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]
Citations  Citation Browser
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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README
.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
nruns=10;

totfiring = zeros(nruns, 4);
totactive = zeros(nruns, 4);

coract = zeros(nruns, 9);

%CUTOFF=5; % Hz

npatterns=1;
ndays  = 4;

brweights = zeros(ndays, npyrs*nbranches, nruns);
nrnweights = zeros(ndays, npyrs, nruns);
branch_syns = zeros(ndays, npyrs*nbranches, nruns);
brsynratio= zeros(ndays, nruns);



for run=1:nruns
        for ncase=1:ndays
            
            sfn=sprintf('./data/%s_%d_%d/spikesperpattern.dat', CONDITION, ncase, run-1);
            spk = load( sfn);
            spk = spk(1, 1:npyrs)/(stimduration/1000);
            
            %pop = spk(spk>=CUTOFF);
            pop = spk(spk>=CUTOFF);
            totfiring(run, ncase) = mean(spk, 2);
            totactive(run, ncase) = sum(spk>CUTOFF,2)/npyrs;



            ff = sprintf('./data/%s_%d_%d/synstate.dat', CONDITION, ncase, run-1);
            ss = load(ff);

            for i=1:size(ss,1)
                bid=ss(i,2);
                nid=ss(i,3);
                srcid=ss(i,5);
                bstrength = ss(i,6);
                w=ss(i,7);
                if (srcid ==0 && bid <= npyrs*nbranches)
                    brweights( ncase, bid+1, run) = brweights(ncase, bid+1, run) + w;
                    brstrengths(ncase, bid+1)=bstrength;
                    nrnweights( ncase, nid+1,run) = nrnweights(ncase, nid+1,run) + w;
                end
                if (srcid ==0 && bid <= npyrs*nbranches &&  w > 0.7)
                    branch_syns(ncase, bid+1, run) = branch_syns(ncase, bid+1, run)+1;
                end
            end
            
            brsynratio(ncase,run) = sum(branch_syns(ncase,:, run)>3)/(nbranches*npyrs);
        end
end

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

mact = mean(totactive,1);
sact = std(totactive,0,1)/sqrt(nruns);

barwitherr(100.* sact(1,:,1), 100.* mact(1,:,1));
title('% coding neurons')
%ylabel('% Active neurons')
xlabel('Day')
ylim([0,80]);

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

figure
barwitherr(sf(1,:,1), mf(1,:,1));

title('Average firing rate [Hz]')
%ylabel('Avg firing rate [Hz]')
%xlabel('Number of trainings')
%ylim([0,70]);
export_fig(sprintf('./figs/%s_rates.pdf',CONDITION), '-transparent')

figure
hs=hist(branch_syns(1, :), [0:8]);

bar(hs(:,2:end)/sum(hs(:)))
title('1st day')
ylim([0,0.2]);
export_fig(sprintf('./figs/%s_brsyns_1day.pdf',CONDITION), '-transparent')

figure
hs=hist(branch_syns(4, :), [0:8]);
bar(hs(:,2:end)/sum(hs(:)))
ylim([0,0.2]);
title('4th day')
export_fig(sprintf('./figs/%s_brsyns_4day.pdf',CONDITION), '-transparent')


figure
barwitherr(100.0*std(brsynratio,0,2)/sqrt(nruns), 100.0*mean(brsynratio,2))
title('Branches with >2 potentiated synapses')
ylabel('Percentage')
xlabel('Day')
ylim([0,16]);
export_fig(sprintf('./figs/%s_brsyns.pdf',CONDITION), '-transparent')


figure
aa = mean(branch_syns(:,:)');
ss = std(branch_syns(:,:)');
errorbar(ss, aa, 'o')
title('Potentiated synapses per branch');
ylabel('Number of synapses')
xlabel('Day')
ylim([0,6]);
export_fig(sprintf('./figs/%s_syn_per_branch.pdf',CONDITION), '-transparent')



figure
tweights = (squeeze(sum(nrnweights,2)));

barwitherr(std(tweights,0,2), mean(tweights,2))

%barwitherr(100.0*std(brsynratio,0,2)/sqrt(nruns), 100.0*mean(brsynratio,2))
title('Total Syn Weights')
ylabel('Total Syn Weight')
xlabel('Day')
ylim([0,12000]);
export_fig(sprintf('./figs/%s_tweights.pdf',CONDITION), '-transparent')


CONDITION
b1 = branch_syns(1,:);
b1 = b1(b1>0);
b4 = branch_syns(4,:);
b4 = b4(b4>0);
mean(b1)
std(b1)
mean(b4)
std(b4)
[h,p] = ttest2(b1, b4)