Benchmark 3 of the simulator review (HH-COBA)

---

Run this example as a Jupyter notebook:

JupyterHub service

See our guide for more information and troubleshooting.

---

This script creates a sparsely coupled network of excitatory and inhibitory neurons which exhibits self-sustained activity after an initial stimulus. Connections within and across both populations are created at random. Both neuron populations receive Poissonian background input. The spike output of 500 neurons from each population are recorded. Neurons are modeled as Hodgkin-Huxley neurons with conductance-based synapses (exponential functions). The model is based on the Vogels & Abbott network model [1].

This is Benchmark 3 of the FACETS simulator review (Brette et al., 2007) [2]: - Neuron model: Hodgkin-Huxley (hh_cond_exp_traub) - Synapse model: conductance-based (COBA) - Synapse time course: exponential - Spike times: grid-constrained

References

[1]

Vogels TP, Abbott LF. 2005. Signal propagation and logic gating in networks of integrate-and-fire neurons. Journal of Neuroscience. 25(46):10786-10795. https://doi.org/10.1523/JNEUROSCI.3508-05.2005

[2]

Brette R, Rudolph M, Carnevale T, Hines M, Beeman D, Bower JM, et al. 2007. Simulation of networks of spiking neurons: a review of tools and strategies. Journal of Computational Neuroscience. 23(3):349-398. https://doi.org/10.1007/s10827-007-0038-6

import nest
from brette_et_al_2007_benchmark import run_simulation

Set benchmark parameters

params = {
    "model": "hh_cond_exp_traub",  # neuron model
    "model_params": {
        "g_Na": 20000.0,  # sodium conductance [nS]
        "g_K": 6000.0,  # K conductance [nS]
        "g_L": 10.0,  # leak conductance [nS]
        "C_m": 200.0,  # membrane capacitance [pF]
        "E_Na": 50.0,  # reversal potential (Sodium) [mV]
        "E_K": -90.0,  # reversal potential (Potassium) [mV]
        "E_L": -60.0,  # resting potential [mV]
        "E_ex": 0.0,  # excitatory reversal potential [mV]
        "E_in": -80.0,  # inhibitory reversal potential [mV]
        "tau_syn_ex": 5.0,  # excitatory synaptic time constant [ms]
        "tau_syn_in": 10.0,  # inhibitory synaptic time constant [ms]
    },
    "delay": 0.1,  # synaptic delay [ms]
    "E_synapse_params": {
        "weight": 6.0,  # excitatory synaptic conductance [nS]
    },
    "I_synapse_params": {
        "weight": -67.0,  # inhibitory synaptic conductance [nS]
    },
    "stimulus": "poisson_generator",
    "stimulus_params": {
        "rate": 300.0,  # rate of initial Poisson stimulus [spikes/s]
        "start": 1.0,  # start of Poisson generator [ms]
        "stop": 51.0,  # stop of Poisson generator [ms]
        "origin": 0.0,  # origin of time [ms]
    },
    "recorder": "spike_recorder",
    "recorder_params": {
        "record_to": "ascii",
        "label": "hh_coba",
    },
    "Nrec": 500,  # number of neurons per population to record from
    "Nstim": 50,  # number of neurons to stimulate
    "simtime": 1000.0,  # simulated time [ms]
    "dt": 0.1,  # simulation step [ms]
    "NE": 3200,  # number of excitatory neurons
    "NI": 800,  # number of inhibitory neurons
    "epsilon": 0.02,  # connection probability
    "virtual_processes": 1,  # number of virtual processes to use
}

Run the simulation

if __name__ == "__main__":
    nest.set_verbosity("M_WARNING")
    results = run_simulation(params)