brainpy.connect module

Base Connection Classes and Tools

mat2coo(dense)
mat2csc(dense)
mat2csr(dense)	convert a dense matrix to (indices, indptr).
csr2csc(csr, post_num[, data])	Convert csr to csc.
csr2mat(csr, num_pre, num_post)	convert (indices, indptr) to a dense matrix.
csr2coo(csr)
coo2csr(coo, num_pre)	convert pre_ids, post_ids to (indices, indptr) when'jax_platform_name' = 'gpu'
coo2csc(coo, post_num[, data])	Convert csr to csc.
coo2mat(ij, num_pre, num_post)	convert (indices, indptr) to a dense matrix.
Connector()	Base Synaptic Connector Class.
TwoEndConnector([pre, post])	Synaptic connector to build connections between two neuron groups.
OneEndConnector(*args, **kwargs)	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

Custom Connections

MatConn(conn_mat, **kwargs)	Connector built from the dense connection matrix.
IJConn(i, j, **kwargs)	Connector built from the pre_ids and post_ids connections.
CSRConn(indices, inptr, **kwargs)	Connector built from the CSR sparse connection matrix.
SparseMatConn(csr_mat, **kwargs)	Connector built from the sparse connection matrix

Random Connections

FixedProb(prob[, pre_ratio, include_self, ...])	Connect the post-synaptic neurons with fixed probability.
FixedPreNum(num[, include_self, ...])	Connect a fixed number pf pre-synaptic neurons for each post-synaptic neuron.
FixedPostNum(num[, include_self, ...])	Connect the fixed number of post-synaptic neurons for each pre-synaptic neuron.
FixedTotalNum(num[, seed])	Connect the synaptic neurons with fixed total number.
GaussianProb(sigma[, encoding_values, ...])	Builds a Gaussian connectivity pattern within a population of neurons, where the connection probability decay according to the gaussian function.
ProbDist([dist, prob, pre_ratio, seed, ...])	Connection with a maximum distance under a probability p.
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(*args, **kwargs)	Connect two neuron groups one by one.
All2All(*args[, include_self])	Connect each neuron in first group to all neurons in the post-synaptic neuron groups.
GridFour([include_self, periodic_boundary])	The nearest four neighbors connection method.
GridEight([include_self, periodic_boundary])	The nearest eight neighbors conn method.
GridN([N, include_self, periodic_boundary])	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 connection method.
grid_eight	The nearest eight neighbors conn method.