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Neun

Boost v1.45 dependency CMake v2.8 dependency Build Status

A library for the simulation of dynamical systems, aimed to model neural networks with high performance. It is written in an object oriented fashion with heavily templated C++.

Forked from: https://code.launchpad.net/~elferdo/neun/trunk

Build

To build it, just type:

mkdir build && cd build
cmake ..

Install it using:

make install

The library will install a pkg-config file called "neun.pc" under ${prefix}/${project_name}/${project_version}/pkgconfig. If you want other applications to be able to find it, you must add this directory to your PKG_CONFIG_PATH

Usage

In order to perform any simulation first you need to define the numerical integrator you are going to use, e.g.:

typedef RungeKutta4 Integrator;

Then, you define the neuron model and the precision of the simulation, e.g.:

typedef HodgkinHuxleyModel<double> HHModel;

Finally, you wrapp the model and the numerical integrator to build an integrable dynamical system, e.g.:

typedef DifferentialNeuronWrapper<HHModel, Integrator> Neuron;

Integrators

Currently implemented integrators are:

  • Stepper
  • Euler
  • RungeKutta4
  • RungeKutta6

Neuron models

Currently implemented neuron models:

  • Hodgkin-Huxley conductance model (Hodgkin and Huxley, 1952)
  • Hindmarsh–Rose model (Hindmarsh-Rose, 1984)
  • Izhikevich spiking neuron model (Izhikevich, 2003)
  • Simple oscillator
  • Matsuoka oscillator (Matsuoka, 1985)
  • Rowat and Selverston (Rowat and Selverston, 1997)
  • Rulkov Map model (Nikolai F. Rulkov, 2002)
  • Bistable Rulkov Map model (Nikolai F. Rulkov, 2002)
  • Vavoulis model (Vavoulis et al., 2007)

Synapsis models

Currently implemented synapsis models are:

  • Diffusion synapsis (Destexhe et al. 1994)
  • Electrical synapsis
  • Conductance-based direct synapsis
  • Sigmoidal direct synapsis