Content#
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class
nngt.core.
Connections
[source]# The basic class that computes the properties of the connections between neurons for graphs.
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static
delays
(graph=None, dlist=None, elist=None, distribution='constant', parameters=None, noise_scale=None)[source]# Compute the delays of the neuronal connections.
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.
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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: - 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.
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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: - 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 (
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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: - 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.
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static
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nngt.core.
GraphObject
# alias of
nngt.core.nx_graph._NxGraph