ModelDB: Surround Suppression in V1 via Withdraw of Balanced Local Excitation in V1 (Shushruth 2012)

Surround Suppression in V1 via Withdraw of Balanced Local Excitation in V1 (Shushruth 2012)

Accession: 144096

The model is mean-field network models, which is set up as a so-called ring-model, i. e. it is a highly idealized model of an orientation hypercolumn in primary visual cortex. Long-range intra-areal and inter-areal feedback connections are modeled phenomenologically as an external input. In this model, there are recurrent interactions via short-range local connections between orientation columns, but not between hypercolumns.
Reference: Shushruth S, Mangapathy P, Ichida JM, Bressloff PC, Schwabe L, Angelucci A (2012) Strong recurrent networks compute the orientation tuning of surround modulation in the primate primary visual cortex. J Neurosci 32:308-21 [PubMed]

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Model Information (Click on a link to find other models with that property)

Model Type:	Network;
Brain Region(s)/Organism:
Cell Type(s):	Neocortical pyramidal neuron: superficial; Neocortical basket cell;
Channel(s):
Gap Junctions:
Receptor(s):	GabaA; AMPA;
Gene(s):
Transmitter(s):	Gaba; Glutamate;
Simulation Environment:	MATLAB;
Model Concept(s):
Implementer(s):

Search NeuronDB for information about: Neocortical pyramidal neuron: superficial; Neocortical basket cell; GabaA; AMPA; Gaba; Glutamate;

Model files

Help downloading and running models

SimpleHypercolumnModel

Using these Matlab scripts, you can run the simulations of the model
and you can generate the figures showing the predicted surround surpression
for varying surround orientations.

1) Start Matlab
2) Navigate to the folder 'SimpleHypercolumnModel'
3) Execute the script 'main.m'

After a minute you should see a figure like the below:


The scripts shall be self-explanatory. In case of questions or comments, please
don't hesitate to contact me (lars.schwabe@uni-rostock.de).

If you use this code in your work and/or base any further development on it, please
give credit by citing our corresponding joint experimental-modeling work:

Strong recurrent networks compute the orientation tuning of surround modulation
in the primate primary visual cortex.
Shushruth S, Mangapathy P, Ichida JM, Bressloff PC, Schwabe L, Angelucci A.
J Neurosci. 2012 Jan 4;32(1):308-21.
PMID: 22219292

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