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

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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:
1 . 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: Realistic Network;
Brain Region(s)/Organism:
Cell Type(s): Neocortex L2/3 pyramidal GLU cell; Neocortex V1 interneuron basket PV GABA cell;
Channel(s):
Gap Junctions:
Receptor(s): GabaA; AMPA;
Gene(s):
Transmitter(s): Gaba; Glutamate;
Simulation Environment: MATLAB;
Model Concept(s): Vision;
Implementer(s):
Search NeuronDB for information about:  Neocortex L2/3 pyramidal GLU cell; Neocortex V1 interneuron basket PV GABA cell; GabaA; AMPA; Gaba; Glutamate;
ModelParams.N = 32;

ModelParams.TAU_E = 0.010;
ModelParams.TAU_B = 0.006;

ModelParams.KAPPA_REC_BSK = 0.2;
ModelParams.KAPPA_REC_EXC = 0.2;
ModelParams.KAPPA_FF  = 0.5;
ModelParams.KAPPA_MOD = 0.5;

W = 20;
K = 1.2;
ModelParams.W_EE = W;
ModelParams.W_BE = W;
ModelParams.W_EB = -K*W;
ModelParams.W_BB = -K*W;
ModelParams.W_ES = 0;
ModelParams.W_BS = 0.03;

% Parameters for the neuronal populations
ModelParams.GE = 1;         % gain of exc neurons
ModelParams.GB = 1;         % gain of bsk neurons

ModelParams.TH_E = 0.5;     % threshold of exc neurons
ModelParams.TH_B = 0.5;     % threshold of bsk neurons