Model of memory linking through memory allocation (Kastellakis et al. 2016)

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Accession:206249
Here, we present a simplified, biophysically inspired network model that incorporates multiple plasticity processes and explains linking of information at three different levels: (a) learning of a single associative memory (b) rescuing of a weak memory when paired with a strong one and (c) linking of multiple memories across time. By dissecting synaptic from intrinsic plasticity and neuron-wide from dendritically restricted protein capture, the model reveals a simple, unifying principle: Linked memories share synaptic clusters within the dendrites of overlapping populations of neurons
Reference:
1 . Kastellakis G, Silva AJ, Poirazi P (2016) Linking Memories across Time via Neuronal and Dendritic Overlaps in Model Neurons with Active Dendrites. Cell Rep 17:1491-1504 [PubMed]
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Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism:
Cell Type(s): Abstract integrate-and-fire leaky neuron with dendritic subunits;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: C or C++ program; C or C++ program (web link to model);
Model Concept(s): Active Dendrites;
Implementer(s): Kastellakis, George [gkastel at gmail.com];
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stdmodel
distributionPlot
exportfig
figs
mtrand
README
allgraphs.m
allrun.m
an_brtest.m
an_stats.m
anmulti.py
ansims.py
barwitherr.m *
btagstats.m *
CImg.h *
constructs.cpp
constructs.h
defaults.m
dir2.m *
getspikedata.m *
getsynstate.m *
getsynstate2.m *
graphs.m *
hist_percents.m *
hist_with_errs.m *
interact.m *
intexp_constructs.cpp
job_sims.sh
kurtos.m *
lamodel.cpp
LICENSE *
make_graphs.m *
Makefile *
matlab.mat *
mtest.py
mtrand.cpp *
mtrand.h *
multi.py
multistats.m *
nextplot.m *
pairstrong.m *
repeated.m *
rotateXLabels.m *
run_1.sh
run_2strong.sh
run_2weak.sh
run_3.sh
run_all.sh
run_brov.sh
run_brtest.sh
run_btag.sh
run_dir.sh
run_ep.sh
run_gp.sh
run_gp2.sh
run_mult.sh
run_Nsparse.sh
run_pairstrong.sh
run_rep.sh
run_sims.sh
run_sparse.sh
run_sparseS2.sh
runloc.sh
runmany.sh
S2sparse.m *
savefig.m *
scratch.m *
sensitivity.m *
stats.m *
stats.py *
stderr.m *
strong2.m *
strongstrong.m *
submit_lamodel.sh *
three.m *
trevrolls.m *
vis.py *
weastrong.m *
wxglmodel *
wxglmodel.cpp *
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wxmodel.cpp *
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close all

nruns = 10
itvls = [60, 90,120, 150, 180]
pops = zeros(2, length(itvls), nruns)


for run = 1:10
    for d = 1:length(itvls)
        itvl = itvls(d)
        fn=sprintf('N100.B40.I10.i6.P2.p1.T%d.S%d.w2c', itvl, 1980+run-1)
                
        stimduration = 2000;
        
        ar = sscanf(fn, 'N%d.B40.I10.i6.P%d.p1.T%d.S%d.w%d');

        nneurons = ar(1);
        npatterns = ar(2);
        interval = ar(3);
        seed = ar(4);
        weakmem = ar(5);
        npyrs = nneurons*0.8;

        ff = sprintf('./data/%s/spikes.dat', fn);
        f = fopen(ff);
        t = fgets(f);
        raster = zeros(nneurons, stimduration*npatterns*2);
        nid = 1;
        while (ischar(t))
           times = str2num(t);
           raster(nid, times) = 1;
           t = fgets(f);
           nid=nid+1;
        end
        fclose(f)

        %figure()
        %imagesc(raster)

        trainingspikes = zeros(npyrs, npatterns);
        recallspikes = zeros(npyrs, npatterns);

        for n=1:npyrs
            for j=1:npatterns
                tstart = (j-1)*stimduration;
                tend = tstart + stimduration;
                trainingspikes(n,j) = sum(raster(n, tstart+1:tend));

                tstart = npatterns*stimduration + (j-1)*stimduration;
                tend = tstart + stimduration;
                recallspikes(n,j) = sum(raster(n, tstart+1:tend));
            end
        end

        pop = sum(recallspikes > 20)/npyrs;

        ff = sprintf('./data/%s/synstate.dat', fn);
        ss = load(ff);
        for n=1:npatterns
            sb = ss(find(ss(:,5)==n & ss(:,7) > 1.4) , 2); % branch ids array
            sun = unique(sb);
            zz = [];
            for k=1:length(sun)
                zz(end+1) = length(find(sb(:) == sun(k)));
            end
        end

    
        pops(:, d, run) = pop'
        
    end
end

figure()
title('Strong before weak')
pops = pops*100.0
mpop = mean(pops, 3)
stdpop = std(pops, 0, 3)
errorbar(mpop', stdpop')
xlabel('Interval between memories (minutes)')
ylabel('% of recruited pyramidal neurons')
legend('Strong memory', 'Weak memory');
set(gca, 'XTick',[1:length(itvls)]);
set(gca, 'XTickLabel',itvls);
saveas(gcf, 'w_s_s.eps')