Generation module#

Functions that generates the underlying connectivity of graphs, as well as the synaptic properties (weight/strength and delay).

Content#

`nngt.generation.all_to_all`([nodes, …])	Generate a graph where all nodes are connected.
`nngt.generation.connect_neural_groups`(…[, …])	Function to connect excitatory and inhibitory population with a given graph model.
`nngt.generation.connect_neural_types`(…[, …])	Function to connect excitatory and inhibitory population with a given graph model.
`nngt.generation.connect_nodes`(network, …)	Function to connect nodes with a given graph model.
`nngt.generation.distance_rule`(scale[, rule, …])	Create a graph using a 2D distance rule to create the connection between neurons.
`nngt.generation.erdos_renyi`([density, …])	Generate a random graph as defined by Erdos and Renyi but with a reciprocity that can be chosen.
`nngt.generation.fixed_degree`(degree[, …])	Generate a random graph with constant in- or out-degree.
`nngt.generation.gaussian_degree`(avg, std[, …])	Generate a random graph with constant in- or out-degree.
`nngt.generation.newman_watts`(coord_nb, …)	Generate a small-world graph using the Newman-Watts algorithm.
`nngt.generation.price_scale_free`(m[, c, …])	@todo make the algorithm.
`nngt.generation.random_scale_free`(in_exp, …)	Generate a free-scale graph of given reciprocity and otherwise devoid of correlations.

Details#

nngt.generation.connect_neural_groups(network, source_groups, target_groups, graph_model, density=-1.0, edges=-1, avg_deg=-1.0, unit='um', weighted=True, directed=True, multigraph=False, **kwargs)[source]#

Function to connect excitatory and inhibitory population with a given graph model.

Changed in version 0.8: Model-specific arguments are now provided as keywords and not through a dict. It is now possible to provide different weights and delays at each call.

@todo: make the modifications for only a set of edges

Parameters:

Parameters:	network (`Network` or `SpatialNetwork`) – The network to connect. source_groups (str or tuple) – Names of the source groups (which contain the pre-synaptic neurons) target_groups (str or tuple) – Names of the target groups (which contain the post-synaptic neurons) graph_model (string) – The name of the connectivity model (among “erdos_renyi”, “random_scale_free”, “price_scale_free”, and “newman_watts”). kwargs (keyword arguments) – Specific model parameters. or edge attributes specifiers such as weights or delays.

network (Network or SpatialNetwork) – The network to connect.
source_groups (str or tuple) – Names of the source groups (which contain the pre-synaptic neurons)
target_groups (str or tuple) – Names of the target groups (which contain the post-synaptic neurons)
graph_model (string) – The name of the connectivity model (among “erdos_renyi”, “random_scale_free”, “price_scale_free”, and “newman_watts”).
kwargs (keyword arguments) – Specific model parameters. or edge attributes specifiers such as weights or delays.

nngt.generation.connect_neural_types(network, source_type, target_type, graph_model, density=-1.0, edges=-1, avg_deg=-1.0, unit='um', weighted=True, directed=True, multigraph=False, **kwargs)[source]#

Function to connect excitatory and inhibitory population with a given graph model.

Changed in version 0.8: Model-specific arguments are now provided as keywords and not through a dict. It is now possible to provide different weights and delays at each call.

@todo: make the modifications for only a set of edges

Parameters:

Parameters:	network (`Network` or `SpatialNetwork`) – The network to connect. source_type (int) – The type of source neurons (`1` for excitatory, `-1` for inhibitory neurons). target_type (int) – The type of target neurons. graph_model (string) – The name of the connectivity model (among “erdos_renyi”, “random_scale_free”, “price_scale_free”, and “newman_watts”). kwargs (keyword arguments) – Specific model parameters. or edge attributes specifiers such as weights or delays.

network (Network or SpatialNetwork) – The network to connect.
source_type (int) – The type of source neurons (1 for excitatory, -1 for inhibitory neurons).
target_type (int) – The type of target neurons.
graph_model (string) – The name of the connectivity model (among “erdos_renyi”, “random_scale_free”, “price_scale_free”, and “newman_watts”).
kwargs (keyword arguments) – Specific model parameters. or edge attributes specifiers such as weights or delays.

nngt.generation.distance_rule(scale, rule='exp', shape=None, neuron_density=1000.0, max_proba=-1.0, nodes=0, density=-1.0, edges=-1, avg_deg=-1.0, unit='um', weighted=True, directed=True, multigraph=False, name='DR', positions=None, population=None, from_graph=None, **kwargs)[source]#

Create a graph using a 2D distance rule to create the connection between neurons. Available rules are linear and exponential.

Parameters:

Parameters:	scale (float) – Characteristic scale for the distance rule. E.g for linear distance- rule, \(P(i,j) \propto (1-d_{ij}/scale))\), whereas for the exponential distance-rule, \(P(i,j) \propto e^{-d_{ij}/scale}\). rule (string, optional (default: ‘exp’)) – Rule that will be apply to draw the connections between neurons. Choose among “exp” (exponential), “gaussian” (Gaussian), or “lin” (linear). shape (`Shape`, optional (default: None)) – Shape of the neurons’ environment. If not specified, a square will be created with the appropriate dimensions for the number of neurons and the neuron spatial density. neuron_density (float, optional (default: 1000.)) – Density of neurons in space (\(neurons \cdot mm^{-2}\)). nodes (int, optional (default: None)) – The number of nodes in the graph. p (float, optional) – Normalization factor for the distance rule; it is equal to the probability of connection when testing a node at zero distance. density (double, optional) – Structural density given by edges / (nodes * nodes). edges (int, optional) – The number of edges between the nodes avg_deg (double, optional) – Average degree of the neurons given by edges / nodes. unit (string (default: ‘um’)) – Unit for the length scale among ‘um’ (\(\mu m\)), ‘mm’, ‘cm’, ‘dm’, ‘m’. weighted (bool, optional (default: True)) – @todo Whether the graph edges have weights. directed (bool, optional (default: True)) – Whether the graph is directed or not. multigraph (bool, optional (default: False)) – Whether the graph can contain multiple edges between two nodes. name (string, optional (default: “DR”)) – Name of the created graph. positions (`numpy.ndarray`, optional (default: None)) – A 2D (N, 2) or 3D (N, 3) shaped array containing the positions of the neurons in space. population (`NeuralPop`, optional (default: None)) – Population of neurons defining their biological properties (to create a `Network`). from_graph (`Graph` or subclass, optional (default: None)) – Initial graph whose nodes are to be connected.

scale (float) – Characteristic scale for the distance rule. E.g for linear distance- rule, \(P(i,j) \propto (1-d_{ij}/scale))\), whereas for the exponential distance-rule, \(P(i,j) \propto e^{-d_{ij}/scale}\).
rule (string, optional (default: ‘exp’)) – Rule that will be apply to draw the connections between neurons. Choose among “exp” (exponential), “gaussian” (Gaussian), or “lin” (linear).
shape (Shape, optional (default: None)) – Shape of the neurons’ environment. If not specified, a square will be created with the appropriate dimensions for the number of neurons and the neuron spatial density.
neuron_density (float, optional (default: 1000.)) – Density of neurons in space (\(neurons \cdot mm^{-2}\)).
nodes (int, optional (default: None)) – The number of nodes in the graph.
p (float, optional) – Normalization factor for the distance rule; it is equal to the probability of connection when testing a node at zero distance.
density (double, optional) – Structural density given by edges / (nodes * nodes).
edges (int, optional) – The number of edges between the nodes
avg_deg (double, optional) – Average degree of the neurons given by edges / nodes.
unit (string (default: ‘um’)) – Unit for the length scale among ‘um’ (\(\mu m\)), ‘mm’, ‘cm’, ‘dm’, ‘m’.
weighted (bool, optional (default: True)) – @todo Whether the graph edges have weights.
directed (bool, optional (default: True)) – Whether the graph is directed or not.
multigraph (bool, optional (default: False)) – Whether the graph can contain multiple edges between two nodes.
name (string, optional (default: “DR”)) – Name of the created graph.
positions (numpy.ndarray, optional (default: None)) – A 2D (N, 2) or 3D (N, 3) shaped array containing the positions of the neurons in space.
population (NeuralPop, optional (default: None)) – Population of neurons defining their biological properties (to create a Network).
from_graph (Graph or subclass, optional (default: None)) – Initial graph whose nodes are to be connected.

nngt.generation.erdos_renyi(density=-1.0, nodes=0, edges=-1, avg_deg=-1.0, reciprocity=-1.0, weighted=True, directed=True, multigraph=False, name='ER', shape=None, positions=None, population=None, from_graph=None, **kwargs)[source]#

Generate a random graph as defined by Erdos and Renyi but with a reciprocity that can be chosen.

Parameters:

Parameters:	density (double, optional (default: -1.)) – Structural density given by edges / nodes\(^2\). It is also the probability for each possible edge in the graph to exist. nodes (int, optional (default: None)) – The number of nodes in the graph. edges (int (optional)) – The number of edges between the nodes avg_deg (double, optional) – Average degree of the neurons given by edges / nodes. reciprocity (double, optional (default: -1 to let it free)) – Fraction of edges that are bidirectional (only for directed graphs – undirected graphs have a reciprocity of 1 by definition) weighted (bool, optional (default: True)) – Whether the graph edges have weights. directed (bool, optional (default: True)) – Whether the graph is directed or not. multigraph (bool, optional (default: False)) – Whether the graph can contain multiple edges between two nodes. name (string, optional (default: “ER”)) – Name of the created graph. shape (`Shape`, optional (default: None)) – Shape of the neurons’ environment. positions (`numpy.ndarray`, optional (default: None)) – A 2D or 3D array containing the positions of the neurons in space. population (`NeuralPop`, optional (default: None)) – Population of neurons defining their biological properties (to create a `Network`). from_graph (`Graph` or subclass, optional (default: None)) – Initial graph whose nodes are to be connected.
Returns:	graph_er (`Graph`, or subclass) – A new generated graph or the modified from_graph.

density (double, optional (default: -1.)) – Structural density given by edges / nodes\(^2\). It is also the probability for each possible edge in the graph to exist.
nodes (int, optional (default: None)) – The number of nodes in the graph.
edges (int (optional)) – The number of edges between the nodes
avg_deg (double, optional) – Average degree of the neurons given by edges / nodes.
reciprocity (double, optional (default: -1 to let it free)) – Fraction of edges that are bidirectional (only for directed graphs – undirected graphs have a reciprocity of 1 by definition)
weighted (bool, optional (default: True)) – Whether the graph edges have weights.
directed (bool, optional (default: True)) – Whether the graph is directed or not.
multigraph (bool, optional (default: False)) – Whether the graph can contain multiple edges between two nodes.
name (string, optional (default: “ER”)) – Name of the created graph.
shape (Shape, optional (default: None)) – Shape of the neurons’ environment.
positions (numpy.ndarray, optional (default: None)) – A 2D or 3D array containing the positions of the neurons in space.
population (NeuralPop, optional (default: None)) – Population of neurons defining their biological properties (to create a Network).
from_graph (Graph or subclass, optional (default: None)) – Initial graph whose nodes are to be connected.

Returns:

graph_er (Graph, or subclass) – A new generated graph or the modified from_graph.

Note

nodes is required unless from_graph or population is provided. If an from_graph is provided, all preexistant edges in the object will be deleted before the new connectivity is implemented.

nngt.generation.fixed_degree(degree, degree_type='in', nodes=0, reciprocity=-1.0, weighted=True, directed=True, multigraph=False, name='FD', shape=None, positions=None, population=None, from_graph=None, **kwargs)[source]#

Generate a random graph with constant in- or out-degree.

Parameters:

Parameters:	degree (int) – The value of the constant degree. degree_type (str, optional (default: ‘in’)) – The type of the fixed degree, among `'in'`, `'out'` or `'total'`. @todo ‘total’ not implemented yet. nodes (int, optional (default: None)) – The number of nodes in the graph. reciprocity (double, optional (default: -1 to let it free)) – @todo: not implemented yet. Fraction of edges that are bidirectional (only for directed graphs – undirected graphs have a reciprocity of 1 by definition) weighted (bool, optional (default: True)) – Whether the graph edges have weights. directed (bool, optional (default: True)) – @todo: only for directed graphs for now. Whether the graph is directed or not. multigraph (bool, optional (default: False)) – Whether the graph can contain multiple edges between two nodes. name (string, optional (default: “ER”)) – Name of the created graph. shape (`Shape`, optional (default: None)) – Shape of the neurons’ environment. positions (`numpy.ndarray`, optional (default: None)) – A 2D or 3D array containing the positions of the neurons in space. population (`NeuralPop`, optional (default: None)) – Population of neurons defining their biological properties (to create a `Network`). from_graph (`Graph` or subclass, optional (default: None)) – Initial graph whose nodes are to be connected.

degree (int) – The value of the constant degree.
degree_type (str, optional (default: ‘in’)) – The type of the fixed degree, among 'in', 'out' or 'total'.

@todo

‘total’ not implemented yet.
nodes (int, optional (default: None)) – The number of nodes in the graph.
reciprocity (double, optional (default: -1 to let it free)) – @todo: not implemented yet. Fraction of edges that are bidirectional (only for directed graphs – undirected graphs have a reciprocity of 1 by definition)
weighted (bool, optional (default: True)) – Whether the graph edges have weights.
directed (bool, optional (default: True)) – @todo: only for directed graphs for now. Whether the graph is directed or not.
multigraph (bool, optional (default: False)) – Whether the graph can contain multiple edges between two nodes.
name (string, optional (default: “ER”)) – Name of the created graph.
shape (Shape, optional (default: None)) – Shape of the neurons’ environment.
positions (numpy.ndarray, optional (default: None)) – A 2D or 3D array containing the positions of the neurons in space.
population (NeuralPop, optional (default: None)) – Population of neurons defining their biological properties (to create a Network).
from_graph (Graph or subclass, optional (default: None)) – Initial graph whose nodes are to be connected.

Note

nodes is required unless from_graph or population is provided. If an from_graph is provided, all preexistant edges in the object will be deleted before the new connectivity is implemented.

Returns:	graph_fd (`Graph`, or subclass) – A new generated graph or the modified from_graph.

nngt.generation.gaussian_degree(avg, std, degree_type='in', nodes=0, reciprocity=-1.0, weighted=True, directed=True, multigraph=False, name='GD', shape=None, positions=None, population=None, from_graph=None, **kwargs)[source]#

Generate a random graph with constant in- or out-degree. @todo: adapt it for undirected graphs!

Parameters:

Parameters:	avg (float) – The value of the average degree. std (float) – The standard deviation of the Gaussian distribution. degree_type (str, optional (default: ‘in’)) – The type of the fixed degree, among ‘in’, ‘out’ or ‘total’ @todo: Implement ‘total’ degree nodes (int, optional (default: None)) – The number of nodes in the graph. reciprocity (double, optional (default: -1 to let it free)) – @todo: not implemented yet. Fraction of edges that are bidirectional (only for directed graphs – undirected graphs have a reciprocity of 1 by definition) weighted (bool, optional (default: True)) – Whether the graph edges have weights. directed (bool, optional (default: True)) – @todo: only for directed graphs for now. Whether the graph is directed or not. multigraph (bool, optional (default: False)) – Whether the graph can contain multiple edges between two nodes. name (string, optional (default: “ER”)) – Name of the created graph. shape (`Shape`, optional (default: None)) – Shape of the neurons’ environment. positions (`numpy.ndarray`, optional (default: None)) – A 2D or 3D array containing the positions of the neurons in space. population (`NeuralPop`, optional (default: None)) – Population of neurons defining their biological properties (to create a `Network`). from_graph (`Graph` or subclass, optional (default: None)) – Initial graph whose nodes are to be connected.
Returns:	graph_gd (`Graph`, or subclass) – A new generated graph or the modified from_graph.

avg (float) – The value of the average degree.
std (float) – The standard deviation of the Gaussian distribution.
degree_type (str, optional (default: ‘in’)) – The type of the fixed degree, among ‘in’, ‘out’ or ‘total’ @todo: Implement ‘total’ degree
nodes (int, optional (default: None)) – The number of nodes in the graph.
reciprocity (double, optional (default: -1 to let it free)) – @todo: not implemented yet. Fraction of edges that are bidirectional (only for directed graphs – undirected graphs have a reciprocity of 1 by definition)
weighted (bool, optional (default: True)) – Whether the graph edges have weights.
directed (bool, optional (default: True)) – @todo: only for directed graphs for now. Whether the graph is directed or not.
multigraph (bool, optional (default: False)) – Whether the graph can contain multiple edges between two nodes.
name (string, optional (default: “ER”)) – Name of the created graph.
shape (Shape, optional (default: None)) – Shape of the neurons’ environment.
positions (numpy.ndarray, optional (default: None)) – A 2D or 3D array containing the positions of the neurons in space.
population (NeuralPop, optional (default: None)) – Population of neurons defining their biological properties (to create a Network).
from_graph (Graph or subclass, optional (default: None)) – Initial graph whose nodes are to be connected.

Returns:

graph_gd (Graph, or subclass) – A new generated graph or the modified from_graph.

Note

nodes is required unless from_graph or population is provided. If an from_graph is provided, all preexistant edges in the object will be deleted before the new connectivity is implemented.

nngt.generation.random_scale_free(in_exp, out_exp, nodes=0, density=-1, edges=-1, avg_deg=-1, reciprocity=0.0, weighted=True, directed=True, multigraph=False, name='RandomSF', shape=None, positions=None, population=None, from_graph=None, **kwargs)[source]#

Generate a free-scale graph of given reciprocity and otherwise devoid of correlations.

Parameters:

Parameters:	in_exp (float) – Absolute value of the in-degree exponent \(\gamma_i\), such that \(p(k_i) \propto k_i^{-\gamma_i}\) out_exp (float) – Absolute value of the out-degree exponent \(\gamma_o\), such that \(p(k_o) \propto k_o^{-\gamma_o}\) nodes (int, optional (default: None)) – The number of nodes in the graph. density (double, optional (default: 0.1)) – Structural density given by edges / (nodesnodes). edges* (int (optional)) – The number of edges between the nodes avg_deg (double, optional) – Average degree of the neurons given by edges / nodes. weighted (bool, optional (default: True)) – @todo Whether the graph edges have weights. directed (bool, optional (default: True)) – Whether the graph is directed or not. multigraph (bool, optional (default: False)) – Whether the graph can contain multiple edges between two nodes. can contain multiple edges between two name (string, optional (default: “ER”)) – Name of the created graph. shape (`Shape`, optional (default: None)) – Shape of the neurons’ environment. positions (`numpy.ndarray`, optional (default: None)) – A 2D or 3D array containing the positions of the neurons in space. population (`NeuralPop`, optional (default: None)) – Population of neurons defining their biological properties (to create a `Network`) from_graph (`Graph` or subclass, optional (default: None)) – Initial graph whose nodes are to be connected.
Returns:	graph_fs (`Graph`)

in_exp (float) – Absolute value of the in-degree exponent \(\gamma_i\), such that \(p(k_i) \propto k_i^{-\gamma_i}\)
out_exp (float) – Absolute value of the out-degree exponent \(\gamma_o\), such that \(p(k_o) \propto k_o^{-\gamma_o}\)
nodes (int, optional (default: None)) – The number of nodes in the graph.
density (double, optional (default: 0.1)) – Structural density given by edges / (nodes*nodes).
edges (int (optional)) – The number of edges between the nodes
avg_deg (double, optional) – Average degree of the neurons given by edges / nodes.
weighted (bool, optional (default: True)) – @todo Whether the graph edges have weights.
directed (bool, optional (default: True)) – Whether the graph is directed or not.
multigraph (bool, optional (default: False)) – Whether the graph can contain multiple edges between two nodes. can contain multiple edges between two
name (string, optional (default: “ER”)) – Name of the created graph.
shape (Shape, optional (default: None)) – Shape of the neurons’ environment.
positions (numpy.ndarray, optional (default: None)) – A 2D or 3D array containing the positions of the neurons in space.
population (NeuralPop, optional (default: None)) – Population of neurons defining their biological properties (to create a Network)
from_graph (Graph or subclass, optional (default: None)) – Initial graph whose nodes are to be connected.

Returns:

graph_fs (Graph)

Note

As reciprocity increases, requested values of in_exp and out_exp will be less and less respected as the distribution will converge to a common exponent \(\gamma = (\gamma_i + \gamma_o) / 2\). Parameter nodes is required unless from_graph or population is provided.

nngt.generation.price_scale_free(m, c=None, gamma=1, nodes=0, weighted=True, directed=True, seed_graph=None, multigraph=False, name='PriceSF', shape=None, positions=None, population=None, from_graph=None, **kwargs)[source]#

@todo make the algorithm.

Generate a Price graph model (Barabasi-Albert if undirected).

Parameters:

Parameters:	m (int) – The number of edges each new node will make. c (double) – Constant added to the probability of a vertex receiving an edge. gamma (double) – Preferential attachment power. nodes (int, optional (default: None)) – The number of nodes in the graph. weighted (bool, optional (default: True)) – @todo Whether the graph edges have weights. directed (bool, optional (default: True)) – Whether the graph is directed or not. multigraph (bool, optional (default: False)) – Whether the graph can contain multiple edges between two nodes. name (string, optional (default: “ER”)) – Name of the created graph. shape (`Shape`, optional (default: None)) – Shape of the neurons’ environment positions (`numpy.ndarray`, optional (default: None)) – A 2D or 3D array containing the positions of the neurons in space. population (`NeuralPop`, optional (default: None)) – Population of neurons defining their biological properties (to create a `Network`). from_graph (`Graph` or subclass, optional (default: None)) – Initial graph whose nodes are to be connected.
Returns:	graph_price (`Graph` or subclass.)

m (int) – The number of edges each new node will make.
c (double) – Constant added to the probability of a vertex receiving an edge.
gamma (double) – Preferential attachment power.
nodes (int, optional (default: None)) – The number of nodes in the graph.
weighted (bool, optional (default: True)) –

@todo

Whether the graph edges have weights.
directed (bool, optional (default: True)) – Whether the graph is directed or not.
multigraph (bool, optional (default: False)) – Whether the graph can contain multiple edges between two nodes.
name (string, optional (default: “ER”)) – Name of the created graph.
shape (Shape, optional (default: None)) – Shape of the neurons’ environment
positions (numpy.ndarray, optional (default: None)) – A 2D or 3D array containing the positions of the neurons in space.
population (NeuralPop, optional (default: None)) – Population of neurons defining their biological properties (to create a Network).
from_graph (Graph or subclass, optional (default: None)) – Initial graph whose nodes are to be connected.

Returns:

graph_price (Graph or subclass.)

Note

nodes is required unless from_graph or population is provided.

nngt.generation.newman_watts(coord_nb, proba_shortcut, nodes=0, weighted=True, directed=True, multigraph=False, name='NW', shape=None, positions=None, population=None, from_graph=None, **kwargs)[source]#

Generate a small-world graph using the Newman-Watts algorithm.

@todo: generate the edges of a circular graph to not replace the graph of the from_graph and implement chosen reciprocity.

Parameters:

Parameters:	coord_nb (int) – The number of neighbours for each node on the initial topological lattice. proba_shortcut (double) – Probability of adding a new random (shortcut) edge for each existing edge on the initial lattice. nodes (int, optional (default: None)) – The number of nodes in the graph. density (double, optional (default: 0.1)) – Structural density given by edges / (nodes``nodes). edges* (int (optional)) – The number of edges between the nodes avg_deg (double, optional) – Average degree of the neurons given by edges / nodes. weighted (bool, optional (default: True)) – @todo Whether the graph edges have weights. directed (bool, optional (default: True)) – Whether the graph is directed or not. multigraph (bool, optional (default: False)) – Whether the graph can contain multiple edges between two nodes. name (string, optional (default: “ER”)) – Name of the created graph. shape (`Shape`, optional (default: None)) – Shape of the neurons’ environment positions (`numpy.ndarray`, optional (default: None)) – A 2D or 3D array containing the positions of the neurons in space. population (`NeuralPop`, optional (default: None)) – Population of neurons defining their biological properties (to create a `Network`). from_graph (`Graph` or subclass, optional (default: None)) – Initial graph whose nodes are to be connected.
Returns:	graph_nw (`Graph` or subclass)

coord_nb (int) – The number of neighbours for each node on the initial topological lattice.
proba_shortcut (double) – Probability of adding a new random (shortcut) edge for each existing edge on the initial lattice.
nodes (int, optional (default: None)) – The number of nodes in the graph.
density (double, optional (default: 0.1)) – Structural density given by edges / (nodes`*`nodes).
edges (int (optional)) – The number of edges between the nodes
avg_deg (double, optional) – Average degree of the neurons given by edges / nodes.
weighted (bool, optional (default: True)) – @todo Whether the graph edges have weights.
directed (bool, optional (default: True)) – Whether the graph is directed or not.
multigraph (bool, optional (default: False)) – Whether the graph can contain multiple edges between two nodes.
name (string, optional (default: “ER”)) – Name of the created graph.
shape (Shape, optional (default: None)) – Shape of the neurons’ environment
positions (numpy.ndarray, optional (default: None)) – A 2D or 3D array containing the positions of the neurons in space.
population (NeuralPop, optional (default: None)) – Population of neurons defining their biological properties (to create a Network).
from_graph (Graph or subclass, optional (default: None)) – Initial graph whose nodes are to be connected.

Returns:

graph_nw (Graph or subclass)

Note

nodes is required unless from_graph or population is provided.