Welcome to NNGT’s documentation!¶

Graph container classes¶

class nngt.SpatialGraph(nodes=0, name='Graph', weighted=True, directed=True, from_graph=None, shape=None, positions=None, **kwargs)[source]

The detailed class that inherits from Graph and implements additional properties to describe various biological functions and interact with the NEST simulator.

Variables:	shape – Shape Shape of the neurons environment. positions – numpy.array Positions of the neurons. graph – GraphObject Main attribute of the class instance.

classmethod make_spatial(graph, shape=<nngt.core.graph_datastruct.Shape instance>, positions=None)[source]

position

shape

class nngt.Network(name='Graph', weighted=True, directed=True, from_graph=None, population=None, **kwargs)[source]

The detailed class that inherits from Graph and implements additional properties to describe various biological functions and interact with the NEST simulator.

Variables:	population – NeuralPop Object reparting the neurons into groups with specific properties. graph – GraphObject Main attribute of the class instance nest_gid – numpy.array Array containing the NEST gid associated to each neuron; it is None until a NEST network has been created. id_from_nest_gid – dict Dictionary mapping each NEST gid to the corresponding neuron index in the nngt.~Network

classmethod ei_network(size, ei_ratio=0.2, en_model='aeif_cond_alpha', en_param={}, es_model='static_synapse', es_param={}, in_model='aeif_cond_alpha', in_param={}, is_model='static_synapse', is_param={})[source]

Generate a network containing a population of two neural groups: inhibitory and excitatory neurons.

Parameters:

size (int) – Number of neurons in the network. ei_ratio (double, optional (default: 0.2)) – Ratio of inhibitory neurons: \(\frac{N_i}{N_e+N_i}\). en_model (string, optional (default: ‘aeif_cond_alpha’)) – Nest model for the excitatory neuron. en_param (dict, optional (default: {})) – Dictionary of parameters for the the excitatory neuron. es_model (string, optional (default: ‘static_synapse’)) – NEST model for the excitatory synapse. es_param (dict, optional (default: {})) – Dictionary containing the excitatory synaptic parameters. in_model (string, optional (default: ‘aeif_cond_alpha’)) – Nest model for the inhibitory neuron. in_param (dict, optional (default: {})) – Dictionary of parameters for the the inhibitory neuron. is_model (string, optional (default: ‘static_synapse’)) – NEST model for the inhibitory synapse. is_param (dict, optional (default: {})) – Dictionary containing the inhibitory synaptic parameters.

Returns:

net (Network or subclass) – Network of disconnected excitatory and inhibitory neurons.

static make_network(graph, neural_pop)[source]

Turn a Graph object into a Network, or a SpatialGraph into a SpatialNetwork.

Parameters:	graph (Graph or SpatialGraph) – Graph to convert neural_pop (NeuralPop) – Population to associate to the new Network

Notes

In-place operation that directly converts the original graph.

nest_gid

neuron_properties(idx_neuron)[source]

Properties of a neuron in the graph.

Parameters:	idx_neuron (int) – Index of a neuron in the graph.
Returns:	dict of the neuron properties.

classmethod num_networks()[source]

Returns the number of alive instances.

population

NeuralPop that divides the neurons into groups with specific properties.

classmethod uniform_network(size, neuron_model='aeif_cond_alpha', neuron_param={}, syn_model='static_synapse', syn_param={})[source]

Generate a network containing only one type of neurons.

Parameters:

size (int) – Number of neurons in the network. neuron_model (string, optional (default: ‘aief_cond_alpha’)) – Name of the NEST neural model to use when simulating the activity. neuron_param (dict, optional (default: {})) – Dictionary containing the neural parameters; the default value will make NEST use the default parameters of the model. syn_model (string, optional (default: ‘static_synapse’)) – NEST synaptic model to use when simulating the activity. syn_param (dict, optional (default: {})) – Dictionary containing the synaptic parameters; the default value will make NEST use the default parameters of the model.

Returns:

net (Network or subclass) – Uniform network of disconnected neurons.

class nngt.SpatialNetwork(population, name='Graph', weighted=True, directed=True, shape=None, from_graph=None, positions=None, **kwargs)[source]

Class that inherits from Network and SpatialGraph to provide a detailed description of a real neural network in space, i.e. with positions and biological properties to interact with NEST.

Variables:

shape – nngt.core.Shape Shape of the neurons environment. positions – numpy.array Positions of the neurons. population – NeuralPop Object reparting the neurons into groups with specific properties. graph – GraphObject Main attribute of the class instance. nest_gid – numpy.array Array containing the NEST gid associated to each neuron; it is None until a NEST network has been created. id_from_nest_gid – dict Dictionary mapping each NEST gid to the corresponding neuron index in the nngt.~SpatialNetwork

Side classes¶

class nngt.Shape(parent=None)[source]¶

Class containing the shape of the area where neurons will be distributed to form a network.

..warning :

so far, only a rectangle can be created.

area¶

double

Area of the shape in mm^2.

com¶

tuple of doubles

Position of the center of mass of the current shape.

add_subshape: void

@todo Add a Shape to a preexisting one.

add_subshape(subshape, position, unit='mm')[source]¶

Add a Shape to the current one.

Parameters:	subshape (Shape) – Subshape to add. position (tuple of doubles) – Position of the subshape’s center of gravity in space. unit (string (default ‘mm’)) – Unit in the metric system among ‘um’, ‘mm’, ‘cm’, ‘dm’, ‘m’
Returns:	None

area

Area of the shape.

com

Center of mass of the shape.

classmethod rectangle(parent, height, width, pos_com=(0.0, 0.0))[source]¶

Generate a rectangle of given height, width and center of mass.

Parameters:	parent (SpatialGraph or subclass) – The parent container. height (float) – Height of the rectangle. width (float) – Width of the rectangle. pos_com (tuple of floats, optional (default: (0., 0.))) – Position of the rectangle’s center of mass
Returns:	shape (Shape) – Rectangle shape.

rnd_distrib(nodes=None)[source]¶

set_parent(parent)[source]¶

vertices¶

class nngt.NeuralPop(size=None, parent=None, with_models=True, **kwargs)[source]¶

The basic class that contains groups of neurons and their properties.

Variables:	has_models – bool, True if every group has a model attribute.

add_to_group(group_name, id_list)[source]¶

classmethod copy(pop)[source]¶

Copy an existing NeuralPop

classmethod ei_population(size, iratio=0.2, parent=None, en_model='aeif_cond_alpha', en_param={}, es_model='static_synapse', es_param={}, in_model='aeif_cond_alpha', in_param={}, is_model='static_synapse', is_param={})[source]¶

Make a NeuralPop with a given ratio of inhibitory and excitatory neurons.

has_models¶

is_valid¶

new_group(name, id_list, ntype=1, neuron_model=None, neuron_param={}, syn_model='static_synapse', syn_param={})[source]¶

next()[source]¶

classmethod pop_from_network(graph, *args)[source]¶

Make a NeuralPop object from a network. The groups of neurons are determined using instructions from an arbitrary number of GroupProperties.

set_model(model, group=None)[source]¶

Set the groups’ models.

Parameters:

model (dict) – Dictionary containing the model type as key (“neuron” or “synapse”) and the model name as value (e.g. {“neuron”: “iaf_neuron”}). group (list of strings, optional (default: None)) – List of strings containing the names of the groups which models should be updated. .. warning (:) – No check is performed on the validity of the models, which means that errors will only be detected when building the graph in NEST. .. note (:) – By default, synapses are registered as “static_synapse”s in NEST; because of this, only the neuron_model attribute is checked by the has_models function: it will answer True if all groups have a ‘non-None’ neuron_model attribute.

set_param(param, group=None)[source]¶

Set the groups’ parameters.

Parameters:

param (dict) – Dictionary containing the model type as key (“neuron” or “synapse”) and the model parameter as value (e.g. {“neuron”: {“C_m”: 125.}}). group (list of strings, optional (default: None)) – List of strings containing the names of the groups which models should be updated. .. warning (:) – No check is performed on the validity of the parameters, which means that errors will only be detected when building the graph in NEST.

size¶

classmethod uniform_population(size, parent=None, neuron_model='aeif_cond_alpha', neuron_param={}, syn_model='static_synapse', syn_param={})[source]¶

Make a NeuralPop of identical neurons

Core module¶

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

Generation module¶

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

Simulation module¶

Module to interact easily with the NEST simulator. It allows to :

• build a NEST network from Network or SpatialNetwork objects,
• monitor the activity of the network (taking neural groups into account)
• plot the activity while separating the behaviours of predefined neural groups

Plot module¶

Functions for plotting graphs and graph properties.

note ::

For now, graph plotting is only supported when using the graph_tool library.

Analysis module¶

Lib module¶

Properties module¶

So far it contains only NeuralGroup... I’m thinking about moving it to core.