Warning

This is A PREVIEW for NEST 3.0 and NOT an OFFICIAL RELEASE! Some functionality may not be available and information may be incomplete!

pynest microcircuit parameters

Hendrik Rothe, Hannah Bos, Sacha van Albada; May 2016

import numpy as np


def get_mean_delays(mean_delay_exc, mean_delay_inh, number_of_pop):
    """ Creates matrix containing the delay of all connections.

    Arguments
    ---------
    mean_delay_exc
        Delay of the excitatory connections.
    mean_delay_inh
        Delay of the inhibitory connections.
    number_of_pop
        Number of populations.

    Returns
    -------
    mean_delays
        Matrix specifying the mean delay of all connections.

    """

    dim = number_of_pop
    mean_delays = np.zeros((dim, dim))
    mean_delays[:, 0:dim:2] = mean_delay_exc
    mean_delays[:, 1:dim:2] = mean_delay_inh
    return mean_delays


def get_std_delays(std_delay_exc, std_delay_inh, number_of_pop):
    """ Creates matrix containing the standard deviations of all delays.

    Arguments
    ---------
    std_delay_exc
        Standard deviation of excitatory delays.
    std_delay_inh
        Standard deviation of inhibitory delays.
    number_of_pop
        Number of populations in the microcircuit.

    Returns
    -------
    std_delays
        Matrix specifying the standard deviation of all delays.

    """

    dim = number_of_pop
    std_delays = np.zeros((dim, dim))
    std_delays[:, 0:dim:2] = std_delay_exc
    std_delays[:, 1:dim:2] = std_delay_inh
    return std_delays


def get_mean_PSP_matrix(PSP_e, g, number_of_pop):
    """ Creates a matrix of the mean evoked postsynaptic potential.

    The function creates a matrix of the mean evoked postsynaptic
    potentials between the recurrent connections of the microcircuit.
    The weight of the connection from L4E to L23E is doubled.

    Arguments
    ---------
    PSP_e
        Mean evoked potential.
    g
        Relative strength of the inhibitory to excitatory connection.
    number_of_pop
        Number of populations in the microcircuit.

    Returns
    -------
    weights
        Matrix of the weights for the recurrent connections.

    """
    dim = number_of_pop
    weights = np.zeros((dim, dim))
    exc = PSP_e
    inh = PSP_e * g
    weights[:, 0:dim:2] = exc
    weights[:, 1:dim:2] = inh
    weights[0, 2] = exc * 2
    return weights


def get_std_PSP_matrix(PSP_rel, number_of_pop):
    """ Relative standard deviation matrix of postsynaptic potential created.

    The relative standard deviation matrix of the evoked postsynaptic potential
    for the recurrent connections of the microcircuit is created.

    Arguments
    ---------
    PSP_rel
        Relative standard deviation of the evoked postsynaptic potential.
    number_of_pop
        Number of populations in the microcircuit.

    Returns
    -------
    std_mat
        Matrix of the standard deviation of postsynaptic potentials.

    """
    dim = number_of_pop
    std_mat = np.zeros((dim, dim))
    std_mat[:, :] = PSP_rel
    return std_mat

net_dict = {
    # Neuron model.
    'neuron_model': 'iaf_psc_exp',
    # The default recording device is the spike_detector. If you also
    # want to record the membrane potentials of the neurons, add
    # 'voltmeter' to the list.
    'rec_dev': ['spike_detector'],
    # Names of the simulated populations.
    'populations': ['L23E', 'L23I', 'L4E', 'L4I', 'L5E', 'L5I', 'L6E', 'L6I'],
    # Number of neurons in the different populations. The order of the
    # elements corresponds to the names of the variable 'populations'.
    'N_full': np.array([20683, 5834, 21915, 5479, 4850, 1065, 14395, 2948]),
    # Mean rates of the different populations in the non-scaled version
    # of the microcircuit. Necessary for the scaling of the network.
    # The order corresponds to the order in 'populations'.
    'full_mean_rates':
        np.array([0.971, 2.868, 4.746, 5.396, 8.142, 9.078, 0.991, 7.523]),
    # Connection probabilities. The first index corresponds to the targets
    # and the second to the sources.
    'conn_probs':
        np.array(
            [[0.1009, 0.1689, 0.0437, 0.0818, 0.0323, 0., 0.0076, 0.],
             [0.1346, 0.1371, 0.0316, 0.0515, 0.0755, 0., 0.0042, 0.],
             [0.0077, 0.0059, 0.0497, 0.135, 0.0067, 0.0003, 0.0453, 0.],
             [0.0691, 0.0029, 0.0794, 0.1597, 0.0033, 0., 0.1057, 0.],
             [0.1004, 0.0622, 0.0505, 0.0057, 0.0831, 0.3726, 0.0204, 0.],
             [0.0548, 0.0269, 0.0257, 0.0022, 0.06, 0.3158, 0.0086, 0.],
             [0.0156, 0.0066, 0.0211, 0.0166, 0.0572, 0.0197, 0.0396, 0.2252],
             [0.0364, 0.001, 0.0034, 0.0005, 0.0277, 0.008, 0.0658, 0.1443]]
            ),
    # Number of external connections to the different populations.
    # The order corresponds to the order in 'populations'.
    'K_ext': np.array([1600, 1500, 2100, 1900, 2000, 1900, 2900, 2100]),
    # Factor to scale the indegrees.
    'K_scaling': 0.1,
    # Factor to scale the number of neurons.
    'N_scaling': 0.1,
    # Mean amplitude of excitatory postsynaptic potential (in mV).
    'PSP_e': 0.15,
    # Relative standard deviation of the postsynaptic potential.
    'PSP_sd': 0.1,
    # Relative inhibitory synaptic strength (in relative units).
    'g': -4,
    # Rate of the Poissonian spike generator (in Hz).
    'bg_rate': 8.,
    # Turn Poisson input on or off (True or False).
    'poisson_input': True,
    # Delay of the Poisson generator (in ms).
    'poisson_delay': 1.5,
    # Mean delay of excitatory connections (in ms).
    'mean_delay_exc': 1.5,
    # Mean delay of inhibitory connections (in ms).
    'mean_delay_inh': 0.75,
    # Relative standard deviation of the delay of excitatory and
    # inhibitory connections (in relative units).
    'rel_std_delay': 0.5,
    # Parameters of the neurons.
    'neuron_params': {
        # Membrane potential average for the neurons (in mV).
        'V0_mean': -58.0,
        # Standard deviation of the average membrane potential (in mV).
        'V0_sd': 10.0,
        # Reset membrane potential of the neurons (in mV).
        'E_L': -65.0,
        # Threshold potential of the neurons (in mV).
        'V_th': -50.0,
        # Membrane potential after a spike (in mV).
        'V_reset': -65.0,
        # Membrane capacitance (in pF).
        'C_m': 250.0,
        # Membrane time constant (in ms).
        'tau_m': 10.0,
        # Time constant of postsynaptic excitatory currents (in ms).
        'tau_syn_ex': 0.5,
        # Time constant of postsynaptic inhibitory currents (in ms).
        'tau_syn_in': 0.5,
        # Time constant of external postsynaptic excitatory current (in ms).
        'tau_syn_E': 0.5,
        # Refractory period of the neurons after a spike (in ms).
        't_ref': 2.0}
    }

updated_dict = {
    # PSP mean matrix.
    'PSP_mean_matrix': get_mean_PSP_matrix(
        net_dict['PSP_e'], net_dict['g'], len(net_dict['populations'])
        ),
    # PSP std matrix.
    'PSP_std_matrix': get_std_PSP_matrix(
        net_dict['PSP_sd'], len(net_dict['populations'])
        ),
    # mean delay matrix.
    'mean_delay_matrix': get_mean_delays(
        net_dict['mean_delay_exc'], net_dict['mean_delay_inh'],
        len(net_dict['populations'])
        ),
    # std delay matrix.
    'std_delay_matrix': get_std_delays(
        net_dict['mean_delay_exc'] * net_dict['rel_std_delay'],
        net_dict['mean_delay_inh'] * net_dict['rel_std_delay'],
        len(net_dict['populations'])
        ),
    }


net_dict.update(updated_dict)