Olfactory bulb microcircuits model with dual-layer inhibition (Gilra & Bhalla 2015)

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Accession:153574
A detailed network model of the dual-layer dendro-dendritic inhibitory microcircuits in the rat olfactory bulb comprising compartmental mitral, granule and PG cells developed by Aditya Gilra, Upinder S. Bhalla (2015). All cell morphologies and network connections are in NeuroML v1.8.0. PG and granule cell channels and synapses are also in NeuroML v1.8.0. Mitral cell channels and synapses are in native python.
Reference:
1 . Gilra A, Bhalla US (2015) Bulbar microcircuit model predicts connectivity and roles of interneurons in odor coding. PLoS One 10:e0098045 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism: Olfactory bulb;
Cell Type(s): Olfactory bulb main mitral GLU cell; Olfactory bulb main interneuron periglomerular GABA cell; Olfactory bulb main interneuron granule MC GABA cell;
Channel(s): I A; I h; I K,Ca; I Sodium; I Calcium; I Potassium;
Gap Junctions:
Receptor(s): AMPA; NMDA; Gaba;
Gene(s):
Transmitter(s): Gaba; Glutamate;
Simulation Environment: Python; MOOSE/PyMOOSE;
Model Concept(s): Sensory processing; Sensory coding; Markov-type model; Olfaction;
Implementer(s): Bhalla, Upinder S [bhalla at ncbs.res.in]; Gilra, Aditya [aditya_gilra -at- yahoo -period- com];
Search NeuronDB for information about:  Olfactory bulb main mitral GLU cell; Olfactory bulb main interneuron periglomerular GABA cell; Olfactory bulb main interneuron granule MC GABA cell; AMPA; NMDA; Gaba; I A; I h; I K,Ca; I Sodium; I Calcium; I Potassium; Gaba; Glutamate;
function matlab_fit_pulses()

    kernels = [];
    fitted_mitrals = [0 1];
    % ========================================================================
    figure;
    hold on;
    for fitted_mitral = fitted_mitrals
        py = load(strcat('mit',int2str(fitted_mitral),'.mat'));

        for odornum = [0 1];
            % NOTE: matlab indices start with 1,
            % so careful with pulsenums and start_i (python-convention)
            if odornum==0
                pulsenums = [3 5];
            else
                pulsenums = [4 6];
            end

            % matlab cannot add int64-s, so convert to int32
            starti = int32(py.start_i)+1;
            ydata = py.firingbinsmeanList(pulsenums-1,starti:end);
            len_data = size(ydata,2);
            xdata = py.pulseList(pulsenums,starti:starti+len_data-1);
            start_kernel = py.start_kernels(odornum+1,:);
            %start_kernel = zeros(size(start_kernel));

            % Copied from Priyanka
            % ========================================================================
            % initialize and define the optimization function
            % ========================================================================
            Eval_max = 1e+6; Iter_max = 1e+6; lb = []; ub = [];
            options = optimset('MaxFunEvals',Eval_max,'MaxIter',Iter_max,...
                    'TolFun',1e-15,'TolX',1e-15);
            [kernel,resnorm,residual,exitflag] = ...
                    lsqcurvefit(@conv_adi,start_kernel,...
                    xdata,ydata,lb,ub,options);
            % normalize chi-sq to the number of dof-s
            num_dof = prod(size(ydata)) - prod(size(kernel));
            resnorm/num_dof
            exitflag
            % extra iteration seems to make no difference in resnorm
            %randnoise = rand(size(kernel))*5;
            %[kernel,resnorm,residual,exitflag] = ...
            %        lsqcurvefit(@conv_adi,kernel.*randnoise,...
            %        xdata,ydata,lb,ub,options);
            %resnorm
            %exitflag

            subplot(3,2,odornum*2+fitted_mitral+1);
            hold on;
            % plot only the 1st pulselist & response for this odor
            plot(xdata(1,:)*20,'g-');
            plot(ydata(1,:),'b-');
            [fit] = conv_adi(kernel,xdata(1,:));
            plot(fit,'r-');
            %[origfit] = conv_adi(start_kernel,xdata(1,:));
            %plot(origfit,'m-');

            kernels = [kernels; kernel];
        end
        
        subplot(3,2,5+fitted_mitral);
        hold on;
        AplusBresponse = py.firingbinsmeanList(6,starti:end);
        Apulse = py.pulseList(7,starti:starti+len_data-1);
        Bpulse = py.pulseList(8,starti:starti+len_data-1);
        AplusBpulse = Apulse+Bpulse;
        plot(AplusBpulse*20,'g-');
        plot(AplusBresponse,'b-');
        % bgnd gets added twice by conv_adi, so subtract once.
        [fit] = conv_adi(kernels(fitted_mitral*2+1,:),Apulse) ...
            + conv_adi(kernels(fitted_mitral*2+2,:),Bpulse) - py.bgnd;
        plot(fit,'r-');

    end
    
    % plot kernels
    % savitsky golay filtering (operates on col-s, hence ')
    kernels = sgolayfilt(kernels',4,11)'
    figure(2);
    subplot(2,1,1);
    plot(kernels(1,:),'r-');
    hold on;
    plot(kernels(3,:),'b-');
    subplot(2,1,2);
    plot(kernels(2,:),'r-');
    hold on;
    plot(kernels(4,:),'b-');

    % partly from Priyanka
    % ========================================================================
    % convolution and fit generation for optimization
    % ========================================================================
    function [fit] = conv_adi(kernel,xdata)
        fit = [];
        % convolve for each pulse data
        for i = 1:size(xdata,1)
            M = [];
            M = conv(kernel,xdata(i,:));
            M = M(1:len_data).*py.pulserebindt;
            M = M + py.bgnd;
            fit(i,1:length(M)) = M;
        end
        fit(fit<0) = 0;
    end

end

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