brainpy.connect.FixedPreNum#

class brainpy.connect.FixedPreNum(num, include_self=True, seed=None)[source]#

Connect the pre-synaptic neurons with fixed number for each post-synaptic neuron.

Parameters

  • num (float, int) – The connection probability (if “num” is float) or the fixed number of connectivity (if “num” is int).

  • include_self (bool) – Whether create (i, i) conn ?

  • seed (None, int) –

    Seed the random generator.

    • matrix: This method will create a big matrix, then, the connectivity is constructed from this matrix \((N_{pre}, N_{post})\). In a large network, this method will consume huge memories, including a matrix: \((N_{pre}, N_{post})\), two vectors: \(2 * N_{need} * N_{post}\).

    • iter: This method will iteratively build the synaptic connections. It has the minimum pressure of memory consuming, only \(2 * N_{need} * N_{post}\) (i and j vectors).

__init__(num, include_self=True, seed=None)[source]#

Methods

__init__(num[, include_self, seed])

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)