neurodynex.brunel_model package


neurodynex.brunel_model.LIF_spiking_network module

Implementation of the Brunel 2000 network: sparsely connected network of identical LIF neurons (Model A).


A simple example to get started

neurodynex.brunel_model.LIF_spiking_network.simulate_brunel_network(N_Excit=5000, N_Inhib=None, N_extern=1000, connection_probability=0.1, w0=100. * uvolt, g=4.0, synaptic_delay=1.5 * msecond, poisson_input_rate=13. * hertz, w_external=None, v_rest=0. * volt, v_reset=10. * mvolt, firing_threshold=20. * mvolt, membrane_time_scale=20. * msecond, abs_refractory_period=2. * msecond, monitored_subset_size=100, random_vm_init=False, sim_time=100. * msecond)[source]

Fully parametrized implementation of a sparsely connected network of LIF neurons (Brunel 2000)

  • N_Excit (int) – Size of the excitatory popluation
  • N_Inhib (int) – optional. Size of the inhibitory population. If not set (=None), N_Inhib is set to N_excit/4.
  • N_extern (int) – optional. Number of presynaptic excitatory poisson neurons. Note: if set to a value, this number does NOT depend on N_Excit and NOT depend on connection_probability (this is different from the book and paper. Only if N_extern is set to ‘None’, then N_extern is computed as N_Excit*connection_probability.
  • connection_probability (float) – probability to connect to any of the (N_Excit+N_Inhib) neurons CE = connection_probability*N_Excit CI = connection_probability*N_Inhib Cexternal = N_extern
  • w0 (float) – Synaptic strength J
  • g (float) – relative importance of inhibition. J_exc = w0. J_inhib = -g*w0
  • synaptic_delay (Quantity) – Delay between presynaptic spike and postsynaptic increase of v_m
  • poisson_input_rate (Quantity) – Poisson rate of the external population
  • w_external (float) – optional. Synaptic weight of the excitatory external poisson neurons onto all neurons in the network. Default is None, in that case w_external is set to w0, which is the standard value in the book and in the paper Brunel2000. The purpose of this parameter is to see the effect of external input in the absence of network feedback(setting w0 to 0mV and w_external>0).
  • v_rest (Quantity) – Resting potential
  • v_reset (Quantity) – Reset potential
  • firing_threshold (Quantity) – Spike threshold
  • membrane_time_scale (Quantity) – tau_m
  • abs_refractory_period (Quantity) – absolute refractory period, tau_ref
  • monitored_subset_size (int) – nr of neurons for which a VoltageMonitor is recording Vm
  • random_vm_init (bool) – if true, the membrane voltage of each neuron is initialized with a random value drawn from Uniform(v_rest, firing_threshold)
  • sim_time (Quantity) – Simulation time

(rate_monitor, spike_monitor, voltage_monitor, idx_monitored_neurons) PopulationRateMonitor: Rate Monitor SpikeMonitor: SpikeMonitor for ALL (N_Excit+N_Inhib) neurons StateMonitor: membrane voltage for a selected subset of neurons list: index of monitored neurons. length = monitored_subset_size

Module contents