Laminar connectivity matrix simulation (Weiler et al 2008)

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Accession:114655
A routine that simulates the flow of activity within and across laminar levels in the local pyramidal neuron network, based on a connectivity matrix (W) measured by laser scanning photostimulation in mouse somatic motor cortex, and a very simple neural network simulation.
Reference:
1 . Weiler N, Wood L, Yu J, Solla SA, Shepherd GM (2008) Top-down laminar organization of the excitatory network in motor cortex. Nat Neurosci 11:360-6 [PubMed]
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 L5/6 pyramidal GLU cell; Neocortex L2/3 pyramidal GLU cell;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: MATLAB;
Model Concept(s): Activity Patterns; Connectivity matrix; Laminar Connectivity;
Implementer(s): Shepherd, Gordon MG [g-shepherd at northwestern.edu];
Search NeuronDB for information about:  Neocortex L5/6 pyramidal GLU cell; Neocortex L2/3 pyramidal GLU cell;
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laminarWsimulation
readme.html
laminarWsimulation.m
screenshot.jpg
                            
function laminarWsimulation(gain, W, inputvector, iterations)
% laminarWsimulation
%
% Simulates the flow of activity within and across laminar levels in the 
% local pyramidal neuron network, based on a connectivity matrix (W) 
% measured by laser scanning photostimulation.
%
% The simulation is a simple neural network simulation:
%
% p(n) ----> c * W ----> p(n+1)
%
% where p(n=0) is an input vector ('inputvector') representing a laminar 
% profile of activity, c is a global scaling constant ('gain'), W is a 
% measured connectivity matrix, and p(n+1) is an output vector, which is 
% fed back into the network as the input vector for the next iteration.
%
% For further details, see Weiler et al., 2008, Nature Neuroscience.

% 2008 Gordon Shepherd, Northwestern University
% ---------------------------------------------------------------------

if nargin == 0
    % default values:
    gain = 1; % modify to change global 'excitability'
    W = getW('mouse somatic M1');
    inputvector = zeros(size(W,1), 1)';
    inputvector(1) = 1; % modify to change laminar input pattern
    iterations = 10;
end

for I = 1 : iterations
    inputvector(I + 1, :) = inputvector(I, :) * W * gain;
end

figure('Color', 'w');
imagesc(inputvector')
xlabel('Iteration (number)')
ylabel('Laminar level (cortical depth)')
title('Simulated flow of network activity')
colorbar

function out = getW(datasetname)
switch datasetname
    case 'mouse somatic M1'
        out = [
            0.1720    0.1036    0.2159    1.0000    0.4482    0.0710    0.0312    0.0147    0.0044
            0.0885    0.0937    0.1043    0.2385    0.2511    0.0431    0.0227   -0.0003    0.0016
            0.1181    0.1545    0.0659    0.0768    0.1320    0.0442    0.0463    0.0112   -0.0050
            0.1193    0.0668    0.0520    0.1136    0.1011    0.0636    0.0574    0.0283    0.0039
            0.0344    0.0407    0.0347    0.0764    0.1104    0.0834    0.1087    0.0434    0.0053
            0.0021    0.0186    0.0142    0.0428    0.1195    0.1198    0.1328    0.0591    0.0113
            -0.0028    0.0028    0.0182    0.0266    0.1068    0.1327    0.1074    0.0599    0.0247
            0.0007    0.0013    0.0198    0.0204    0.0621    0.1151    0.1268    0.0673    0.0321
            0.0067    0.0031    0.0071    0.0061    0.0249    0.0289    0.0409    0.0502    0.0384
            ];
end

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