Modelling gain modulation in stability-optimised circuits (Stroud et al 2018)

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We supply Matlab code to create 'stability-optimised circuits'. These networks can give rise to rich neural activity transients that resemble primary motor cortex recordings in monkeys during reaching. We also supply code that allows one to learn new network outputs by changing the input-output gain of neurons in a stability-optimised network. Our code recreates the main results of Figure 1 in our related publication.
1 . Stroud JP, Porter MA, Hennequin G, Vogels TP (2018) Motor primitives in space and time via targeted gain modulation in cortical networks. Nat Neurosci 21:1774-1783 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Connectionist Network;
Brain Region(s)/Organism:
Cell Type(s): Abstract rate-based neuron;
Gap Junctions:
Receptor(s): M1;
Simulation Environment: MATLAB;
Model Concept(s): Learning;
Implementer(s): Stroud, Jake P [jp.stroud at]; Hennequin, Guillaume ; Vogels, Tim [tim.vogels at];
Search NeuronDB for information about:  M1;
function W = initialnet(N, p, R, gamma)

% Create random matrix where the left columns are excitatory neurons and 
% right columns are inhibitory neurons with zeros on the diagonal and 
% density p elsewhere.

NN = round(p*N*(N-1));
fill = [ones(1,NN), zeros(1,N*(N-1) - NN)];
fill = reshape(fill(randperm(N*(N-1))),N,N-1); %fill is an N by N-1 matrix with NN entries that are 1

W1 = zeros(N);
W1(1:end-1,2:end) = fill(1:end-1,:);

W2 = zeros(N);
W2(2:end,1:end-1) = fill(2:end,:);

W = triu(W1,1) + tril(W2,-1); %The off-diagonal elements of the matrix W are taken from the matrix fill 

%Create synaptic strengths as in Hennequin et al., Neuron, 2014.
w0 = sqrt(2)*R/(sqrt(p*(1-p)*(1 + gamma^2)));

W = W*(w0/sqrt(N)); %Excitatory synapses
W(:,(N/2 + 1):end) = -gamma*W(:,(N/2 +1):end); %Inhibitory synapses

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