Hotspots of dendritic spine turnover facilitates new spines and NN sparsity (Frank et al 2018)

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Accession:227087
Model for the following publication: Adam C. Frank, Shan Huang, Miou Zhou, Amos Gdalyahu, George Kastellakis, Panayiota Poirazi, Tawnie K. Silva, Ximiao Wen, Joshua T. Trachtenberg, and Alcino J. Silva Hotspots of Dendritic Spine Turnover Facilitate Learning-related Clustered Spine Addition and Network Sparsity
Reference:
1 . Frank AC, Huang S, Zhou M, Gdalyahu A, Kastellakis G, Silva TK, Lu E, Wen X, Poirazi P, Trachtenberg JT, Silva AJ (2018) Hotspots of dendritic spine turnover facilitate clustered spine addition and learning and memory. Nat Commun 9:422 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell; Connectionist Network;
Brain Region(s)/Organism:
Cell Type(s): Abstract integrate-and-fire leaky neuron with dendritic subunits;
Channel(s):
Gap Junctions:
Receptor(s): NMDA;
Gene(s):
Transmitter(s):
Simulation Environment: C or C++ program; MATLAB;
Model Concept(s): Active Dendrites; Synaptic Plasticity;
Implementer(s): Kastellakis, George [gkastel at gmail.com];
Search NeuronDB for information about:  NMDA;
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.exrc *
an_m_to.m
an_to.m
barwitherr.m *
btagstats.m *
CImg.h *
constructs. *
constructs.cpp *
constructs.h
csvgraph.m
defaults.m
dir2.m *
gconstructs.cpp *
getspikedata.m *
getsynstate.m *
getsynstate2.m *
graphs.m *
hist_percents.m *
hist_with_errs.m *
interact.m *
kurtos.m *
lamodel
lamodel.cpp
LICENSE *
make_graphs.m *
Makefile *
matlab.mat *
mtrand.cpp *
mtrand.h *
multistats.m *
nextplot.m *
pairstrong.m *
repeated.m *
rotateXLabels.m *
run_to.sh
S2sparse.m *
savefig.m *
scratch.m *
sensitivity.m *
stats.m *
stats.py *
stderr.m *
strong2.m *
strongstrong.m *
submit_lamodel.sh *
three.m *
to.cpp
trevrolls.m *
vis.py *
weastrong.m *
wxglmodel *
wxglmodel.cpp *
wxglmodel.h *
wxmodel.cpp *
wxmodel.h *
                            
defaults
close all
nruns=5;

diffs = [ 60, 120, 300, 1440, 2880 ];

difflabels = { '1H','2H', '5H', '24H', '48H'};

ndiffs = length(diffs);

totcommon = zeros(nruns, ndiffs);

totfiring = zeros(nruns, ndiffs, 2);
totactive = zeros(nruns, ndiffs, 2);

overlap = zeros(nruns, ndiffs);
coract = zeros(nruns, ndiffs);

brsyns = zeros(nruns,npyrs*nbranches);
brsynsH = zeros(nruns,10);

nrnsyns = zeros(nruns,npyrs);

nrnbins = [0:40]
nrnsynsH = zeros(nruns, length(nrnbins));

brcors = zeros( nruns, ndiffs);
nrncors = zeros(nruns, ndiffs);


histCSUSrange = [1:50];
histCSUS = zeros(nruns, length(diffs), length(histCSUSrange));

brcommon = [];

for ncase=1:length(diffs)
        for run=1:nruns
            diff = diffs(ncase);
            
            if (diff < 0)
                p1 = 0;
                p2 = -diff;
            else
                p1 = diff;
                p2 = 0;
            end
            
            sfn=sprintf('./data/%s_%d_%d/spikesperpattern.dat', CONDITION, p1,run-1)
            
            spk = load( sfn);
            spk = spk(1:2, :);

            if (diff<0)
                spk = flipud(spk);
            end
            
            spk = spk(:, 1:npyrs)/(stimduration/1000); %duration
            
            
            totfiring(run, ncase, 1) = mean(spk(1, :), 2);
            totfiring(run, ncase, 2) = mean(spk(2, :), 2);
            totactive(run, ncase, :) = sum(spk>CUTOFF,2)/npyrs;
            
            
            ffactive(run, ncase, 1) = mean(spk(1, spk(1,:)>CUTOFF), 2);
            ffactive(run, ncase, 2) = mean(spk(2, spk(2,:)>CUTOFF), 2);
            
            
            spk1 = (spk(1,:)>=CUTOFF);
            spk2 = (spk(2,:)>=CUTOFF);
            totcommon(run, ncase) = sum(spk1 & spk2)/( sum(spk1)+sum(spk2));
            
            
            s = corrcoef(spk');
            coract(run, ncase) = s(1,2);

            overlap(run, ncase) = corr( (spk(1,:)>=CUTOFF)' ,  (spk(2,:)>=CUTOFF)' );
            
            if (1)
                [bw, bs, nw, ns] = getsynstate(sprintf('./data/%s_%d_%d/synstate.dat', CONDITION, p1,run-1));

                %brcors(run,ncase) = corr(bw(1,:)', bw(2,:)');
                %nrncors(run,ncase) = corr(nw(1,:)', nw(2,:)');


                brsyns = bs(1,:);
                brsyns2 = bs(2,:);

                brcommon(run, ncase) =  sum( (brsyns>1) & (brsyns2>1) ,2)/ sum( (brsyns>1) | (brsyns2>1) ,2);
                
                brtot = brsyns+brsyns2;
                histCSUS(run, ncase, :)  = histc(brtot(find((brsyns>0) & (brsyns2>0))), [1:50]);
                
                
               % brsyns(run,:) = bs(1,:);
               % brsynsH(run,:) = hist(bs(1,:), [0:9]);
               % brsynsH(run,:) = brsynsH(run,:)/sum(brsynsH(run,:));

               % nrnsyns(run,:) = ns(1,:);
               % nrnsynsH(run,:) = hist(ns(1,:), nrnbins);
               % nrnsynsH(run,:) = nrnsynsH(run,:)/sum(nrnsynsH(run,:));
            end
        end
        
        
end

if (strcmp(CONDITION,'dir1L') || strcmp(CONDITION,'dir2L'))
    COL='r';
elseif (strcmp(CONDITION,'dir1G') || strcmp(CONDITION,'dir2G'))
    COL='g';
else
    COL='b';
end

%close all
mf = mean(totfiring,1)
sf = std(totfiring,0,1)/sqrt(nruns)
figure

hold on
errorbar(mf(:,:,1), sf(:,:,1));
errorbar(mf(:,:,2), sf(:,:,2), 'g');
hold off

%barwitherr(sf(1,:,1), mf(1,:,1));
set(gca, 'XTick', [1:length(diffs)])
set(gca, 'XTickLabel', difflabels)
title(sprintf('Avg firing rate \nAll Neurons [Hz] - %s', CONDITION))
xlabel('Interval Between Memories [minutes]')

%ylim([0,10])

export_fig(sprintf('./figs/%s_ffirst.pdf',CONDITION), '-transparent')


figure
mact = 100.0*mean(totactive,1)
sact = 100.0*std(totactive,0,1)/(sqrt(nruns))


hold on
errorbar(mact(:,:,1), sact(:,:,1), COL);
errorbar(mact(:,:,2), sact(:,:,2), 'g');

hold off

%barwitherr(sact(1,:,1), mact(1,:,1));
set(gca, 'XTick', [1:length(diffs)])
set(gca, 'XTickLabel', difflabels)
title('% Active neurons');
%xlabel('Weak-Strong Interval [minutes]')
ylim([10,50])
export_fig(sprintf('./figs/%s_actfirst.pdf',CONDITION), '-transparent')



figure
barwitherr(std(coract, 0,1)/sqrt(nruns), mean(coract), COL);
set(gca, 'XTick', [1:length(diffs)])
set(gca, 'XTickLabel', difflabels)
title(sprintf('Similarity between population firing patterns %s ', CONDITION))
ylabel('Similarity')
%xlabel('Weak-Strong Interval [minutes]')
%ylim([0,.8])
export_fig(sprintf('./figs/%s_corr.pdf',CONDITION), '-transparent')





figure
barwitherr(std(overlap, 0,1)/sqrt(nruns), mean(overlap), COL);
set(gca, 'XTick', [1:length(diffs)])
set(gca, 'XTickLabel', difflabels)
title(sprintf('Active population similarity %s ', CONDITION))
ylabel('Similarity')
%xlabel('Weak-Strong Interval [minutes]')
ylim([-.1,1.0])
export_fig(sprintf('./figs/%s_overlap.pdf',CONDITION), '-transparent')

%close all
mf = mean(ffactive,1)
sf = std(ffactive,0,1)/sqrt(nruns)
figure


hold on
errorbar(mf(:,:,1), sf(:,:,1));
errorbar(mf(:,:,2), sf(:,:,2), 'g');
hold off
%barwitherr(sf(1,:,1), mf(1,:,1));
set(gca, 'XTick', [1:length(diffs)])
set(gca, 'XTickLabel', difflabels)
title('Active neurons firing rate [Hz]')
xlabel('Interval Between Memories [minutes]')
ylim([0,100])
export_fig(sprintf('./figs/%s_ffactive.pdf',CONDITION), '-transparent')


figure
barwitherr(100.*std(totcommon)/(sqrt(nruns)), 100.*mean(totcommon), COL);
set(gca, 'XTick', [1:length(diffs)])
set(gca, 'XTickLabel', difflabels)
title(sprintf('Common Active Neurons %s', CONDITION))
ylabel('% common neurons')
%xlabel('Weak-Strong Interval [minutes]')
ylim([0,50])
export_fig(sprintf('./figs/%s_totcommon.pdf',CONDITION), '-transparent')


figure
barwitherr(100.*std(brcommon)/(sqrt(nruns)), 100.*mean(brcommon), COL);
set(gca, 'XTick', [1:length(diffs)])
set(gca, 'XTickLabel', difflabels)
title('% Branches with clusters of both memories')
ylabel('% branches')
%xlabel('Weak-Strong Interval [minutes]')
ylim([0,50])
export_fig(sprintf('./figs/%s_brcommon.pdf',CONDITION), '-transparent')


figure;
cc=winter(6);
nn = [1,3,4];
color = [0,0,1];

for i=1:length(nn)
    ncase = nn(i);
    
    diff = diffs(ncase);
    
    aa  = mean(histCSUS(:,ncase,:));
    bb = stderr(histCSUS(:,ncase,:));
    aa = aa(:);
    
    bb =bb(:);
    errorbar(aa,bb, 'Color',  cc(ncase,:));
    %set(gca, 'Color', 'o');
    hold on
    xlim([0,15]);
end

ylabel('Number of clusters of both memories');
xlabel('Synapses per  cluster');

legend({'1 hour', '3 hours', '5 hours'});
export_fig(sprintf('./figs/%s_clustering2.pdf',CONDITION), '-transparent')
hold off;

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