bs.NeuronGroup(1,
model=lif,
reset=reset_eq,
threshold=threshold_eq,
method="euler")
place_cell.tau_m = TAU_M
place_cell.tau_s = TAU_S
place_cell.v = -80.0 * bs.mV
print(f"Rates: {r1, r2}")
# bs.restore()
input = bs.PoissonGroup(2, rates=np.array([r1, r2]) * bs.Hz)
# connect input poisson spike generator to the input cells (grid and boundary vector)
S1 = bs.Synapses(input, place_cell, on_pre=synaptic_update)
S1.connect = S1.connect(i=[0, 1], j=0)
step_per_time = 100
place_cell_v_monitor = bs.StateMonitor(place_cell,
'v',
record=True,
dt=(dt / step_per_time) *
bs.second)
place_cell_monitor = bs.SpikeMonitor(source=place_cell)
bs.run(dt * bs.second)
spikes_i = place_cell_monitor.i
spikes_t = place_cell_monitor.t
print(spikes_i)
print(spikes_t)
if total_spikes is None:
total_spikes = spikes_t / bs.ms
else:
total_spikes = np.concatenate(
[total_spikes, (i * step_per_time) + spikes_t / bs.ms])
print("time", place_cell_v_monitor.t / bs.ms)
if total_time is None:
total_time = place_cell_v_monitor.t / bs.ms
else:
total_time = np.concatenate([
total_time,
(i * step_per_time) + place_cell_v_monitor.t / bs.ms
])
total_time = interp_based(total_time, N=10)
print(type(total_time))
print(total_time.shape)
print(total_time)
print(total_spikes)
plt.figure()
_, ind, _ = np.intersect1d(total_time,
total_spikes,
assume_unique=True,
return_indices=True)
spikes = np.zeros_like(total_time)
spikes[ind] = 1
plt.plot(total_time, spikes)
plt.xlabel('Time (ms)')
plt.ylabel('v')
plt.title(f"Spikes")
plt.show()
spikesgpi = b2.SpikeMonitor(GPI)
spikemonitors = [
spikesd1, spikesd2, spikesfsn, spikesgpi, spikesgpta, spikesgpti, spikesstn
]
nuclei = ['D1', 'D2', 'FSN', 'GPi', 'GPeTA', 'GPeTI', 'STN']
firingratesd1 = []
firingratesd2 = []
firingratesfsn = []
firingratesgpi = []
firingratesgpta = []
firingratesgpti = []
firingratesstn = []
b2.store()
c_var = np.arange(0., 0.11, 0.01)
# c_var = [0.]
# file_path = os.path.join(os.environ['USERPROFILE'], 'Desktop', 'frandff.txt')
file_path = '/home/f_mastellone/frandff.txt'
text_file = open(file_path, "w")
for i, c_i in enumerate(c_var):
b2.restore()
c = c_i
for spikemonitor in spikemonitors:
spikemonitor.active = False
b2.run(300 * b2.ms)
import brian2 as b
import matplotlib.pyplot as plt
import numpy as np
print("\nLibraries loaded.")
b.start_scope()
time_per_neuron = 400 * b.ms
n_times = 100
n_neurons = 2
freq = 50 * b.Hz
P = b.PoissonGroup(1, freq)
M = b.SpikeMonitor(P)
b.store()
print("Simulating-1...", end='', flush=True)
spike_times = []
spike_indices = []
start_time = 0
for k in range(n_times):
for n in range(n_neurons):
b.restore()
b.run(time_per_neuron)
percentage = 100.0 * (k * n_neurons + n) / (n_times * n_neurons + 0.0)
print("\rSimulating-1... t={}%".format(round(percentage, 2)),
end='',
flush=True)
spike_times += [a + start_time for a in list(M.t)]
spike_indices += [n] * M.num_spikes