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Inhibitory network bistability explains increased activity prior to seizure onset (Rich et al 2020)

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Accession:266435
" ... the mechanisms predisposing an inhibitory network toward increased activity, specifically prior to ictogenesis, without a permanent change to inputs to the system remain unknown. We address this question by comparing simulated inhibitory networks containing control interneurons and networks containing hyperexcitable interneurons modeled to mimic treatment with 4-Aminopyridine (4-AP), an agent commonly used to model seizures in vivo and in vitro. Our in silico study demonstrates that model inhibitory networks with 4-AP interneurons are more prone than their control counterparts to exist in a bistable state in which asynchronously firing networks can abruptly transition into synchrony driven by a brief perturbation. This transition into synchrony brings about a corresponding increase in overall firing rate. We further show that perturbations driving this transition could arise in vivo from background excitatory synaptic activity in the cortex. Thus, we propose that bistability explains the increase in interneuron activity observed experimentally prior to seizure via a transition from incoherent to coherent dynamics. Moreover, bistability explains why inhibitory networks containing hyperexcitable interneurons are more vulnerable to this change in dynamics, and how such networks can undergo a transition without a permanent change in the drive. ..."
Reference:
1 . Rich S, Chameh HM, Rafiee M, Ferguson K, Skinner FK, Valiante TA (2020) Inhibitory Network Bistability Explains Increased Interneuronal Activity Prior to Seizure Onset. Front Neural Circuits 13:81 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Synapse;
Brain Region(s)/Organism:
Cell Type(s): Abstract Izhikevich neuron;
Channel(s): I Potassium;
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s): Gaba;
Simulation Environment: C or C++ program;
Model Concept(s): Synchronization; Epilepsy;
Implementer(s): Rich, Scott [sbrich at umich.edu];
Search NeuronDB for information about:  I Potassium; Gaba;
/
CorticalInhibitoryNetwork-master
README.md
columnlegend.m
conv_gaussian.m
convert_spiketimes.m
getpos.m
golomb_measure.m
golomb_synch.m
InhibitoryCortex_pulse_0713.m
InhibitoryCortex_pulse_2d_FullWithRep_0905.m
InhibitoryCortex_pulse_bistabilitymeasure_zoom_repetitions_0823.m
InhibitoryCortex_pulse_stitchtogether_0709.m
InhibitoryCortex_pulse_stitchtogether_full_repetitions_0822.m
InhibitoryCortex_pulse_stitchtogether_zoom_0726.m
InhibitoryCortex_pulse_stitchtogether_zoom_repetitions_0813.m
InhibitoryCortex_pulse_zoom_0726.m
InhibitoryNetwork_cortex.sh
InhibitoryNetwork_cortex_0712.c
InhibitoryNetwork_cortex_IF_0615.c
InhibitoryNetwork_cortex_submit_0812.sh
InhibitoryNetwork_cortex_submit_zoom_0725.sh
InhibitoryNetwork_SortedRaster.m
legendflex.m
legendtitle.m
LICENSE *
mtit.m
parseArgs.m *
PlottingFICurves_v1.m
savemultfigs.m
subaxis.m *
suplabel.m
                            
function InhibitoryCortex_pulse_zoom_0726(brange)

%% Parameters
numcells=500;
endtime=2000;
pulsetime=1000;
dt=.01;
steps=endtime/dt+1;

numI=50;

csvname=cell(length(brange),2);
csvname2=cell(length(brange),2);

for b=brange
    %% Input data from csv files
    current=cd;
    cd('/Users/scottrich/OneDrive - UHN/1) Compute Canada Files');
%             cd('E:\OneDrive - UHN\1) Compute Canada Files');
    
    str1=sprintf('InhibitoryNetwork_%d_TrackVariables.csv', b);
    str2=sprintf('InhibitoryNetwork_%d_SpikeTimes.csv', b);
    str3=sprintf('InhibitoryNetwork_%d_InputCurrents.csv', b);
    str4=sprintf('InhibitoryNetwork_%d_ConnectivityMatricies.csv', b);
    str5=sprintf('InhibitoryNetwork_%d_Parameters.csv', b);
    
    % trackvariables=csvread(str1);
    spikes=csvread(str2);
    currents=csvread(str3);
    % connectivity=csvread(str4);
    variables=csvread(str5);
    
    cd(current);
    
    numgsyn=length(variables(:,1))/numI;
    
    %% Initialize Variables
    loopnum=numgsyn*numI;
    synchrony=NaN(1, loopnum);
    popfreq=NaN(1,loopnum);
    
    %% Post Pulse
    if length(spikes)/numcells==loopnum
        endnum=loopnum;
    else
        endnum=floor(length(spikes)/numcells);
    end
    for i=1:endnum
        myspikes=spikes(1+(i-1)*numcells:(i)*numcells, :);
        use=myspikes>(endtime-500);
        usespikes=zeros(numcells, ceil(length(spikes(1,:))/3));
        for j=1:numcells
            temp=myspikes(j,use(j,:));
            usespikes(j,1:length(temp))=temp;
        end
        
        %% Calculate MFF (as approximation for the network frequency)
        freq=zeros(1,numcells);
        for j=1:numcells
            freq(j)=sum(usespikes(j,:)>0)/.5;
        end
        popfreq(i)=sum(freq)/numcells;
        
        %% Calculate Synchrony Measure
        myspikes=spikes((1+(i-1)*numcells):(i*numcells),:);
        indices=find(myspikes<(endtime-500));
        myspikes(indices)=0;
        count=1;
        totalspikes=sum(sum(myspikes~=0));
        myspikes_formatted=zeros(totalspikes,2);
        for k=1:numcells
            for j=1:length(myspikes(1,:))
                if myspikes(k,j)~=0
                    myspikes_formatted(count,2)=k;
                    myspikes_formatted(count,1)=myspikes(k,j)-(endtime-500);
                    count=count+1;
                end
            end
        end
        synchrony(i)=golomb_measure(numcells,myspikes_formatted,2);
        if isfinite(synchrony(i))==0
            synchrony(i)=0;
        end
        str=sprintf('i = %1.0d', i);
        disp(str)
        progressname=sprintf('progress%d.mat', b);
        save(progressname, 'synchrony', 'b', 'i');
    end
    %% Save Output
    csvname{b-brange(1)+1,1}=sprintf('Synchrony_postpulse_state%d_probii%1.0f_std%1.0f_gsynmin%1.0f_gsynmax%1.0f_num%d.csv', variables(1,5), variables(1,1)*100, variables(1,4), variables(1,2)*10, variables(end,2)*10,b);
    csvname{b-brange(1)+1,2}=sprintf('PopulationFreq_postpulse_state%d_probii%1.0f_std%1.0f_gsynmin%1.0f_gsynmax%1.0f_num%d.csv', variables(1,5), variables(1,1)*100, variables(1,4), variables(1,2)*10, variables(end,2)*10,b);
    csvwrite(csvname{b-brange(1)+1,1},synchrony);
    csvwrite(csvname{b-brange(1)+1,2},popfreq);
    
    str=sprintf('b = %1.0d', b);
    disp(str)
    delete(progressname);
    
    %% Pre Pulse
    if length(spikes)/numcells==loopnum
        endnum=loopnum;
    else
        endnum=floor(length(spikes)/numcells);
    end
    for i=1:endnum
        myspikes=spikes(1+(i-1)*numcells:(i)*numcells, :);
        use=myspikes>(pulsetime-500);
        use2=myspikes<pulsetime;
        myuse=logical(use.*use2);
        usespikes=zeros(numcells, ceil(length(spikes(1,:))/3));
        for j=1:numcells
            temp=myspikes(j,myuse(j,:));
            usespikes(j,1:length(temp))=temp;
        end
        
        %% Calculate MFF (as approximation for the network frequency)
        freq=zeros(1,numcells);
        for j=1:numcells
            freq(j)=sum(usespikes(j,:)>0)/.5;
        end
        popfreq(i)=sum(freq)/numcells;
        
        %% Calculate Synchrony Measure
        myspikes=spikes((1+(i-1)*numcells):(i*numcells),:);
        indices1=find(myspikes<(pulsetime-500));
        indices2=find(myspikes>pulsetime);
        myspikes(indices1)=0;
        myspikes(indices2)=0;
        count=1;
        totalspikes=sum(sum(myspikes~=0));
        myspikes_formatted=zeros(totalspikes,2);
        for k=1:numcells
            for j=1:length(myspikes(1,:))
                if myspikes(k,j)~=0
                    myspikes_formatted(count,2)=k;
                    myspikes_formatted(count,1)=myspikes(k,j)-(pulsetime-500);
                    count=count+1;
                end
            end
        end
        synchrony(i)=golomb_measure(numcells,myspikes_formatted,2);
        if isfinite(synchrony(i))==0
            synchrony(i)=0;
        end
        str=sprintf('i = %1.0d', i);
        disp(str)
        progressname=sprintf('progress%d.mat', b);
        save(progressname, 'synchrony', 'b', 'i');
    end
    %% Save Output
    csvname2{b-brange(1)+1,1}=sprintf('Synchrony_prepulse_state%d_probii%1.0f_std%1.0f_gsynmin%1.0f_gsynmax%1.0f_num%d.csv', variables(1,5), variables(1,1)*100, variables(1,4), variables(1,2)*10, variables(end,2)*10,b);
    csvname2{b-brange(1)+1,2}=sprintf('PopulationFreq_prepulse_state%d_probii%1.0f_std%1.0f_gsynmin%1.0f_gsynmax%1.0f_num%d.csv', variables(1,5), variables(1,1)*100, variables(1,4), variables(1,2)*10, variables(end,2)*10,b);
    csvwrite(csvname2{b-brange(1)+1,1},synchrony);
    csvwrite(csvname2{b-brange(1)+1,2},popfreq);
    
    str=sprintf('b = %1.0d', b);
    disp(str)
    delete(progressname);
end

filename=sprintf('progress_postpulse_%dto%d.mat', brange(1), brange(end));
save(filename, 'csvname');

filename2=sprintf('progress_prepulse_%dto%d.mat', brange(1), brange(end));
save(filename2, 'csvname2');

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