Extraction and classification of three cortical neuron types (Mensi et al. 2012)

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This script proposes a new convex fitting procedure that allows the parameters estimation of a large class of stochastic Integrate-and-Fire model upgraded with spike-triggered current and moving threshold from patch-clamp experiments (i.e. given the injected current and the recorded membrane potential). This script applies the method described in the paper to estimate the parameters of a reference model from a single voltage trace and the corresponding input current and evaluate the performance of the fitted model on a separated test set.
1 . Mensi S, Naud R, Pozzorini C, Avermann M, Petersen CC, Gerstner W (2012) Parameter extraction and classification of three cortical neuron types reveals two distinct adaptation mechanisms. J Neurophysiol 107:1756-75 [PubMed]
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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: Neocortex;
Cell Type(s): Hippocampus CA1 pyramidal GLU cell; Hippocampus CA3 pyramidal GLU cell; Neocortex fast spiking (FS) interneuron; Neocortex spiking regular (RS) neuron;
Gap Junctions:
Simulation Environment: MATLAB;
Model Concept(s):
Implementer(s): Mensi, Skander [skander.mensi at epfl.ch];
Search NeuronDB for information about:  Hippocampus CA1 pyramidal GLU cell; Hippocampus CA3 pyramidal GLU cell;
function [output] = Extract_interval(input,spiketimes,dt)
%   Remove the spikes from the data
%   input = vector of real value (i.e. current or voltage)
%   spiketimes = index of the spikes
%   dt = period of time that are removed following each spikes

    output = input;
    output = input(1:spiketimes(1));
    for i=1:length(spiketimes)-1
        if(spiketimes(i) + dt <= spiketimes(i+1))
            output = [output;input(spiketimes(i)+dt:spiketimes(i+1))];

    output = [output;input(spiketimes(end)+dt:end)];