Learning intrinsic excitability in Medium Spiny Neurons (Scheler 2014)

 Download zip file 
Help downloading and running models
Accession:155131
"We present an unsupervised, local activation-dependent learning rule for intrinsic plasticity (IP) which affects the composition of ion channel conductances for single neurons in a use-dependent way. We use a single-compartment conductance-based model for medium spiny striatal neurons in order to show the effects of parameterization of individual ion channels on the neuronal membrane potential-curent relationship (activation function). We show that parameter changes within the physiological ranges are sufficient to create an ensemble of neurons with significantly different activation functions. ... "
Reference:
1 . Scheler G (2014) Learning intrinsic excitability in medium spiny neurons F1000Research 2:88 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell;
Brain Region(s)/Organism: Striatum;
Cell Type(s): Neostriatum medium spiny direct pathway GABA cell; Neostriatum medium spiny indirect pathway GABA cell;
Channel(s): I A; I K; I h; I K,Ca; I Calcium; I A, slow; I Cl, leak; I Ca,p;
Gap Junctions:
Receptor(s): GabaA; AMPA; NMDA;
Gene(s): Kv4.2 KCND2; Kv1.1 KCNA1; Kv1.2 KCNA2; Kv4.3 KCND3; Kv1.4 KCNA4; Kv1.3 KCNA3; Kv1.5 KCNA5; Kv3.3 KCNC3; Cav3.2 CACNA1H; Cav3.1 CACNA1G; Cav3.3 CACNA1I; Cav1.3 CACNA1D; Cav1.1 CACNA1S; Cav1.2 CACNA1C; KCa2.1 KCNN1; Kv2.1 KCNB1; Kv3.1 KCNC1; HCN Cnga1; Cav2.1 CACNA1A; Cav2.2 CACNA1B; KCa2.2 KCNN2; Kv1.9 Kv7.1 KCNQ1; IRK; NR2A GRIN2A; NR2B GRIN2B; Kv3.4 KCNC4; Kv4.1 KCND1;
Transmitter(s): Gaba; Glutamate; Ions;
Simulation Environment: MATLAB;
Model Concept(s): Intrinsic plasticity;
Implementer(s): Schumann, Johann [johann.schumann at gmail.com];
Search NeuronDB for information about:  Neostriatum medium spiny direct pathway GABA cell; Neostriatum medium spiny indirect pathway GABA cell; GabaA; AMPA; NMDA; I A; I K; I h; I K,Ca; I Calcium; I A, slow; I Cl, leak; I Ca,p; Gaba; Glutamate; Ions;
%
% 	generate correlated input and plot analysis and explanation
%
%	$Revision:$
%

function plot_corr_analysis(FN, all_nn_inputs, sim)


figure;

for r=1:3,
	idx=1+(r-1)*4;
	
	subplot(3,3,r);
	
	for i=1:10,
		plot([i*(idx-1),i*(idx-1)+5],[i,i], 'LineWidth', 5);
		hold on;
		end;
	axis([0, 100,  -1, 11]);
	ylabel('Superposition');
	%set(gca,'Visible','off');

	subplot(3,3,3+r);

	plot(all_nn_inputs(idx,:));
	axis([0,1000,-0.1,5])
	%set(gca,'Visible','off');
	ylabel('I_s');
	xlabel('t[ms]');

	subplot(3,3,6+r);
%	z=fft(detrend(all_nn_inputs(idx,:)));
	z=fft(detrend(all_nn_inputs(idx,:)./mean(all_nn_inputs(idx,:))));
	zz=z.*conj(z);
%	semilogx(2:100,zz(2:100));
	semilogx(2:50,zz(2:50));
	xlabel('frequency [Hz]');
	ylabel('Power spectrum');
	axis([2,60,0,2e5]);

	end;

%------------------------------------------------------------------
% print the stuff to file
%------------------------------------------------------------------
fn_eps =sprintf('%s.eps', FN);
print('-depsc', fn_eps);
fn_jpg =sprintf('%s.jpg', FN);
print('-djpeg', fn_jpg);
fn_tiff =sprintf('%s.tiff', FN);
print('-dtiff', fn_tiff);
fn_png =sprintf('%s.png', FN);
print('-dpng','-r72', fn_png);




Loading data, please wait...