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;
% 	script: plot synaptic input (current) vs. synaptic input (conductance)
%
% 	based upon data from sr_kas.* input and simulation data
%
%	$Revision:$
%

FN='sr_kas_conduct_corrW_001_02';
FN_plot='i_g_corr.eps';
offset = 1;
ax_lim = [0,500,-0.2,0.1];

%FN='sr_kas_conduct_uncorrW_001_06';
%FN_plot='i_g_uncorr.eps';
%offset=1500;
%ax_lim = [0,500,-0.05,0.02];

%----------------------------------
fn=sprintf('%s.dat', FN);
load(fn, '-mat');

selected_neuron = 1;
nn=selected_neuron;
LW=2;

figure;


vm=reshape(sim.instrument.allvm(1,nn,offset:sim.T_upd),1, sim.T_upd-offset+1);
vm(find(vm==0)) = 1e-6;

% V = I * R = I /G
% G = I / V
%
nn_input_conduct = -nn_inputs(nn,offset:end) ./ vm;

subplot(2,1,1);
plot(nn_input_conduct(1:end-1), 'k-', 'Linewidth',LW);
%set(gca,'Visible','off');
axis(ax_lim);
ylabel('mS/cm^2');

subplot(2,1,2);
plot(-nn_inputs(nn,offset:end), 'Linewidth',LW);
%set(gca,'Visible','off');
ylabel('\muA/cm^2');
ax=axis;
ax(2)=500;
axis(ax);

print('-deps', FN_plot);

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