Simulated cortical color opponent receptive fields self-organize via STDP (Eguchi et al., 2014)

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Accession:152197
"... In this work, we address the problem of understanding the cortical processing of color information with a possible mechanism of the development of the patchy distribution of color selectivity via computational modeling. ... Our model of the early visual system consists of multiple topographically-arranged layers of excitatory and inhibitory neurons, with sparse intra-layer connectivity and feed-forward connectivity between layers. Layers are arranged based on anatomy of early visual pathways, and include a retina, lateral geniculate nucleus, and layered neocortex. ... After training with natural images, the neurons display heightened sensitivity to specific colors. ..."
Reference:
1 . Eguchi A, Neymotin SA and Stringer SM (2014) Color opponent receptive fields self-organize in a biophysical model of visual cortex via spike-timing dependent plasticity 8:16. doi: Front. Neural Circuits 8:16
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism: Neocortex; Thalamus; Retina;
Cell Type(s): Hodgkin-Huxley neuron;
Channel(s): I K; I Na, leak;
Gap Junctions:
Receptor(s): GabaA; AMPA;
Gene(s):
Transmitter(s): Gaba; Glutamate;
Simulation Environment: NEURON; Python;
Model Concept(s): Learning; STDP; Laminar Connectivity; Development; Information transfer; Sensory processing; Hebbian plasticity; Vision;
Implementer(s): Eguchi, Akihiro [akihiro.eguchi at psy.ox.ac.uk];
Search NeuronDB for information about:  GabaA; AMPA; I K; I Na, leak; Gaba; Glutamate;
This simulation was used in the following article:

  Eguchi A, Neymotin SA, Stringer SM. (2014)
  Color opponent receptive fields self-organize in a biophysical model
  of visual cortex via spike-timing dependent plasticity.
  Front. Neural Circuits 8:16. doi: 10.3389/fncir.2014.00016

For questions email: akihiro dot eguchi at psy dot ox dot ac dot uk

This simulation was tested/developed on LINUX systems, but may run on
Microsoft Windows or Mac OS.

To run, you will need the NEURON simulator (available at
http://www.neuron.yale.edu) compiled with python enabled. To draw the
output you will need to have Matplotlib installed (
http://matplotlib.org/ ).

Instructions:
 Unzip the contents of the zip file to a new directory.

 compile the mod files from the command line with:
  nrnivmodl *.mod

The nrnivmodl command will produce an architecture-dependent folder
with a script called special.  On 64 bit systems the folder is
x86_64. To run the simulation from the command line use:
 python runMe.py

Various parameters used in the simulation are set in the python codes.
State of the networks are exported and saved every n iterations as
"Network_"+str(itr)+".obj" format so that various analysis can be
applied to the network with specific point during the training using
runMe2.py script.

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