Welcome to NNGT’s documentation!

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The Neural Network Growth and Topology (NNGT) module provides tools to grow and study detailed biological networks by interfacing efficient graph libraries with highly distributed activity simulators.

Main classes

NNGT uses four main classes:

provides a simple implementation over graphs objects from graph libraries (namely the addition of a name, management of detailed nodes and connection properties, and simple access to basic graph measurements).
a Graph embedded in space (neurons have positions and connections are associated to a distance)
provides more detailed characteristics to emulate biological neural networks, such as classes of inhibitory and excitatory neurons, synaptic properties...
combines spatial embedding and biological properties

Generation of graphs

Structured connectivity:
connectivity between the nodes can be chosen from various well-known graph models
populations of neurons are distributed afterwards on the structured connectivity, and can be set to respect various constraints (for instance a given fraction of inhibitory neurons and synapses)
Synaptic properties:
synaptic weights and delays can be set from various distributions or correlated to edge properties

Interacting with NEST

The generated graphs can be used to easily create complex networks using the NEST simulator, on which you can then simulate their activity.

Indices and tables