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;
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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
                            
%this function takes a vector 'spike_times' (which lists spike times in
%milliseconds), then converts the list of spike times into a time series of
%ones and zeros, with a one indicating a spike at a certain time. The user
%specifies the sampling rate (in Hz) and the duration of the resulting
%signal (in seconds)

function [time,signal] = convert_spiketimes(spike_times,duration,srate)
    time = 1000*(1/srate:1/srate:duration); %time stamps (in ms, not seconds) corresonding to each entry in signal
    signal = zeros(duration*srate,1); %number of points in final signal will be the number of seconds multiplied by samples per second
    Npoints = length(signal);
    %use the vector 'spike_times' to insert ones in 'signal' where appropriate
    for i_s = 1:length(spike_times)
        %1000*duration is the duration of the signal in milliseconds;
        %multiply the fraction of the way through the signal the spike
        %occurs by the number of points in the discrete signal, and round
        %to obtain the index of the spike
        signal( max(round( spike_times(i_s)/(duration*1000) * Npoints ),1)) = 1;
    end
end

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