Reverse-time correlation analysis for idealized orientation tuning dynamics (Kovacic et al. 2008)

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Accession:117514
"A theoretical analysis is presented of a reverse-time correlation method used in experimentally investigating orientation tuning dynamics of neurons in the primary visual cortex. An exact mathematical characterization of the method is developed, and its connection with the Volterra–Wiener nonlinear systems theory is described. Various mathematical consequences and possible physiological implications of this analysis are illustrated using exactly solvable idealized models of orientation tuning."
Reference:
1 . Kovacic G, Tao L, Cai D, Shelley MJ (2008) Theoretical analysis of reverse-time correlation for idealized orientation tuning dynamics. J Comput Neurosci 25:401-38 [PubMed]
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Model Information (Click on a link to find other models with that property)
Model Type:
Brain Region(s)/Organism: Neocortex;
Cell Type(s):
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: MATLAB;
Model Concept(s): Methods; Vision;
Implementer(s): Kovacic, Gregor [kovacg at rpi.edu];
%This is a code that computes the
%cartoon RTC function in a linear
%coupled network, with given coupling
%lengths. It also computes all
%the partial derivatives on all the
%parameters

clear all;

%LGN kernel time delay
T=3;

%LGN kernel time parameters
alpha=4;
beta=1/2;

%LGN kernel width
sigma=0.3;

%Cortical time delays
taue=0.2;
taui=0.8;

%Cortical coupling constants 
see=0.8;
sei=7.6;
sie=1.5;
sii=sei;

%cortical reversal and threshold potentials
VE=14/3;
VI=-2/3;
VT=1;

%Coupling constants in the linear model
cee=see*(VE-1);
cei=sei*(VI-1);
cie=sie*(VE-1);
cii=sii*(VI-1);

%Cortical kernel widths
ae=0.3;
ai=0.2;

%RTC time slot
nu=1;

%time interval
tmin=-1;
tmax=4;

%time discretization
Nt=10;
dt=(tmax-tmin)/Nt;

%angle discretization
Nth=50;
dth=pi/2/Nth;

%number of fourier modes
N=200;
%n=1:N;
n=N;

%initialize RTC function
Me=zeros(2*Nth+1,Nt+1);
Mi=zeros(2*Nth+1,Nt+1);
%initialize RTC partials
Met=zeros(2*Nth+1,Nt+1);
Mit=zeros(2*Nth+1,Nt+1);
Meth=zeros(2*Nth+1,Nt+1);
Mith=zeros(2*Nth+1,Nt+1);



%compute the RTC functions
for ii=1:2*Nth+1
for jj=1:Nt+1
	%
	tt=tmin+(jj-1)*dt;
	thh=-pi/2+(ii-1)*dth;
	%
	tth=[tt, thh];
	%
	%the RTC functions
	%
	Me(ii,jj)=rtccome(tth,n,ae,ai,sigma,cee,cie,cei,cii,taue,taui,T,alpha,beta,nu);
	%
	Mi(ii,jj)=rtccomi(tth,n,ae,ai,sigma,cee,cie,cei,cii,taue,taui,T,alpha,beta,nu);
	%
	%the RTC drivatives 
	%
	Met(ii,jj)=rtccomet(tth,n,ae,ai,sigma,cee,cie,cei,cii,taue,taui,T,alpha,beta,nu);
	%
	Meth(ii,jj)=rtccometh(tth,n,ae,ai,sigma,cee,cie,cei,cii,taue,taui,T,alpha,beta,nu);
	%
	Mit(ii,jj)=rtccomit(tth,n,ae,ai,sigma,cee,cie,cei,cii,taue,taui,T,alpha,beta,nu);
	%
	Mith(ii,jj)=rtccomith(tth,n,ae,ai,sigma,cee,cie,cei,cii,taue,taui,T,alpha,beta,nu);
	%
end;
end;

%time and angle grid
[t,th]=meshgrid(tmin:dt:tmax,-pi/2:dth:pi/2);

%plot the RTC functions
figure(1);
plot3(t,th,Me,'k');
xlabel('t');
ylabel('\theta');
zlabel('M_E');
stre=['RTC linear coupled model: '...
'\nu=',num2str(nu),', \alpha=',num2str(alpha), ...
', \beta=',num2str(beta),', \tau_{LGN} =',num2str(T),...
', \sigma_{LGN}=',num2str(sigma),', \tau_E=',num2str(taue),', \sigma_E=',num2str(ae)...
', s_{EE}=',num2str(see),', s_{EI}=',num2str(sei)];
title(stre);

figure(2);
plot3(t,th,Met,'k');
xlabel('t');
ylabel('\theta');
zlabel('M_{E,t}');
title(stre);

figure(3);
plot3(t,th,Meth,'k');
xlabel('t');
ylabel('\theta');
zlabel('M_{E,\theta}');
title(stre);

figure(4);
plot3(t,th,Mi,'k');
xlabel('t');
ylabel('\theta');
zlabel('M_I');
stri=['RTC linear coupled model: '...
'\nu=',num2str(nu),', \alpha=',num2str(alpha), ...
', \beta=',num2str(beta),', \tau_{LGN} =',num2str(T),...
', \sigma_{LGN}=',num2str(sigma),', \tau_I=',num2str(taui),', \sigma_I=',num2str(ai)...
', s_{IE}=',num2str(sie),', s_{II}=',num2str(sii)];
title(stri);

figure(5);
plot3(t,th,Mit,'k');
xlabel('t');
ylabel('\theta');
zlabel('M_{I,t}');
title(stri);

figure(6);
plot3(t,th,Mith,'k');
xlabel('t');
ylabel('\theta');
zlabel('M_{I,\theta}');
title(stri);