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Network simulations of self-sustained activity in networks of adaptive exponential integrate and fire neurons
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Demo files implemented using both NEURON and PyNN. The top level
folder (the folder with this README) python files will work with PyNN
version 0.8 and the python files in the prevPyNN subfolder will work
with PyNN version 0.7
demo_cx-lts
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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
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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 (NEURON version):
nrnivmodl
nrngui
Usage (Python version):
python
where is one of the demo files, and
is one of neuron, nest, pcsim, brian, facets_hardware2, etc...