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 question_external_poisson_population(): 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, w_profile = \ wm_model.simulate_wm( sim_time=800. * b2.ms, poisson_firing_rate=2.2 * b2.Hz, sigma_weight_profile=20., Jpos_excit2excit=1.6) plot_tools.plot_network_activity(rate_monitor_excit, spike_monitor_excit, voltage_monitor_excit, t_min=0. * b2.ms) plt.show()
def question_weight_profile(): 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_45 = \ wm_model.simulate_wm( sim_time=800. * b2.ms, poisson_firing_rate=2.3 * b2.Hz, sigma_weight_profile=5., Jpos_excit2excit=6) plot_tools.plot_network_activity(rate_monitor_excit, spike_monitor_excit, voltage_monitor_excit, t_min=0. * b2.ms) plt.show() plt.figure() plt.plot(weight_profile_45) 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(): """ 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 question_role_of_inhib_population(): 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, w_profile = \ wm_model.simulate_wm(N_excitatory=1024, N_inhibitory=1, sigma_weight_profile=20, stimulus_center_deg=120, stimulus_width_deg=30, stimulus_strength=0.5 * b2.namp, t_stimulus_start=100 * b2.ms, t_stimulus_duration=100 * b2.ms, sim_time=500. * b2.ms, Jpos_excit2excit=1.6, poisson_firing_rate=1.5 * b2.Hz) fig, ax_raster, ax_rate, ax_voltage = plot_tools.plot_network_activity( rate_monitor_excit, spike_monitor_excit, voltage_monitor_excit, t_min=0. * b2.ms) plt.show()
def distractor_at_same_time(): 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, w_profile = \ wm_model.simulate_wm(N_excitatory=1024, N_inhibitory=256, sigma_weight_profile=60, stimulus_center_deg=40, stimulus_width_deg=20, stimulus_strength=0.5 * b2.namp, t_stimulus_start=100 * b2.ms,G_excit2inhib=0.355*b2.nS, t_stimulus_duration=100 * b2.ms, sim_time=500. * b2.ms, Jpos_excit2excit=1.6, poisson_firing_rate=1.5 * b2.Hz, distractor_center_deg=300, distractor_width_deg=20, distractor_strength=0.5 * b2.namp, t_distractor_start=110 * b2.ms, t_distractor_duration=100 * b2.ms) fig, ax_raster, ax_rate, ax_voltage = plot_tools.plot_network_activity( rate_monitor_excit, spike_monitor_excit, voltage_monitor_excit, t_min=0. * b2.ms) plt.show()
def question_integration_of_input(): 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, w_profile = \ wm_model.simulate_wm( stimulus_center_deg=120, stimulus_width_deg=60, stimulus_strength=0.5 * b2.namp, t_stimulus_start=100 * b2.ms, t_stimulus_duration=200 * b2.ms, sim_time=500. * b2.ms) fig, ax_raster, ax_rate, ax_voltage = plot_tools.plot_network_activity( rate_monitor_excit, spike_monitor_excit, voltage_monitor_excit, t_min=0. * b2.ms) plt.show() 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, w_profile = \ wm_model.simulate_wm( stimulus_center_deg=120, stimulus_width_deg=30, stimulus_strength=0.5 * b2.namp, t_stimulus_start=100 * b2.ms, t_stimulus_duration=200 * b2.ms, sim_time=500. * b2.ms) fig, ax_raster, ax_rate, ax_voltage = plot_tools.plot_network_activity( rate_monitor_excit, spike_monitor_excit, voltage_monitor_excit, t_min=0. * b2.ms) 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()
def _some_example_calls_and_tests(): from neurodynex3.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")