Core module#

Core classes and functions. Most of them are not visible in the module as they are directly loaded at nngt level.

Content#

class nngt.core.Connections[source]#

The basic class that computes the properties of the connections between neurons for graphs.

static delays(graph=None, dlist=None, elist=None, distribution='constant', parameters=None, noise_scale=None)[source]#

Compute the delays of the neuronal connections.

Parameters:

Parameters:	graph (class:~nngt.Graph or subclass) – Graph the nodes belong to. dlist (class:numpy.array, optional (default: None)) – List of user-defined delays). elist (class:numpy.array, optional (default: None)) – List of the edges which value should be updated. distribution (class:string, optional (default: “constant”)) – Type of distribution (choose among “constant”, “uniform”, “lognormal”, “gaussian”, “user_def”, “lin_corr”, “log_corr”). parameters (class:dict, optional (default: {})) – Dictionary containing the distribution parameters. noise_scale (class:int, optional (default: None)) – Scale of the multiplicative Gaussian noise that should be applied on the weights.
Returns:	new_delays (class:scipy.sparse.lil_matrix) – A sparse matrix containing ONLY the newly-computed weights.

graph (class:~nngt.Graph or subclass) – Graph the nodes belong to.
dlist (class:numpy.array, optional (default: None)) – List of user-defined delays).
elist (class:numpy.array, optional (default: None)) – List of the edges which value should be updated.
distribution (class:string, optional (default: “constant”)) – Type of distribution (choose among “constant”, “uniform”, “lognormal”, “gaussian”, “user_def”, “lin_corr”, “log_corr”).
parameters (class:dict, optional (default: {})) – Dictionary containing the distribution parameters.
noise_scale (class:int, optional (default: None)) – Scale of the multiplicative Gaussian noise that should be applied on the weights.

Returns:

new_delays (class:scipy.sparse.lil_matrix) – A sparse matrix containing ONLY the newly-computed weights.

static distances(graph, elist=None, pos=None, dlist=None, overwrite=False)[source]#

Compute the distances between connected nodes in the graph. Try to add only the new distances to the graph. If they overlap with previously computed distances, recomputes everything.

Parameters:

Parameters:	graph (class:~nngt.Graph or subclass) – Graph the nodes belong to. elist (class:numpy.array, optional (default: None)) – List of the edges. pos (class:numpy.array, optional (default: None)) – Positions of the nodes; note that if graph has a “position” attribute, pos will not be taken into account. dlist (class:numpy.array, optional (default: None)) – List of distances (for user-defined distances)
Returns:	new_dist (class:numpy.array) – Array containing ONLY the newly-computed distances.

graph (class:~nngt.Graph or subclass) – Graph the nodes belong to.
elist (class:numpy.array, optional (default: None)) – List of the edges.
pos (class:numpy.array, optional (default: None)) – Positions of the nodes; note that if graph has a “position” attribute, pos will not be taken into account.
dlist (class:numpy.array, optional (default: None)) – List of distances (for user-defined distances)

Returns:

new_dist (class:numpy.array) – Array containing ONLY the newly-computed distances.

static types(graph, inhib_nodes=None, inhib_frac=None)[source]#

@todo

Define the type of a set of neurons. If no arguments are given, all edges will be set as excitatory.

Parameters:

Parameters:	graph (`Graph` or subclass) – Graph on which edge types will be created. inhib_nodes (int, float or list, optional (default: None)) – If inhib_nodes is an int, number of inhibitory nodes in the graph (all connections from inhibitory nodes are inhibitory); if it is a float, ratio of inhibitory nodes in the graph; if it is a list, ids of the inhibitory nodes. inhib_frac (float, optional (default: None)) – Fraction of the selected edges that will be set as refractory (if inhib_nodes is not None, it is the fraction of the nodes’ edges that will become inhibitory, otherwise it is the fraction of all the edges in the graph).
Returns:	t_list (`ndarray`) – List of the edges’ types.

graph (Graph or subclass) – Graph on which edge types will be created.
inhib_nodes (int, float or list, optional (default: None)) – If inhib_nodes is an int, number of inhibitory nodes in the graph (all connections from inhibitory nodes are inhibitory); if it is a float, ratio of inhibitory nodes in the graph; if it is a list, ids of the inhibitory nodes.
inhib_frac (float, optional (default: None)) – Fraction of the selected edges that will be set as refractory (if inhib_nodes is not None, it is the fraction of the nodes’ edges that will become inhibitory, otherwise it is the fraction of all the edges in the graph).

Returns:

t_list (ndarray) – List of the edges’ types.

static weights(graph=None, elist=None, wlist=None, distribution='constant', parameters={}, noise_scale=None)[source]#

Compute the weights of the graph’s edges. @todo: take elist into account

Parameters:

Parameters:	graph (class:~nngt.Graph or subclass) – Graph the nodes belong to. elist (class:numpy.array, optional (default: None)) – List of the edges (for user defined weights). wlist (class:numpy.array, optional (default: None)) – List of the weights (for user defined weights). distribution (class:string, optional (default: “constant”)) – Type of distribution (choose among “constant”, “uniform”, “lognormal”, “gaussian”, “user_def”, “lin_corr”, “log_corr”). parameters (class:dict, optional (default: {})) – Dictionary containing the distribution parameters. noise_scale (class:int, optional (default: None)) – Scale of the multiplicative Gaussian noise that should be applied on the weights.
Returns:	new_weights (class:scipy.sparse.lil_matrix) – A sparse matrix containing ONLY the newly-computed weights.

graph (class:~nngt.Graph or subclass) – Graph the nodes belong to.
elist (class:numpy.array, optional (default: None)) – List of the edges (for user defined weights).
wlist (class:numpy.array, optional (default: None)) – List of the weights (for user defined weights).
distribution (class:string, optional (default: “constant”)) – Type of distribution (choose among “constant”, “uniform”, “lognormal”, “gaussian”, “user_def”, “lin_corr”, “log_corr”).
parameters (class:dict, optional (default: {})) – Dictionary containing the distribution parameters.
noise_scale (class:int, optional (default: None)) – Scale of the multiplicative Gaussian noise that should be applied on the weights.

Returns:

new_weights (class:scipy.sparse.lil_matrix) – A sparse matrix containing ONLY the newly-computed weights.

nngt.core.GraphObject#

Graph object (reference to one of the main libraries’ wrapper

alias of _NxGraph