Learning intrinsic excitability in Medium Spiny Neurons (Scheler 2014)

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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;
%
% 	input analysis
%
%	$Revision:$
%
function inp_analysis(inp)

fprintf('Input analysis...\n');

N=sim.T_upd;

filterlength = floor(N/10);

used_inp = inp(input_params.start+1:end);
finp = filter(ones(1,filterlength)/filterlength,1,[mean(used_inp(1:filterlength))*ones(filterlength,1); used_inp]);

used_finp = finp(filterlength+1:end);

inp_amount = -mean(used_inp)*sim.ts;
inp_txt = sprintf('|I_s| = %.2f\\muAscm^{-2}', inp_amount);

figure;
subplot(2,1,1);
plot([-used_inp -used_finp]);
text(0,0, inp_txt, 'FontSize',[12]);

z=fft(detrend(inp));
zz=z.*conj(z);
subplot(2,1,2);
%plot(20*log(zz(1:100)));
%plot(zz(1:100));
semilogx(2:100,zz(2:100));
xlabel('frequency [Hz]');
ylabel('Power spectrum');


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