Dopamine-modulated medium spiny neuron, reduced model (Humphries et al. 2009)

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We extended Izhikevich's reduced model of the striatal medium spiny neuron (MSN) to account for dopaminergic modulation of its intrinsic ion channels and synaptic inputs. We tuned our D1 and D2 receptor MSN models using data from a recent (Moyer et al, 2007) large-scale compartmental model. Our new models capture the input-output relationships for both current injection and spiking input with remarkable accuracy, despite the order of magnitude decrease in system size. They also capture the paired pulse facilitation shown by MSNs. Our dopamine models predict that synaptic effects dominate intrinsic effects for all levels of D1 and D2 receptor activation. Our analytical work on these models predicts that the MSN is never bistable. Nonetheless, these MSN models can produce a spontaneously bimodal membrane potential similar to that recently observed in vitro following application of NMDA agonists. We demonstrate that this bimodality is created by modelling the agonist effects as slow, irregular and massive jumps in NMDA conductance and, rather than a form of bistability, is due to the voltage-dependent blockade of NMDA receptors
1 . Humphries MD, Lepora N, Wood R, Gurney K (2009) Capturing dopaminergic modulation and bimodal membrane behaviour of striatal medium spiny neurons in accurate, reduced models. Front Comput Neurosci 3:26 [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:
Cell Type(s): Neostriatum medium spiny direct pathway GABA cell;
Gap Junctions:
Receptor(s): D1; D2; GabaA; AMPA; NMDA;
Transmitter(s): Dopamine;
Simulation Environment: MATLAB;
Model Concept(s): Action Potential Initiation; Parameter Fitting; Simplified Models; Parkinson's; Bifurcation;
Implementer(s): Humphries, Mark D [m.d.humphries at];
Search NeuronDB for information about:  Neostriatum medium spiny direct pathway GABA cell; D1; D2; GabaA; AMPA; NMDA; Dopamine;
function aic = AIC(SS,N,K)

%AIC Akaike's Information Criterion for curve-fitting model comparison
%   AIC(SS,N,K) where SS is the sum of squared residuals (as returned by e.g. lsqcurvefit)
%   N is the number of data points and K is the number of coefficients. 
%   Computes and returns the corrected AIC score for the model.
%   The model with the lowest AIC value is the best fit.
%   References: (1) Motulsky, H. and Christopoulos, A. (2003) Fitting models to biological data using 
%   linear and nonlinear regression. A practical guide to curve fitting. Graphpad Software Inc., San Diego CA.
%   (2) Akaike, H. (1974) "A new look at the statistical model identification." IEEE Transactions on Automatic Control, AC-19, 716-723
%   (3) Hurvich, C. M., and Tsai, C-L. (1989). Regression and time series model selection in small samples. Biometrika, 76, 297-307.
%   [the AIC correction]   
%   NOTE: this computes AIC from sum-of-squares (SS), and thus uses SS as
%   an esimator for the maximum likelihood; when actually fitting
%   distributions using MLE, then use AICL instead!
%   Mark Humphries 11/10/2004

K  = K + 1; % additional degree-of-freedom is model!

% raw AIC
aic = N .* log(SS./N) + 2 .* K;

% apply correction in case N close to K
aic = aic + (2.*K.*(K+1)) ./ (N - K - 1);

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