.. NNGT documentation master file
Welcome to NNGT's documentation!
.. image:: https://builds.sr.ht/~tfardet/nngt/commits.svg
.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.3402493.svg
The Neural Networks and Graphs' Topology (NNGT) module provides a unified
interface to access, generate, and analyze networks via any of the well-known
Python graph libraries: networkx_, igraph_, and `graph-tool`_.
For people in neuroscience, the library also 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 these three graph libraries,
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
NNGT provides four main classes.
The two first are aimed at the graph-theoretical community, the third and
fourth are more for the neuroscience community.
Additional details are provided on the :ref:`main_api` page.
provides a simple implementation to access and analyse topological graphs by
wrapping any graph object from other graph libraries.
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
combines spatial embedding and biological properties.
Generation of graphs
Structured graphs and connectivity:
connectivity between the nodes can be chosen from various well-known graph
models, specific groups and structures can be generated to simplify edge
populations of neurons can be used and be set to respect various constraints
(for instance a given fraction of inhibitory neurons), they simplify
network generation and make it highly efficient to interact with the NEST_
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.
:caption: User Documentation
Indices and tables
.. _`graph-tool`: http://graph-tool.skewed.de
.. _igraph: http://igraph.org/
.. _networkx: https://networkx.github.io/
.. _NEST: nest-simulator.readthedocs.io/