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;
% init_neuron_izh
% initialize neuron state vector
%	$Revision:$
function [sim, nn_params, l_param, nn_mu_params] = init_neuron_izh(sim, varargin)

N_states = 2;
%	V		1
%	U

N_params = 6;
 	% 1 a
        % 2 b
        % 3 c
        % 4 d
	% 5 mu-excit
	% 5 mu-inh

sim.nn_parnames = [ ...
{'a'}, ...
{'b'}, ...
{'c'}, ...
{'d'}, ...
{'mu-excit'}, ...
{'mu-inh'} ...

l_param = 20 + N_params;

nn_params = zeros(1,l_param);

if (nargin > 1),
	nn_mu_params_in = varargin{:};
	nn_mu_params_in = zeros(N_params);

% start values
V_0  = (1/0.08)*(-(5-nn_mu_params_in(1,2).^2) - ...
	    sqrt(abs((5-nn_mu_params_in(1,2)).^2 - 4*0.04*140)));
U  = nn_mu_params_in(1,2)*V_0;


	% 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 = 'euler';


nn_mu_params(:,1) = 0.02;
nn_mu_params(:,2) = 0.2;
nn_mu_params(:,3) = -65;
nn_mu_params(:,4) = 8;
nn_mu_params(:,5) = 5;
nn_mu_params(:,6) = 5;

	% some global settings
sim.get_channels = 0;

	% avoid multiple counting of "longer" peaks
sim.activity_win = 7;

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