Network simulations of self-sustained activity in networks of
Adaptive Exponential integrate and fire neurons

Demo files implemented using PyNN, and which can run on NEURON
or NEST simulators.


demo_cx05_N=500b_LTS.py: 

Simulations of self-sustained AI states in a small N=500 network of
excitatory and inhibitory neurons, described by Adaptive
Exponential (Brette-Gerstner-Izhikevich) type neurons with
exponential approach to threshold.  The connectivity is random and
there is a small proportion (5%) of LTS cells among the excitatory
neurons.  This simulation reproduces Fig. 7 of the paper below.

demo_cx_Up-Down.py: 

Simulations of Up-Down states in a two-layer cortical network, with
one N=2000 network and a smaller N=500 network.  Both networks have
excitatory and inhibitory neurons described by Adaptative
Exponential (Brette-Gerstner-Izhikevich) type neurons with
exponential approach to threshold.  The connectivity is random
within each network as well as between them.  In the N=500 network,
there is a small proportion (5%) of LTS cells among the excitatory
neurons.  This simulation reproduces Fig. 13 of the paper below.

See details in the following article:

Destexhe, A. Self-sustained asynchronous irregular states and
Up/Down states in thalamic, cortical and thalamocortical networks
of nonlinear integrate-and-fire neurons.  Journal of Computational
Neuroscience 27: 493-506, 2009. 

arXiv preprint: http://arxiv.org/abs/0809.0654

Original NEURON implementation by Alain Destexhe
    destexhe@unic.cnrs-gif.fr
    http://cns.iaf.cnrs-gif.fr
    
Converted to PyNN by Andrew Davison
    davison@unic.cnrs-gif.fr
and Lyle Muller
    lyle.e.muller@gmail.com

Usage:

    python <file> <simulator>
    
where <file> is one of the demo files listed above, and <simulator>
is one of neuron, nest, pcsim, brian, facets_hardware2, etc...