Asynchronous irregular and up/down states in excitatory and inhibitory NNs (Destexhe 2009)

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Accession:126466
"Randomly-connected networks of integrate-and-fire (IF) neurons are known to display asynchronous irregular (AI) activity states, which resemble the discharge activity recorded in the cerebral cortex of awake animals. ... Here, we investigate the occurrence of AI states in networks of nonlinear IF neurons, such as the adaptive exponential IF (Brette-Gerstner-Izhikevich) model. This model can display intrinsic properties such as low-threshold spike (LTS), regular spiking (RS) or fast-spiking (FS). We successively investigate the oscillatory and AI dynamics of thalamic, cortical and thalamocortical networks using such models. ..."
Reference:
1 . Destexhe A (2009) Self-sustained asynchronous irregular states and Up-Down states in thalamic, cortical and thalamocortical networks of nonlinear integrate-and-fire neurons. J Comput Neurosci 27:493-506 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism: Neocortex;
Cell Type(s): Abstract integrate-and-fire adaptive exponential (AdEx) neuron;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: PyNN;
Model Concept(s): Spatio-temporal Activity Patterns;
Implementer(s):
  
/
demo_TCX
prevPyNN
README
demo_cx_Up-Down.py
demo_cx05_N=500b_LTS.py
LICENSE.txt
plot.py
                            
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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 <file.oc>

Usage (Python version):

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


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