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;
% 	init_neuron_nmda3_constcai.m
%
% initialize neuron state vector
%
%  with EBIO parameter
%
%	$Revision:$
%
function [sim, nn_params, l_param, nn_mu_params] = init_neuron(sim)

N_states = 18;
%	V_0		1
%	m0_na		2
%	h0_na		3
%	n0_k		4
%	m0_CaL		5
%	h0_CaL		6
%	m0_kas		7
% 	h0_kas		8
% 	m0_nas		9
%	m0_kir		10
% 	m0_kaf		11
% 	h0_kaf		12
% 	m0_AHP		13
% 	m0_m		14
% 	Cai_0		15
% 	s1_nmda		16
% 	s2_nmda		17
% 	m0_h		18

N_params = 13;
 	% 1 K
        % 2 CaL
        % 3 KAs
        % 4 Na
        % 5 NaS
        % 6 Kaf
        % 7 Kir 
        % 8 AHP
	% 9 M
	% 10 mu_NMDA (for Cai)
	% 11 mu_EBIO  (Cai <-> SK)
	% 12 NMDA strength (I_NMDA = par(12)*nmda_in)
	% 13 H



l_param = 20 + N_params;

nn_params = zeros(1,l_param);

%
% start values
%
V_0  = -74.6;	% mV

Cai_0 = 0;

[a,b,c,m0_na, h0_na ] = ina(V_0,0,0);
[a,b,n0_k ] = ik(V_0,0);
%% [a,b,c,m0_CaL, h0_CaL ] = ical(V_0,0,0);
[a,b,c,m0_CaL, h0_CaL ] = ica_traub(V_0,0,0);

[a,b,c,m0_kas, h0_kas ] = ikas(V_0,0,0);

[a,b,m0_nas ] = inap(V_0,0);
[a,b,m0_kir ] = ikir(V_0,0);
[a,b,c,m0_kaf, h0_kaf ] = ikaf(V_0,0,0);


[a,b,m0_AHP] = iAHP(V_0, 0, Cai_0);

[a, b, m0_m ] = im(V_0,0);

[a, b, m0_h ] = ih(V_0,0);

nmda_0 = 0;

	%
	% set the channel states
	%
nn_params(1:N_states)=[V_0, m0_na, h0_na, n0_k, m0_CaL, h0_CaL, ...
	m0_kas, h0_kas, m0_nas,m0_kir, m0_kaf, h0_kaf, m0_AHP, m0_m, Cai_0, nmda_0, nmda_0, m0_h]';

	%
	% standard neuron has mu's of 1.0
	%
nn_params(21:20+N_params) = 1.0;

sim.N_states = N_states;
sim.N_params = N_params;

sim.integration = 'ode';

fprintf('setting mu parameters =1.0...\n');
nn_mu_params=zeros(sim.N_nn,sim.N_params)+1;

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