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.

The library has two main targets:

  • people looking for a unifying interface for the three main graph library, allowing to run and share a single code on different platforms
  • neuroscience people looking for an easy way to generate complex networks while keeping track of neuronal populations and their biological properties

Main classes#

NNGT provides four main classes, the two first being aimed at the graph-theoretical community, the third and fourth are more for the neuroscience community:

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 (nodes 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#