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):
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demo_TCX
prevPyNN
README
demo_cx_Up-Down.py
demo_cx05_N=500b_LTS.py
LICENSE.txt
plot.py
                            
Copyright (c) 2019-2012, Alain Destexhe, Andrew Davison and Lyle Muller
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