Storing serial order in intrinsic excitability: a working memory model (Conde-Sousa & Aguiar 2013)

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Accession:147461
" … Here we present a model for working memory which relies on the modulation of the intrinsic excitability properties of neurons, instead of synaptic plasticity, to retain novel information for periods of seconds to minutes. We show that it is possible to effectively use this mechanism to store the serial order in a sequence of patterns of activity. … The presented model exhibits properties which are in close agreement with experimental results in working memory. ... "
Reference:
1 . Conde-Sousa E, Aguiar P (2013) A working memory model for serial order that stores information in the intrinsic excitability properties of neurons. J Comput Neurosci 35:187-99 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism:
Cell Type(s):
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON;
Model Concept(s): Working memory;
Implementer(s):
clear all
clc
close all
tic
load out_data_size.dat
load out_P_spike_ids.dat
load out_P_spike_times.dat




np          = out_data_size(1);   % N_Principal_NEURONS
ng          = out_data_size(2);   % N_gate_interneurons
n_patt      = out_data_size(3);   % N_PATTERNS
patt_size   = out_data_size(4);   % PATTERN_SIZE

out_P_spike_times = out_P_spike_times./1000; %change units to seconds



%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%% plot inhibitory interneurons %%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

ind = find(out_P_spike_ids<np);
figure                              

plot(out_P_spike_times(ind),out_P_spike_ids(ind),'.','MarkerSize',6)
set(gca,'YLim',[0-1,np])
set(gca,'YTick',0:patt_size:np-1)
set(gca,'YTickLabel','')
grid
% xlim([0,35])
xlabel('Time [s]')
ylabel('Inhibitory Interneuron ID')
set(gcf,'Position',get(0,'ScreenSize'))




%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%% plot principal neurons %%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
ind = find(out_P_spike_ids>np-1&out_P_spike_ids<2*np);
figure                              % plot principal_neurons
ytick=[0,np-1];
j=1;yticklabel={};
for i=ytick
    yticklabel{j}='';
    j=j+1;
end
yticklabel{1}=num2str(0);yticklabel{end}=num2str(ytick(end));

xtick=0:2:max(out_P_spike_times(ind));xtick(end+1)=xtick(end)+2;
j=1;xticklabel={};
for i=xtick
    xticklabel{j}='';
    j=j+1;
end
xticklabel{1}=num2str(0);xticklabel{end}=num2str(xtick(end));
plot(out_P_spike_times(ind),out_P_spike_ids(ind)-np,'.','MarkerSize',6)
set(gca,'YTick',ytick)
set(gca,'YLim',[0-1,np])
set(gca,'YTickLabel',yticklabel)
set(gca,'XTick',xtick)
set(gca,'XLim',[0, xtick(end)])
set(gca,'XTickLabel',xticklabel)
xlabel('Time [s]')
ylabel('Principal Neuron ID')
set(gcf,'Position',get(0,'ScreenSize'))
% grid


%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%% plot gate interneurons %%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

                                
col={[1,0,0],[0,0,1]};

figure                              % plot principal_neurons
hold on


for i=0:np-1
    ind = find(out_P_spike_ids>=2*np+i*ng&out_P_spike_ids<2*np+(i+1)*ng);  
    plot(out_P_spike_times(ind),out_P_spike_ids(ind),'.','MarkerSize',6,'color',col{mod(i,2)+1})
end
hold off
set(gca,'YLim',[2*np,np*(2+ng)])
set(gca,'YTick',2*np:ng:np*(2+ng))
set(gca,'YTickLabel','')
xlabel('Time [s]')
ylabel('Gate Interneuron ID')
set(gcf,'Position',get(0,'ScreenSize'))
grid
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