LGNcircuit: Minimal LGN network model of temporal processing of visual input (Norheim et al. 2012)

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Accession:153092
The responses of relay cells in the lateral geniculate nucleus (LGN) are shaped by their diverse set of impinging inputs: feedforward synaptic inputs stemming from retina, and feedback inputs stemming from the visual cortex and the thalamic reticular nucleus. This MATLAB model, with an easy-to-use graphical user interface (GUI), explores possible roles of these feedforward and feedback inputs in shaping the temporal part of the receptive fields of LGN relay cells with, so called, ON symmetry. A minimal mechanistic firing-rate model tailored to elucidate salient feedforward and feedback effects is considered including, in particular, feedforward excitation and inhibition (via interneurons) from retinal ON cells and excitatory and inhibitory (via thalamic reticular nucleus cells and interneurons) feedback from cortical ON and OFF cells. Various types of visual stimuli can be explored: flashing spots, impulses, sinusoidal gratings.
Reference:
1 . Norheim ES, Wyller J, Nordlie E, Einevoll GT (2012) A minimal mechanistic model for temporal signal processing in the lateral geniculate nucleus. Cogn Neurodyn 6:259-81 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network; Neural mass;
Brain Region(s)/Organism:
Cell Type(s): Thalamus geniculate nucleus/lateral principal GLU cell;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: MATLAB;
Model Concept(s): Tutorial/Teaching; Rate-coding model neurons; Vision;
Implementer(s): Norheim, Eivind [norheim.eivind at gmail.com];
Search NeuronDB for information about:  Thalamus geniculate nucleus/lateral principal GLU cell;
function [t,r_g] = filterMyInput(lgn_struct,in_struct)

% FILTERMYINPUT - Function to evaluate the effective input to the LGN relay
% cell. The input should either be: 1. The ganglion cell firing rate, i.e.
%                                   the light stimulus filtered through 
%                                   the retinal circuit
%                                   2. The light stimulus + impulse
%                                   response of the (linear) retinal
%                                   circuit.

% unpack the parameters needed for the feedforward kernel and input
% parameters
eta_ffi = lgn_struct.eta_ffi;
tau_rg = lgn_struct.tau_rg;
tau_rig = lgn_struct.tau_rig;
Delta_rig = lgn_struct.Delta_rig;
form = in_struct.form;

% create feedforward kernel with respective time vector
tau_max = max([tau_rg tau_rig]);
n_ff = 1000;
t_ff_max = Delta_rig+4*tau_max;
dt = t_ff_max/(n_ff-1);

t_ff = 0:dt:t_ff_max;                                          % time vector
h_rg = 1/tau_rg*exp(-t_ff/tau_rg);                          % excitatory kernel
h_ffi = 1/tau_rig*exp(-(t_ff-Delta_rig)/tau_rig) .* (t_ff>=Delta_rig); % inhibitory kernel
h_ff=(h_rg - eta_ffi*h_ffi);

if strcmp(form,'vec')
    % unpack the input data and interpolate the vectors
    t_tmp = in_struct.t_in;
    r_tmp = in_struct.r_in;
    t_in=t_tmp(1):dt:t_tmp(end);
    r_in=interp1(t_tmp,r_tmp,t_in,'linear');
    clear t_tmp r_tmp
elseif strcmp(form,'fnc')
    h = in_struct.h;
    tstop = in_struct.tstop;
else
end

% 
r_g = (dt * conv(h_ff,r_in))';
t_fin=t_in(end)-t_in(1)+t_ff_max;
t=(0:dt:t_fin)';


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