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

 Download zip file 
Help downloading and running models
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_r, t_zero] = evaluate_dde(lgn_struct, in_struct)

% EVALUATE_DDE - Function to evaluate a linear/nonlinear DDE with a
% given drive function. This function is only needed for feedback models. 
%
% Input parameters are: 
%   in_struct - struct with fields:
%          form: 'vec', 'fnc' or 'ilfnc'
%          t_in: column vector
%          r_in: column vector
%           t_g: column vector
%           r_g: column vector
%
%   lgn_struct - struct with fields and default values 
%       eta_ffi: 0.5000
%        tau_rg: 10
%       tau_rig: 50
%     Delta_rig: 0
%        w_fbON: -1.2000
%      w_fbOFFx: 1.2000
%        tau_rc: 10
%      Delta_fb: 0
%         l_cON: 0
%        l_cOFF: 0
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

  % Extracting parameters
  w_fbON = lgn_struct.w_fbON;
  w_fbOFFx = lgn_struct.w_fbOFFx;
  tau_rc = lgn_struct.tau_rc;
  Delta_fb = lgn_struct.Delta_fb;
  l_cON = lgn_struct.l_cON;
  l_cOFF = lgn_struct.l_cOFF;
  % Extracting input/misc parameters
  if isfield(in_struct,'mode')
      mode = in_struct.mode;
  else
      mode='N/A';
  end
  form = in_struct.form;
  if strcmp(form,'vec')
      t_g = in_struct.t_g;
      r_g = in_struct.r_g;
  elseif strcmp(form,'fnc')
      h = in_struct.h;
%      B = in_struct.B;
  end  
  
  if isfield(in_struct,'tstop')
      tstop=in_struct.tstop;
  else
      tstop = t_g(end);
  end
    
  tspan = [0,tstop];

  % Setting some options for the DDE-solver. The 'MaxStep' option is very
  % demanding. The options 'RelTol' and 'AbsTol' are less demanding.
  
  opt = ddeset('RelTol',1e-4,'MaxStep', 1,'Events',@events);
    
  % opt = ddeset('InitialStep',0.05,);

  % Calling the DDE-solver with the nested function 'dde_eval' and history 
  % 'dde_history'. 

  sol = dde23(@dde_eval, Delta_fb, @dde_history, tspan, opt);

  % Extracting values from the 'sol' struct.

  t = (sol.x)'; % Time
  z = (sol.y)'; % Feedback contributions to the LGN firing rate

  % The feedforward contribution to the LGN firing rate is an analytical
  % expression contained in 'ff_contrib'.
  
  if strcmp(form,'vec')
      r_bar = interp1(t_g,r_g,t,'linear');
  elseif strcmp(form,'fnc')
      r_bar = feval(h, lgn_struct, in_struct, t);
  end
          
  %r_bar = feval(@ff_contrib, ff_params, B, t_out);

  % r_r is the LGN relay cell response, which is the sum of feedforward
  % (r_bar) and feedback (z) contributions. 

  r_r = z + r_bar;
  t_zero=crossings;
  
  if strcmp(mode,'script')
      % If the response shows oscillatory tendencies -> simulate for a
      % longer time
      if length(t_zero) >=3
          i_bound = length(t);      % This is the last index in the previous solution
          tspan = [tstop,2*tstop];
          sol = dde23(@dde_eval, Delta_fb, sol, tspan, opt);

          t = (sol.x)';          
          z = (sol.y)';
          t(i_bound)=[];   % delete double entry
          z(i_bound)=[];   % delete double entry
          r_bar = feval(h, lgn_struct, in_struct, t);
          r_r = z+r_bar;
          t_zero = crossings;
      end
  end

% ----------- EVENTS -----------

    function [value,isterminal,direction] = events(t,y,Z) %#ok<INUSD>
        % Nested function to determine events.
        
        if strcmp(form,'vec')
            r_bar = interp1(t_g,r_g,t,'linear');
        elseif strcmp(form,'fnc')
            r_bar = feval(h, lgn_struct, in_struct, t);
        end
        
        value = y + r_bar;
        isterminal = 0; % do not break
        direction = 0; % all zeros are to be computed     
        
    end

% --------- CROSSINGS -------------

    function t_cross = crossings       
        t_cross = sol.xe;
        k = 0;        
        for i = 1:length(t_cross)    
            if t_cross(i) > 1
                k = k+1;
                t_cross(k) = t_cross(i);        
            end    
        end
        t_cross((k+1):i) = [];        
    end

% -------------- History-function ---------------
  function s = dde_history(t)

  % DDE_HISTORY - function which defines the history, in general this is the background firing rate

    s = 0;

  end


% ------------- Evaulation of DDE --------------

  function dzdt = dde_eval(t,z,z_lag)

  % DDE_EVAL - This function defines the model
    
    % Updating the waitbar
    if strcmp('mode','gui')
        waitbar(t/tstop)
    end
    
    % Extracting parameter values    
    
    %Delta_fb = lgn_struct.Delta_fb;
       
    % Setting lagged time
    u = t - Delta_fb;

    % -- Assign value to the retinal input ---------
    
    if strcmp(form,'vec')
        r_bar_lag = interp1(t_g,r_g,u,'linear','extrap');
    elseif strcmp(form,'fnc')
        r_bar_lag = feval(h, lgn_struct, in_struct, u);
    end        

  %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%       
  %
  % This part defines the model in use
  % 
  %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
          
    % This is the normal model. If w_fbOFFx = w_fbON and l_cOFF = -l_cON, the
    % feedback is linear. 
                
    if (r_bar_lag + z_lag - l_cON) >= 0
        
        dzdt = 1/tau_rc*(w_fbON*(r_bar_lag + z_lag - l_cON) - z);
        
    elseif ( -(r_bar_lag + z_lag - l_cOFF)) >= 0
        
        dzdt = 1/tau_rc*(w_fbOFFx*(l_cOFF - (r_bar_lag + z_lag)) - z);
        
    else
        dzdt = -z/tau_rc;
    end
  end
end

Loading data, please wait...