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
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;
% 	neuron_nmda3_constcai.m
% 	like nmda3 with V(14) == const
%
%	$Revision:$
%
function dot_state = neuron_nmda3(t, state)

global I_S;
global Ts;
global par;

ct = floor(t/Ts);

C_m = 1;

V_M = state(1);
dot_state=zeros(length(state),1);

I_L = ileak(V_M);
[I_Na, dot_state(2), dot_state(3)] = ina(V_M, state(2), state(3));
[I_K, dot_state(4)] = ik(V_M, state(4));

%%[I_CaL, dot_state(5), dot_state(6)] = ical(V_M, state(5), state(6));
[I_CaL, dot_state(5), dot_state(6)] = ica_traub(V_M, state(5), state(6));

[I_KAs, dot_state(7), dot_state(8)] = ikas(V_M, state(7), state(8));

[I_NaS, dot_state(9)] = inap(V_M, state(9));
[I_Kir, dot_state(10)] = ikir(V_M, state(10));
[I_Kaf, dot_state(11), dot_state(12)] = ikaf(V_M, state(11), state(12));


	% V_M, m, Cai
[I_AHP, dot_state(13)] = iAHP(V_M, state(13), par(11)*state(14));

	% NMDA
[nmda_in, dot_state(16), dot_state(17)] = ...
	iNMDAdd(V_M, state(16), state(17), I_S, ct);

	% Cai:
dot_state(14) = 0;


[I_M, dot_state(15)] = im(V_M, state(15));

[I_H, dot_state(18)] = ih(V_M, state(18));





dot_state(1) = -(1/C_m)*...
 (par(1)*I_K + par(2)*I_CaL + par(3)*I_KAs + ...
  par(4)*I_Na + par(5)*I_NaS + par(6)*I_Kaf + par(7)*I_Kir + ...
  par(8)*I_AHP +  par(9)*I_M + par(12)*nmda_in + ...
  par(13)*I_H + ...
  I_L + I_S(1,ct) + I_S(2,ct));

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