Parameters

Model parameters in neurolib are stored as a dictionary-like object params as one of a model's attributes. Changing parameters is straightforward:

from neurolib.models.aln import ALNModel # Import the model
model = ALNModel() # Create an instance

model.params['duration'] = 10 * 1000 # in ms
model.run() # Run it

Parameters are dotdict objects that can also be accessed using the more simple syntax model.params.parameter_name = 123 (see Collections).

Default parameters

The default parameters of a model are stored in the loadDefaultParams.py within each model's directory. This function is called by the model.py file upon initialisation and returns all necessary parameters of the model.

Below is an example function that prepares the structural connectivity matrices Cmat and Dmat, all parameters of the model, and its initial values.

def loadDefaultParams(Cmat=None, Dmat=None, seed=None):
    """Load default parameters for a model

    :param Cmat: Structural connectivity matrix (adjacency matrix) of coupling strengths, will be normalized to 1. If not given, then a single node simulation will be assumed, defaults to None
    :type Cmat: numpy.ndarray, optional
    :param Dmat: Fiber length matrix, will be used for computing the delay matrix together with the signal transmission speed parameter `signalV`, defaults to None
    :type Dmat: numpy.ndarray, optional
    :param seed: Seed for the random number generator, defaults to None
    :type seed: int, optional

    :return: A dictionary with the default parameters of the model
    :rtype: dict
    """

    params = dotdict({})

    ### runtime parameters
    params.dt = 0.1  # ms 0.1ms is reasonable
    params.duration = 2000  # Simulation duration (ms)
    np.random.seed(seed)  # seed for RNG of noise and ICs
    # set seed to 0 if None, pypet will complain otherwise
    params.seed = seed or 0

    # make sure that seed=0 remains None
    if seed == 0:
        seed = None

    # ------------------------------------------------------------------------
    # global whole-brain network parameters
    # ------------------------------------------------------------------------

    # the coupling parameter determines how nodes are coupled.
    # "diffusive" for diffusive coupling, "additive" for additive coupling
    params.coupling = "diffusive"

    params.signalV = 20.0
    params.K_gl = 0.6  # global coupling strength

    if Cmat is None:
        params.N = 1
        params.Cmat = np.zeros((1, 1))
        params.lengthMat = np.zeros((1, 1))

    else:
        params.Cmat = Cmat.copy()  # coupling matrix
        np.fill_diagonal(params.Cmat, 0)  # no self connections
        params.N = len(params.Cmat)  # number of nodes
        params.lengthMat = Dmat

    # ------------------------------------------------------------------------
    # local node parameters
    # ------------------------------------------------------------------------

    # external input parameters:
    params.tau_ou = 5.0  # ms Timescale of the Ornstein-Uhlenbeck noise process
    params.sigma_ou = 0.0  # mV/ms/sqrt(ms) noise intensity
    params.x_ou_mean = 0.0  # mV/ms (OU process) [0-5]
    params.y_ou_mean = 0.0  # mV/ms (OU process) [0-5]

    # neural mass model parameters
    params.a = 0.25  # Hopf bifurcation parameter
    params.w = 0.2  # Oscillator frequency, 32 Hz at w = 0.2

    # ------------------------------------------------------------------------

    # initial values of the state variables
    params.xs_init = 0.5 * np.random.uniform(-1, 1, (params.N, 1))
    params.ys_init = 0.5 * np.random.uniform(-1, 1, (params.N, 1))

    # Ornstein-Uhlenbeck noise state variables
    params.x_ou = np.zeros((params.N,))
    params.y_ou = np.zeros((params.N,))

    # values of the external inputs
    params.x_ext = np.zeros((params.N,))
    params.y_ext = np.zeros((params.N,))

    return params