Parameters: 
 scale (float) – Characteristic scale for the distance rule. E.g for linear distance
rule, , whereas for the
exponential distancerule, .
 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 ().
 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’ (), ‘mm’, ‘cm’,
‘dm’, ‘m’.
 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: “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.
