brainpy.connect.FixedPostNum#

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

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

Parameters
• num (float, int) – The conn 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.

• method (str) –

The method used to create the connection.

• 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: $$2 * N_{need} * N_{pre}$$.

• iter: This method will iteratively build the synaptic connections. It has the minimum pressure of memory consuming, only $$2 * N_{need} * N_{pre}$$ (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)