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demo_cx-lts
demo_cx_up-down
.gitignore
LICENSE.txt
README.md

README.md

Network simulations of self-sustained activity in networks of adaptive exponential integrate and fire neurons

Demo files implemented using both NEURON and PyNN.

demo_cx-lts

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.

Original NEURON version

This is the code originally written for the model by Alain Destexhe. To run:

cd demo_cx-lts
nrnivmodl ../demo_cx_up-down
nrngui demo_cx05_N=500b_LTS.oc

PyNN version

This is a conversion of the model to PyNN by Andrew Davison (davison@unic.cnrs-gif.fr). See http://andrewdavison.info/notes/porting-NEURON-PyNN/

This can be run on NEURON:

python demo_cx05_N=500b_LTS.py neuron 

but other PyNN backends should work also, e.g. NEST, Brian.

demo_cx_up-down

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.

Original NEURON version

This is the code originally written for the model by Alain Destexhe. To run:

cd demo_cx_up-down
nrnivmodl
nrngui demo_cxcx01b_N=2500_LTS.oc

PyNN version

This is a conversion of the model to PyNN by Lyle Muller (lyle.e.muller@gmail.com).

This can be run on NEURON:

python demo_cx_Up-Down.py neuron 

Publication

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