neurodynex3.working_memory_network package


neurodynex3.working_memory_network.wm_model module

Implementation of a working memory model. Literature: Compte, A., Brunel, N., Goldman-Rakic, P. S., & Wang, X. J. (2000). Synaptic mechanisms and network dynamics underlying spatial working memory in a cortical network model. Cerebral Cortex, 10(9), 910-923.

Some parts of this implementation are inspired by material from Stanford University, BIOE 332: Large-Scale Neural Modeling, Kwabena Boahen & Tatiana Engel, 2013, online available.

neurodynex3.working_memory_network.wm_model.simulate_wm(N_excitatory=1024, N_inhibitory=256, N_extern_poisson=1000, poisson_firing_rate=1.4 * hertz, weight_scaling_factor=2.0, sigma_weight_profile=20.0, Jpos_excit2excit=1.6, stimulus_center_deg=180, stimulus_width_deg=40, stimulus_strength=70. * pamp, t_stimulus_start=0. * second, t_stimulus_duration=0. * second, distractor_center_deg=90, distractor_width_deg=40, distractor_strength=0. * amp, t_distractor_start=0. * second, t_distractor_duration=0. * second, G_inhib2inhib=1.024 * nsiemens, G_inhib2excit=1.336 * nsiemens, G_excit2excit=0.381 * nsiemens, G_excit2inhib=292. * psiemens, monitored_subset_size=1024, sim_time=0.8 * second)[source]
  • N_excitatory (int) – Size of the excitatory population
  • N_inhibitory (int) – Size of the inhibitory population
  • weight_scaling_factor (float) – weight prefactor. When increasing the size of the populations, the synaptic weights have to be decreased. Using the default values, we have N_excitatory*weight_scaling_factor = 2048 and N_inhibitory*weight_scaling_factor=512
  • N_extern_poisson (int) – Size of the external input population (Poisson input)
  • poisson_firing_rate (Quantity) – Firing rate of the external population
  • sigma_weight_profile (float) – standard deviation of the gaussian input profile in the excitatory population.
  • Jpos_excit2excit (float) – Strength of the recurrent input within the excitatory population. Jneg_excit2excit is computed from sigma_weight_profile, Jpos_excit2excit and the normalization condition.
  • stimulus_center_deg (float) – Center of the stimulus in [0, 360]
  • stimulus_width_deg (float) – width of the stimulus. All neurons in stimulus_center_deg +- (stimulus_width_deg/2) receive the same input current
  • stimulus_strength (Quantity) – Input current to the neurons at stimulus_center_deg +- (stimulus_width_deg/2)
  • t_stimulus_start (Quantity) – time when the input stimulus is turned on
  • t_stimulus_duration (Quantity) – duration of the stimulus.
  • distractor_center_deg (float) – Center of the distractor in [0, 360]
  • distractor_width_deg (float) – width of the distractor. All neurons in distractor_center_deg +- (distractor_width_deg/2) receive the same input current distractor_strength (Quantity): Input current to the neurons at distractor_center_deg +- (distractor_width_deg/2)
  • t_distractor_start (Quantity) – time when the distractor is turned on
  • t_distractor_duration (Quantity) – duration of the distractor.
  • G_inhib2inhib (Quantity) – projections from inhibitory to inhibitory population (later rescaled by weight_scaling_factor)
  • G_inhib2excit (Quantity) – projections from inhibitory to excitatory population (later rescaled by weight_scaling_factor)
  • G_excit2excit (Quantity) – projections from excitatory to excitatory population (later rescaled by weight_scaling_factor)
  • G_excit2inhib (Quantity) – projections from excitatory to inhibitory population (later rescaled by weight_scaling_factor)
  • monitored_subset_size (int) – nr of neurons for which a Spike- and Voltage monitor is registered.
  • sim_time (Quantity) – simulation time

rate_monitor_excit (Brian2 PopulationRateMonitor for the excitatory population),

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 (The weights profile for the neuron with preferred direction = 45deg).

Return type:

results (tuple)

Module contents