brainpy.connect.GaussianProb#

class brainpy.connect.GaussianProb(sigma, encoding_values=None, normalize=True, include_self=True, periodic_boundary=False, seed=None)[source]#

Builds a Gaussian connectivity pattern within a population of neurons, where the connection probability decay according to the gaussian function.

Specifically, for any pair of neurons \((i, j)\),

\[p(i, j)=\exp(-\frac{\sum_{k=1}^n |v_k^i - v_k^j|^2 }{2\sigma^2})\]

where \(v_k^i\) is the $i$-th neuron’s encoded value at dimension $k$.

Parameters

  • sigma (float) – Width of the Gaussian function.

  • encoding_values (optional, list, tuple, int, float) –

    The value ranges to encode for neurons at each axis.

    • If values is not provided, the neuron only encodes each positional information, i.e., \((i, j, k, ...)\), where \(i, j, k\) is the index in the high-dimensional space.

    • If values is a single tuple/list of int/float, neurons at each dimension will encode the same range of values. For example, values=(0, np.pi), neurons at each dimension will encode a continuous value space [0, np.pi].

    • If values is a tuple/list of list/tuple, it means the value space will be different for each dimension. For example, values=((-np.pi, np.pi), (10, 20), (0, 2 * np.pi)).

  • periodic_boundary (bool) – Whether the neuron encode the value space with the periodic boundary.

  • normalize (bool) – Whether normalize the connection probability .

  • include_self (bool) – Whether create the conn at the same position.

  • seed (int) – The random seed.

__init__(sigma, encoding_values=None, normalize=True, include_self=True, periodic_boundary=False, seed=None)[source]#

Methods

__init__(sigma[, encoding_values, ...])

build_conn()

build connections with certain data type.

check(structures)

make_returns(structures, conn_data[, csr, ...])

Make the desired synaptic structures and return them.

require(*structures)

requires(*structures)