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;
% run gain simulations
%  * nested for-loop over input magnitude
%	$Revision:$
%exc_Mp = [200,300,400,500,900];
%inh_Mn = [20, 40,60, 100, 300, 500];

N_gain_inp =length(exc_Mp);

fprintf('starting gain experiment with %d neuron types and %d inputs\n',...
	sim.N_nn, N_gain_inp);

for inp_idx =1:N_gain_inp,
	input_params.g0 = -2;
	input_params_inh.g0 = -2;
	input_params.Mp = exc_Mp(inp_idx);
	input_params_inh.Mn = inh_Mn(inp_idx);

	fprintf('\nRunning neuron group with Mp=%d Mn=%d:\n',...
		exc_Mp(inp_idx), inh_Mn(inp_idx));


        m_inp = mean(nn_inputs(1,:)+nn_inputs(2,:),2);
        inp_mag(inp_idx,1:sim.N_nn) = m_inp;
        inp_mag_exc(inp_idx,1:sim.N_nn) = mean(nn_inputs(1,:));
        inp_mag_inh(inp_idx,1:sim.N_nn) = mean(nn_inputs(2,:));

	for i = 1:sim.N_nn,
% 	   m_Cai = mean(sim.instrument.I_Channels(i).Cai(max(1,end-100):end));

	[spi, spt, act] = calc_spiketrain(reshape(sim.instrument.allvm(1,i,off:end),1,sim.T_upd-off+1), sim);

	out_act(inp_idx,i) = act;
	iisi = [spi sim.T_upd] - [0 spi];
	m_isi = mean(iisi(2:end-1));
	s_isi = std(iisi(2:end-1));

        if (~isnan(m_isi) && ((m_isi > 0.0001) || (m_isi ~= -99))),
                out_freq(inp_idx,i) = 1000/m_isi;
                out_freq(inp_idx,i) = 0;

	out_early(inp_idx,i) = 0;
        if (~isempty(spi)),
          if (spi(end) < 0.8*sim.T_upd),
		out_early(inp_idx,i) = 1;
                fprintf('stopped too early\n\n');

        mm = mean(iisi(max(1,length(iisi)-10):end));
        if (mm==0),
                out_freq_ss(inp_idx,i) = 0;
                out_freq_ss(inp_idx,i) = 1000/mm;

	out_Cai(inp_idx,i) = mean(sim.instrument.I_Channels(i).Cai);
	inp_vm(inp_idx,i) = ...
	inp_mean(inp_idx,i) = m_inp;


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