brainpy.connect module

This module provides methods to construct connectivity between neuron groups. You can access them through brainpy.connect.XXX.

Base Class

set_default_dtype([mat_dtype, idx_dtype])	Set the default dtype.
csr2csc(csr, post_num[, data])	Convert csr to csc.
csr2mat(csr, num_pre, num_post)	convert (indices, indptr) to a dense matrix.
mat2csr(dense)	convert a dense matrix to (indices, indptr).
ij2csr(pre_ids, post_ids, num_pre)	convert pre_ids, post_ids to (indices, indptr).
MAT_DTYPE	alias of numpy.bool_
IDX_DTYPE	alias of numpy.uint32
Connector()	Base Synaptic Connector Class.
TwoEndConnector()	Synaptic connector to build synapse connections between two neuron groups.
OneEndConnector()	Synaptic connector to build synapse connections within a population of neurons.
CONN_MAT	str(object='') -> str str(bytes_or_buffer[, encoding[, errors]]) -> str
PRE_IDS	str(object='') -> str str(bytes_or_buffer[, encoding[, errors]]) -> str
POST_IDS	str(object='') -> str str(bytes_or_buffer[, encoding[, errors]]) -> str
PRE2POST	str(object='') -> str str(bytes_or_buffer[, encoding[, errors]]) -> str
POST2PRE	str(object='') -> str str(bytes_or_buffer[, encoding[, errors]]) -> str
PRE2SYN	str(object='') -> str str(bytes_or_buffer[, encoding[, errors]]) -> str
POST2SYN	str(object='') -> str str(bytes_or_buffer[, encoding[, errors]]) -> str
SUPPORTED_SYN_STRUCTURE	Built-in mutable sequence.

Custom Connections

MatConn(conn_mat)	Connector built from the dense connection matrix.
IJConn(i, j)	Connector built from the pre_ids and post_ids connections.
SparseMatConn(csr_mat)	Connector built from the sparse connection matrix

Random Connections

FixedProb(prob[, include_self, seed])	Connect the post-synaptic neurons with fixed probability.
FixedPreNum(num[, include_self, seed])	Connect the pre-synaptic neurons with fixed number for each post-synaptic neuron.
FixedPostNum(num[, include_self, seed])	Connect the post-synaptic neurons with fixed number for each pre-synaptic neuron.
GaussianProb(sigma[, encoding_values, ...])	Builds a Gaussian connectivity pattern within a population of neurons, where the connection probability decay according to the gaussian function.
SmallWorld(num_neighbor, prob[, directed, ...])	Build a Watts–Strogatz small-world graph.
ScaleFreeBA(m[, directed, seed])	Build a random graph according to the Barabási–Albert preferential attachment model.
ScaleFreeBADual(m1, m2, p[, directed, seed])	Build a random graph according to the dual Barabási–Albert preferential attachment model.
PowerLaw(m, p[, directed, seed])	Holme and Kim algorithm for growing graphs with powerlaw degree distribution and approximate average clustering.

Regular Connections

One2One()	Connect two neuron groups one by one.
All2All([include_self])	Connect each neuron in first group to all neurons in the post-synaptic neuron groups.
GridFour([include_self])	The nearest four neighbors conn method.
GridEight([include_self])	The nearest eight neighbors conn method.
GridN([N, include_self])	The nearest (2*N+1) * (2*N+1) neighbors conn method.
one2one	Connect two neuron groups one by one.
all2all	Connect each neuron in first group to all neurons in the post-synaptic neuron groups.
grid_four	The nearest four neighbors conn method.
grid_eight	The nearest eight neighbors conn method.