Olfactory bulb network: neurogenetic restructuring and odor decorrelation (Chow et al. 2012)

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Accession:146583
Adult neurogenesis in the olfactory bulb has been shown experimentally to contribute to perceptual learning. Using a computational network model we show that fundamental aspects of the adult neurogenesis observed in the olfactory bulb -- the persistent addition of new inhibitory granule cells to the network, their activity-dependent survival, and the reciprocal character of their synapses with the principal mitral cells -- are sufficient to restructure the network and to alter its encoding of odor stimuli adaptively so as to reduce the correlations between the bulbar representations of similar stimuli. The model captures the experimentally observed role of neurogenesis in perceptual learning and the enhanced response of young granule cells to novel stimuli. Moreover, it makes specific predictions for the type of odor enrichment that should be effective in enhancing the ability of animals to discriminate similar odor mixtures. NSF grant DMS-0719944.
Reference:
1 . Chow SF, Wick SD, Riecke H (2012) Neurogenesis drives stimulus decorrelation in a model of the olfactory bulb. PLoS Comput Biol 8:e1002398 [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 granule MC GABA cell;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: MATLAB;
Model Concept(s): Activity Patterns; Rate-coding model neurons; Sensory processing; Apoptosis; Neurogenesis; Olfaction;
Implementer(s): Chow, Siu-Fai ;
Search NeuronDB for information about:  Olfactory bulb main mitral GLU cell; Olfactory bulb main interneuron granule MC GABA cell;
function main
    neurogenesis(0);
end % main 

function objfun = neurogenesis(run_num)

% parameters
    % output
        TEXT_OUT = 1;
        PLOT_OUT = 1; ANIME_OUT = 1;
        PLOT2D_OUT = 1; ANIME2D_OUT = 0;
    % stimulus
        sim = 40;
        odor_names = 'limonene(+)_ster limonene(_)_ster propylpropionate_es3 ethylbutyrate_es3 isopropylbenzene_ModuleC1 cyclohexanone_SG18 acetone methylacetate_SG19 cycloheptanelow_cycloalk propanol_simp_2500 isoamylbutyrate_est1 butyricacid_aci1 hexanal_ald1 ethylbenzene_HC';
        choose = 1:14;
    % network
        non_lin = 0;
        conn = 4;
        CS = 0.002;
    % survival
        ts = 0.1;
        gamma = 5/ts;
        th = 0.2;
        rm = 0;
        rg = 0;
    % stepping
        cont_density = 0;
        exp_time = 5000; step = round(exp_time/100); dt = 5;
    % tracking vs T
        tracking = 1; % 0: none, 1: in S
        track_pairs = [1 2; 3 4];
        
    % ...
        S0 = 1; Sstr = 1; MC_per_Glom = 1;
        TYPE = 1; % 1: pearson 2: L2
        prob_conn = 0; % only for cont_density = 0
        Na = 1;
        perm_ratio = 0; % only for cont_density = 0
        minv = 0; maxv = 1;
        
% setup stimulus
        
% setup stimulus
    if TEXT_OUT == 1
        fprintf('\n--- Start ---\n   - initializing odor\n');
    end
    [Sall, coord, metric, name] = gara(14, MC_per_Glom, odor_names, sim);
    Nc = size(Sall,1);
    Ns = length(choose);
    S = S0+Sstr*(Sall(:,choose));
    S_name = cell(1,size(S,2));
    for i = 1:size(S,2)
        S_name{i} = name{choose(i)};
    end
    
    if TEXT_OUT == 1
        fprintf('       - Nc = %d\n', Nc);
        fprintf('           corr = %f\n', mean_excluNaN(uptri_1d(corr(S,TYPE))));
    end
    if PLOT_OUT == 1;
        setup_Pplot(S,corr(S,TYPE),corr(S',TYPE),rg,1);
        drawnow;
    end
    if PLOT2D_OUT == 1;
        setup_Pplot2D(S,coord,101,S_name);
        drawnow;
    end
    
% setup network
    if TEXT_OUT == 1
        fprintf('   - initializing network\n');
    end
    if cont_density == 1
        option = odeset('Stats','off','RelTol',1e-3,'AbsTol',1e-8);
        Isize = nchoosek(Nc,conn);
        perm = nchoosek(1:Nc,conn);
        C = zeros(Nc,Isize);
        for i = 1:Isize
            for c = 1:conn
                C(perm(i,c),i) = 1;
            end
        end
        N = rand(Isize,1);
        Wmg = C*diag(N); Wgm = C';
        Iage = ones(1,Isize);
        Imark = zeros(1,Isize);
    else
        Isize = 2*Nc; N = ones(Isize,1);
        Wmg = zeros(Nc, Isize); Wgm = zeros(Isize, Nc);
        Iage = -ones(1,Isize);
        Imark = zeros(1,Isize);  
    end
    [P, I] = cal_activity(non_lin,CS,Wmg,Wgm,S,S,rm,rg);
    time_axis = 0:step:exp_time;
    N_t = NaN*ones(length(time_axis),Isize);
    Pcorr_t = NaN*ones(1,length(time_axis));
    Tcorr_t = NaN*ones(1,length(time_axis));
    Pangle_t = NaN*ones(1,length(time_axis));
    Tangle_t = NaN*ones(1,length(time_axis));
    Pfoc_t = NaN*ones(1,length(time_axis));
    CV_t = NaN*ones(1,length(time_axis));
    CVid_t = NaN*ones(Ns,length(time_axis));
    F_t = NaN*ones(Ns,Ns,length(time_axis));
    if cont_density == 1
        N_t(1,:) = N;
    end
    if PLOT_OUT == 1;
        HP1d = setup_Pplot(P,corr(P,TYPE),corr(P',TYPE),rg,2,Wmg,Wgm,time_axis,N_t);
        [HI, Iaxis] = setup_Iplot(cont_density,time_axis,I,corr(I(Iage>=0,:),TYPE),Iage,Wmg,N_t(1,:),3);
        VST = cell(1,2);
        VST{1} = Pcorr_t; VST{2} = Tcorr_t;
        line_style = cell(1,2);
        line_style{1} = '-b'; line_style{2} = '-r';
        line_name = cell(1,2);
        line_name{1} = Pcorr_t; line_name{2} = Tcorr_t;
        Hinfo = setup_Infoplot(time_axis,VST,line_style,line_name,corr(S,TYPE),corr(P,TYPE),4);
        drawnow;
    end
    if PLOT2D_OUT == 1;
        setup_Pplot2D(P,coord,102,S_name);
        drawnow;
    end
    
% step
    if TEXT_OUT == 1
        fprintf('   - running\n');
    end
    Pcorr_t(1) = mean_excluNaN(uptri_1d(corr(P,TYPE)));
    Pangle_t(1) = mean_excluNaN(uptri_1d(corr_angle(corr(S,TYPE),corr(P,TYPE))));
    Pfoc_t(1) = mean_excluNaN(focality(P,metric));
    CV_t(1) = std(mean(P,2))/mean(mean(P,2));
    CVid_t(:,1) = std(P)./mean(P);
    F_t(:,:,1) = corr(P);
    if tracking == 1
        Tcorr_t(1) = mean_excluNaN(cal_track_corr(track_pairs,P));
        Tangle_t(1) = mean_excluNaN(corr_angle(cal_track_corr(track_pairs,S),cal_track_corr(track_pairs,P)));
    end
    if TEXT_OUT == 1
        fprintf('           corr = %f\n', Pcorr_t(1));
    end
    for i = 1:round(exp_time/step)
        if TEXT_OUT == 1
            fprintf('       - run num = %d, time = %f\n',run_num,i*step);
        end
        
        if cont_density == 1
            [ignore,N] = ode23(@RHS,[0 step],N_t(i,:),option);
            N_t(i+1,:) = N(end,:);
            Wmg = C*diag(N(end,:)); Wgm = C';
        else
            for j = 1:round(step/dt)
                add_cell;
                [P, I] = cal_activity(non_lin,CS,Wmg,Wgm,S,P,rm,rg);
                remove_cell;
            end
        end
        [P, I] = cal_activity(non_lin,CS,Wmg,Wgm,S,P,rm,rg);
        Pcorr_t(i+1) = mean_excluNaN(uptri_1d(corr(P,TYPE)));
        Pangle_t(i+1) = mean_excluNaN(uptri_1d(corr_angle(corr(S,TYPE),corr(P,TYPE))));
        Pfoc_t(i+1) = mean_excluNaN(focality(P,metric));
        CV_t(i+1) = std(mean(P,2))/mean(mean(P,2));
        CVid_t(:,i+1) = std(P)./mean(P);
        F_t(:,:,i+1) = corr(P);
        if tracking == 1
            Tcorr_t(i+1) = mean_excluNaN(cal_track_corr(track_pairs,P));
            Tangle_t(i+1) = mean_excluNaN(corr_angle(cal_track_corr(track_pairs,S),cal_track_corr(track_pairs,P)));
        end
        
        if TEXT_OUT == 1
            fprintf('           corr = %f\n', Pcorr_t(i+1));
        end

        if ANIME_OUT == 1;
            update_Pplot(P,corr(P,TYPE),corr(P',TYPE),rg,HP1d,Wmg,Wgm,N_t);
            update_Iplot(cont_density,time_axis,I,corr(I(Iage>=0,:),TYPE),Iage,Wmg,N_t(i,:),HI);
            VST = cell(1,2);
            VST{1} = Pcorr_t; VST{2} = Tcorr_t;
            update_Infoplot(VST,corr(P,TYPE),Hinfo);
            drawnow;
        end
        if ANIME2D_OUT == 1;
            setup_Pplot2D(P,coord,102,S_name);
            drawnow;
        end
    end
    
% end

    if PLOT_OUT == 1;
        update_Pplot(P,corr(P,TYPE),corr(P',TYPE),rg,HP1d,Wmg,Wgm,N_t);
        update_Iplot(cont_density,time_axis,I,corr(I(Iage>=0,:),TYPE),Iage,Wmg,N_t(i,:),HI);
        VST = cell(1,2);
        VST{1} = Pcorr_t; VST{2} = Tcorr_t;
        update_Infoplot(VST,corr(P,TYPE),Hinfo);
        drawnow;
    end
    if PLOT2D_OUT == 1;
        setup_Pplot2D(P,coord,102,S_name);
        drawnow;
    end
    
    objfun = return_val;
    if TEXT_OUT == 1
        fprintf('--- End ---\n');
    end

% nested function definition
    function dN_ = RHS(ignore,N1_)
        N_ = N1_;
        Wmg = C*diag(N_); Wgm = C';
        [ignore,G_] = cal_activity(non_lin,CS,Wmg,Wgm,S,P,rm,rg);
        P_ = survival(G_);
        P_ = P_ + 1e-10*randn(size(P_));
        B_ = Na*Nc/Isize/conn;
        dN_ = B_ + log(P_).*N_;
    end % RHS

    function prob_ = survival(G_)
        Ca_ = sum(rec(G_,th,0),2);
        prob_ = (tanh((Ca_-ts)*gamma)+1)*(maxv-minv)/2+minv;
    end % survival
    
    function val = remove_cell
        prob_ = survival(I);
        surv_ = floor(prob_ + rand(size(prob_)));
        IX_ = find((surv_==0)&(Iage'>=0));
        Iage(IX_) = -1;
        Imark(IX_) = 0;
        Wmg(:,IX_) = 0;
        Wgm(IX_,:) = 0;
        val = length(IX_);
    end % remove_cell
    
    function val = add_cell
        IX_ = find(Iage>=0);
        Iage(IX_) = Iage(IX_)+dt;
        IX_ = find(Iage<0);
        if length(IX_) < round(dt*Na*Nc)
            add_space(round(dt*Na*Nc)-length(IX_));
            IX_ = find(Iage<0);
        end
        if prob_conn == 0
            temp_ = [ones(conn,1); zeros(Nc-conn,1)];
            for i_ = 1:round(dt*Na*Nc)
                Iage(IX_(i_)) = 0;
                temp_ = temp_(randperm(Nc));
                Wmg(:, IX_(i_)) = temp_;
                IX2_ = randperm(length(temp_));
                temp1_ = temp_(IX2_(1:round(perm_ratio*length(temp_))));
                temp2_ = temp_(IX2_(1+round(perm_ratio*length(temp_)):end));
                Wgm(IX_(i_),IX2_) = [temp1_(randperm(length(temp1_))); temp2_]';
            end
        else
            conn_prob_ = conn/Nc;
            for i_ = 1:round(dt*Na*Nc)
                Iage(IX_(i_)) = 0;
                if (marking == 1) && (i*step >= marking_t(1)) && (i*step < marking_t(2))
                    Imark(IX_(i_)) = 1;
                end
                temp_ = floor(rand(Nc,1)+conn_prob_);
                Wmg(:, IX_(i_)) = temp_;
                IX2_ = randperm(length(temp_));
                temp1_ = temp_(IX2_(1:round(perm_ratio*length(temp_))));
                temp2_ = temp_(IX2_(1+round(perm_ratio*length(temp_)):end));
                Wgm(IX_(i_),IX2_) = [temp1_(randperm(length(temp1_))); temp2_]';
            end
        end
        val = round(dt*Na*Nc);
    end % add_cell
    
    function add_space(short)
        previous_size = Isize;
        tempI = I;
        tempIage = Iage;
        tempImark = Imark;
        tempWmg = Wmg;
        tempWgm = Wgm;
        
        Isize = Isize + 5*short;
        I = zeros(Isize, Ns);
        Iage = -ones(1, Isize);
        Imark = zeros(1, Isize);
        Wmg = zeros(Nc, Isize);
        Wgm = zeros(Isize, Nc);
        
        I(1:previous_size, :) = tempI;
        Iage(1, 1:previous_size) = tempIage;
        Imark(1, 1:previous_size) = tempImark;
        Wmg(:, 1:previous_size) = tempWmg;
        Wgm(1:previous_size,:) = tempWgm;
                
        if ANIME_OUT==1
            set(Iaxis, 'YLim', [1 Isize]);
        end
    end % add_space
    
    function val = cal_track_corr(track_pairs_,S_)
        corr_ = zeros(1,size(track_pairs_,1));
        for i_ = 1:size(track_pairs_,1)
            temp_ = corr(S_(:,track_pairs_(i_,:)),TYPE);
            corr_(i_) = temp_(1,2);
        end
        val = corr_;
    end % cal_track_corr
    
    function val = return_val
        
        val = 0;

    end % return_val

end % neurogenesis

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