Pyramidal neurons with mutated SCN2A gene (Nav1.2) (Ben-Shalom et al 2017)

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Model of pyramidal neurons that either hyper or hypo excitable due to SCN2A mutations. Mutations are taken from patients with ASD or Epilepsy
1 . Ben-Shalom R, Keeshen CM, Berrios KN, An JY, Sanders SJ, Bender KJ (2017) Opposing Effects on NaV1.2 Function Underlie Differences Between SCN2A Variants Observed in Individuals With Autism Spectrum Disorder or Infantile Seizures. Biol Psychiatry 82:224-232 [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): Neocortex L5/6 pyramidal GLU cell;
Channel(s): I Na,t; I Sodium; I K;
Gap Junctions:
Gene(s): Nav1.2 SCN2A;
Simulation Environment: NEURON; MATLAB;
Model Concept(s): Epilepsy; Autism spectrum disorder;
Implementer(s): Ben-Shalom, Roy [rbenshalom at];
Search NeuronDB for information about:  Neocortex L5/6 pyramidal GLU cell; I Na,t; I K; I Sodium;
function [vrev fitresult, gof] = T_TypeFit(StimAct, peaksActNa)
%  Create a fit.
%  Data for 'T_Type' fit:
%      X Input : StimAct
%      Y Output: peaksActNa
%  Output:
%      fitresult : a fit object representing the fit.
%      gof : structure with goodness-of fit info.
%  See also FIT, CFIT, SFIT.

%  Auto-generated by MATLAB on 29-Jul-2016 13:43:19

%% Fit: 'T_Type'.
[xData, yData] = prepareCurveData( StimAct, peaksActNa );

% Set up fittype and options.
ft = fittype( 'gmax*(x-vrev)/(1+exp((vhalf-x)/k))', 'independent', 'x', 'dependent', 'y' );
opts = fitoptions( 'Method', 'NonlinearLeastSquares' );
opts.Display = 'Off';
opts.StartPoint = [1 5 -30 55];

% Fit model to data.
[fitresult, gof] = fit( xData, yData, ft, opts );
temp = coeffvalues(fitresult);
vrev = temp(4);

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