def _some_example_calls_and_tests():
b2.defaultclock.dt = 0.2 * b2.ms
from neurodynex.tools import spike_tools
poisson_rate = 9.5*b2.Hz
g = 7.5
sim_time = 800. * b2.ms
CE = 500
sampling_frequency_upper_bound = 800*b2.Hz
rate_monitor, spike_monitor, voltage_monitor, monitored_spike_idx = simulate_brunel_network(
N_Excit=CE, poisson_input_rate=poisson_rate, g=g, sim_time=sim_time)
# plot_tools.plot_network_activity(
# rate_monitor, spike_monitor, voltage_monitor, monitored_spike_idx, t_min=0*b2.ms)
# plot_tools.plot_network_activity(
# rate_monitor, spike_monitor, voltage_monitor, monitored_spike_idx, t_min=sim_time-100*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=50, xlim_max_ISI=50. * b2.ms)
# print(spike_stats.CV)
pop_freqs, pop_ps, downsampling_factor, nyquist_frequency = spike_tools.get_population_activity_power_spectrum(
rate_monitor, sampling_frequency_upper_bound=sampling_frequency_upper_bound, window_t_min=100.*b2.ms)
plot_tools.plot_population_activity_power_spectrum(pop_freqs, pop_ps, nyquist_frequency)
plt.show()
freq, mean_ps, all_ps, nyquist_frequency = spike_tools.get_average_power_spectrum(
spike_monitor, sampling_frequency=sampling_frequency_upper_bound, window_t_min=100.*b2.ms, window_t_max=None)
plot_tools.plot_spike_train_power_spectrum(freq, mean_ps, all_ps, nyquist_frequency)
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")