def getting_started():
rate_monitor, spike_monitor, trace_monitor, phase_monitor, monitored_spike_idx = simulate_AdEx_network(N_Excit=1000)
plot_tools.plot_network_activity(rate_monitor, spike_monitor, trace_monitor, spike_train_idx_list=monitored_spike_idx,
t_min=0.*b2.ms)
plt.show()
plot_adex_state(phase_monitor)
def getting_started():
"""
A simple example to get started.
Returns:
"""
stim_start = 150. * b2.ms
stim_duration = 350 * b2.ms
print("stimulus start: {}, stimulus end: {}".format(
stim_start, stim_start + stim_duration))
results = sim_decision_making_network(N_Excit=341,
N_Inhib=85,
weight_scaling_factor=6.0,
t_stimulus_start=stim_start,
t_stimulus_duration=stim_duration,
coherence_level=+0.90,
w_pos=2.0,
mu0_mean_stimulus_Hz=500 * b2.Hz,
max_sim_time=800. * b2.ms)
plot_tools.plot_network_activity(results["rate_monitor_A"],
results["spike_monitor_A"],
results["voltage_monitor_A"],
t_min=0. * b2.ms,
avg_window_width=20. * b2.ms,
sup_title="Left")
plot_tools.plot_network_activity(results["rate_monitor_B"],
results["spike_monitor_B"],
results["voltage_monitor_B"],
t_min=0. * b2.ms,
avg_window_width=20. * b2.ms,
sup_title="Right")
plt.show()
def getting_started():
rate_monitor, spike_monitor, trace_monitor, monitored_spike_idx = simulate_lif_network(
N_Excit=2000, external_input=False)
plot_tools.plot_network_activity(rate_monitor,
spike_monitor,
trace_monitor,
spike_train_idx_list=monitored_spike_idx,
t_min=0. * b2.ms)
plt.show()
def getting_started():
"""
A simple example to get started
"""
rate_monitor, spike_monitor, voltage_monitor, monitored_spike_idx = simulate_brunel_network(
N_Excit=200, sim_time=400. * b2.ms)
plot_tools.plot_network_activity(rate_monitor, spike_monitor, voltage_monitor,
spike_train_idx_list=monitored_spike_idx, t_min=0.*b2.ms,
N_highlighted_spiketrains=3)
plt.show()
def getting_started():
"""
A simple example to get started
"""
rate_monitor, spike_monitor, voltage_monitor, monitored_spike_idx = simulate_brunel_network(
N_Excit=2000, sim_time=800. * b2.ms)
plot_tools.plot_network_activity(rate_monitor, spike_monitor, voltage_monitor,
spike_train_idx_list=monitored_spike_idx, t_min=0.*b2.ms,
N_highlighted_spiketrains=3, avg_window_width=1. * b2.ms)
plt.show()
def getting_started():
b2.defaultclock.dt = 0.1 * b2.ms
rate_monitor_excit, spike_monitor_excit, voltage_monitor_excit, idx_monitored_neurons_excit,\
rate_monitor_inhib, spike_monitor_inhib, voltage_monitor_inhib, idx_monitored_neurons_inhib,\
weight_profile\
= simulate_wm(N_excitatory=256, N_inhibitory=64, weight_scaling_factor=8., sim_time=500. * b2.ms,
stimulus_center_deg=120, t_stimulus_start=100 * b2.ms, t_stimulus_duration=200 * b2.ms,
stimulus_strength=.07 * b2.namp)
plot_tools.plot_network_activity(rate_monitor_excit, spike_monitor_excit, voltage_monitor_excit,
t_min=0. * b2.ms)
plt.show()
def getting_started():
b2.defaultclock.dt = 0.1 * b2.ms
rate_monitor_excit, spike_monitor_excit, voltage_monitor_excit, idx_monitored_neurons_excit,\
rate_monitor_inhib, spike_monitor_inhib, voltage_monitor_inhib, idx_monitored_neurons_inhib,\
weight_profile\
= simulate_wm(N_excitatory=256, N_inhibitory=64, weight_scaling_factor=8., sim_time=500. * b2.ms,
stimulus_center_deg=120, t_stimulus_start=100 * b2.ms, t_stimulus_duration=200 * b2.ms,
stimulus_strength=.07 * b2.namp)
plot_tools.plot_network_activity(rate_monitor_excit, spike_monitor_excit, voltage_monitor_excit,
t_min=0. * b2.ms)
plt.show()
def _some_example_calls_and_tests():
from neurodynex.tools import spike_tools
poisson_rate = 35*b2.Hz
g = 4
CE = 5000
delta_t = 0.1 * b2.ms
delta_f = 5. * b2.Hz
T_init = 100 * b2.ms
k = 9
f_max = 1./(2. * delta_t)
N_samples = 2. * f_max / delta_f
T_signal = N_samples * delta_t
T_sim = k * T_signal + T_init
print("Start simulation. T_sim={}, T_signal={}. N_samples={}".format(T_sim, T_signal, N_samples))
b2.defaultclock.dt = delta_t
stime = T_sim + (10 + k) * b2.defaultclock.dt # add a few extra samples (solves rounding issues)
rate_monitor, spike_monitor, voltage_monitor, monitored_spike_idx = \
simulate_brunel_network(
N_Excit=CE, poisson_input_rate=poisson_rate, g=g, sim_time=stime)
plot_tools.plot_network_activity(rate_monitor, spike_monitor, voltage_monitor,
spike_train_idx_list=monitored_spike_idx, t_min=0*b2.ms)
plot_tools.plot_network_activity(rate_monitor, spike_monitor, voltage_monitor,
spike_train_idx_list=monitored_spike_idx, t_min=T_sim - 80*b2.ms)
spike_stats = spike_tools.get_spike_train_stats(spike_monitor, window_t_min=150.*b2.ms)
plot_tools.plot_ISI_distribution(spike_stats, hist_nr_bins=77, xlim_max_ISI=100*b2.ms)
# # Power Spectrum
pop_freqs, pop_ps, average_population_rate = \
spike_tools.get_population_activity_power_spectrum(
rate_monitor, delta_f, k, T_init, subtract_mean_activity=True)
plot_tools.plot_population_activity_power_spectrum(pop_freqs, pop_ps, 1000*b2.Hz, average_population_rate)
plt.show()
freq, mean_ps, all_ps, mean_firing_rate, all_mean_firing_freqs = \
spike_tools.get_averaged_single_neuron_power_spectrum(
spike_monitor, sampling_frequency=1./delta_t, window_t_min=100.*b2.ms,
window_t_max=T_sim, subtract_mean=False, nr_neurons_average=200)
print("plot_spike_train_power_spectrum")
plot_tools.plot_spike_train_power_spectrum(freq, mean_ps, all_ps, 1000 * b2.Hz,
mean_firing_freqs_per_neuron=all_mean_firing_freqs,
nr_highlighted_neurons=2)
plt.show()
print("done")
def getting_started():
"""
A simple example to get started.
Returns:
"""
stim_start = 150. * b2.ms
stim_duration = 350 * b2.ms
print("stimulus start: {}, stimulus end: {}".format(stim_start, stim_start+stim_duration))
results = sim_decision_making_network(N_Excit=341, N_Inhib=85, weight_scaling_factor=6.0,
t_stimulus_start=stim_start, t_stimulus_duration=stim_duration,
coherence_level=+0.90, w_pos=2.0, mu0_mean_stimulus_Hz=500 * b2.Hz,
max_sim_time=800. * b2.ms)
plot_tools.plot_network_activity(results["rate_monitor_A"], results["spike_monitor_A"],
results["voltage_monitor_A"], t_min=0. * b2.ms, avg_window_width=20. * b2.ms,
sup_title="Left")
plot_tools.plot_network_activity(results["rate_monitor_B"], results["spike_monitor_B"],
results["voltage_monitor_B"], t_min=0. * b2.ms, avg_window_width=20. * b2.ms,
sup_title="Right")
plt.show()
def _demo_emergence_of_oscillation():
poisson_rate = 18 * b2.Hz
g = 2.5
rate_monitor, spike_monitor, voltage_monitor, monitored_spike_idx = \
simulate_brunel_network(N_Excit=6000, random_vm_init=True, poisson_input_rate=poisson_rate,
g=g, sim_time=300. * b2.ms, monitored_subset_size=50)
plot_tools.plot_network_activity(rate_monitor, spike_monitor, voltage_monitor,
spike_train_idx_list=monitored_spike_idx, t_min=0*b2.ms)
plot_tools.plot_network_activity(rate_monitor, spike_monitor, voltage_monitor,
spike_train_idx_list=monitored_spike_idx, t_max=50*b2.ms)
plot_tools.plot_network_activity(rate_monitor, spike_monitor, voltage_monitor,
spike_train_idx_list=monitored_spike_idx, t_min=250*b2.ms)
plt.show()
V_REST = 0. * b2.mV
V_RESET = +10. * b2.mV
FIRING_THRESHOLD = +20. * b2.mV
MEMBRANE_TIME_SCALE = 20. * b2.ms
ABSOLUTE_REFRACTORY_PERIOD = 2.0 * b2.ms
# Default parameters of the network:
SYNAPTIC_WEIGHT_W0 = 0.1 * b2.mV # note: w_ee=w_ie = w0 and = w_ei=w_ii = -g*w0
RELATIVE_INHIBITORY_STRENGTH_G = 4. # balanced
CONNECTION_PROBABILITY_EPSILON = 0.1
SYNAPTIC_DELAY = 1.5 * b2.ms
POISSON_INPUT_RATE = 12. * b2.Hz
N_POISSON_INPUT = 1000
"""
denom = LIF_spiking_network.N_POISSON_INPUT * LIF_spiking_network.SYNAPTIC_WEIGHT_W0 * LIF_spiking_network.MEMBRANE_TIME_SCALE
ni_thresh = LIF_spiking_network.FIRING_THRESHOLD/(denom)
print("La frequenza di soglia della rete poissoniana di input sufficiente a portare i neuroni in uno stato di firing è {} Hz".format(ni_thresh))
T = 500*b2.ms
rate_monitor, spike_monitor, voltage_monitor, monitored_spike_idx = LIF_spiking_network.simulate_brunel_network(poisson_input_rate=ni_thresh, sim_time=T)
plot_tools.plot_network_activity(rate_monitor, spike_monitor, voltage_monitor, spike_train_idx_list=monitored_spike_idx, t_min=0.*b2.ms)
avg_fi_rate_sn = spike_monitor.num_spikes / (T * spike_monitor.source.N)
print("Il firing rate per singolo neurone del netowork è {} Hz".format(avg_fi_rate_sn))
plt.show()
