Learning intrinsic excitability in Medium Spiny Neurons (Scheler 2014)

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"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. ... "
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;
%	plot a figure on the gains
%	$Revision:$

%	inp_dc = [zeros(size(inp_dc,1)) inp_dc];
%	out_freq= [ zeros(N_gain_inp,1) out_freq];
%	out_freq_ss= [ zeros(N_gain_inp,1) out_freq_ss];
	theinput = [zeros(size(inp_mean,2),1) inp_mean'];
	mout_freq= [ zeros(size(out_freq,2),1) out_freq'];
	mout_freq_ss= [ zeros(size(inp_mean,1)) out_freq_ss];

if (sim.nA_units),
	theinput = theinput * sim.nA;

for i=1:sim.N_nn,
        plot(-theinput(i,b),mout_freq(i,b)', 'Marker', markers(i), ...
		'Linewidth', 1.5, 'Color', col(i,:));
        hold on;
if (nA_units),
	xlabel('Input magnitude [nA]', 'FontSize', [16]);
	xlabel('Input magnitude [\muAscm^{-2}]', 'FontSize', [16]);
ylabel('Firing rate [Hz]', 'FontSize', [16]);
%legend({ sprintf('%.1f',thispar(1)), 
%         sprintf('%.1f',thispar(2)), 
%	 sprintf('%.1f',thispar(3)), 
%         sprintf('%.1f',thispar(4)), 
%         sprintf('%.1f',thispar(5))} ...
% , 'Location','SouthEast');
%%legend({ 'N1', 'N2', 'N3', 'N4', 'N5'} ...
%% , 'Location','NorthWest');

% 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);

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