Robust transmission in the inhibitory Purkinje Cell to Cerebellar Nuclei pathway (Abbasi et al 2017)

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1 . Abbasi S, Hudson AE, Maran SK, Cao Y, Abbasi A, Heck DH, Jaeger D (2017) Robust Transmission of Rate Coding in the Inhibitory Purkinje Cell to Cerebellar Nuclei Pathway in Awake Mice PLOS Computational Biology
2 . Steuber V, Schultheiss NW, Silver RA, De Schutter E, Jaeger D (2011) Determinants of synaptic integration and heterogeneity in rebound firing explored with data-driven models of deep cerebellar nucleus cells. J Comput Neurosci 30:633-58 [PubMed]
3 . Steuber V, Jaeger D (2013) Modeling the generation of output by the cerebellar nuclei. Neural Netw 47:112-9 [PubMed]
4 . Steuber V, De Schutter E, Jaeger D (2004) Passive models of neurons in the deep cerebellar nuclei: the effect of reconstruction errors Neurocomputing 58-60:563-568
5 . Luthman J, Hoebeek FE, Maex R, Davey N, Adams R, De Zeeuw CI, Steuber V (2011) STD-dependent and independent encoding of input irregularity as spike rate in a computational model of a cerebellar nucleus neuron. Cerebellum 10:667-82 [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: Cerebellum;
Cell Type(s): Cerebellum deep nucleus neuron;
Channel(s): I h; I T low threshold; I L high threshold; I Na,p; I Na,t; I K,Ca; I K;
Gap Junctions:
Receptor(s): AMPA; NMDA; GabaA;
Transmitter(s): Gaba; Glutamate;
Simulation Environment: GENESIS;
Model Concept(s): Synaptic Integration;
Implementer(s): Jaeger, Dieter [djaeger at];
Search NeuronDB for information about:  GabaA; AMPA; NMDA; I Na,p; I Na,t; I L high threshold; I T low threshold; I K; I h; I K,Ca; Gaba; Glutamate;
function [V,t,Err] = evoked(data,Fs,win,width,plt,err)
% Function to calculate the evoked response given continuous data in the
% form time x channels
% Usage [V,t,Err] = evoked(data,Fs,win,width,plt,err)
% Inputs  
%   Note that all times can be in arbitrary units. But the units have to be
%   consistent. So, if win is in secs, width is in secs and Fs has to be Hz. 
%   If win is in samples, so is width and Fs=1.
%    data(times, channels/trials or a single vector)      (required)    
%    Fs  sampling frequency            (required)
%    win   subsection of data to be used. Default all available data
%    width (s) of smoothing kernel. Default 50 samples                  
%    plt plot 'n' for no plot, otherwise plot color. Default blue colored lines.                                  
%    err = 0/1. Default 1=calculate bootstrap errorbars.                     
% Outputs                                             
%    V = evoked potential                                 
%    t = times of evaluation                              
%    Err = bootstrap statdard deviation                   

if nargin < 2;error('Data, sampling frequency required');end
if nargin <3; win = [0 (N-1)/Fs];end
if nargin <4; width = 50/Fs;end
if nargin <5; plt = 'b';end
if nargin <6;err = 1;end
if isempty(T); T = [0 (N-1)/Fs];end
if isempty(width); width = 50/Fs;end
if isempty(plt); plt = 'b';end
if isempty(err);err = 1;end

t = min(T):1/Fs:max(T);
if nargin >= 5
  indx = find(t>T(1) & t<T(2));
  t = t(indx);
  data = data(:,indx);

if width > (t(length(t))-t(1))/2
  disp('Width is too large for data segment: should be in seconds')
  disp('Turn off smoothing')
  width = 0;

s = t(2)-t(1);
N = fix(width/s);
NT = length(data(:,1));

if NT > 1
    mdata = mean(data);
    mdata = data;
if N > 4
  smdata = locsmooth(mdata,N,fix(N/2)); 
  smdata = mdata;  
% if errorbars requested then do a bootstrap over trials...

Err = 0;
if NT < 4; 
  disp('Too few trials: no errorbars calculated')
  err = 0;    

if err ~= 0 && NT > 1
  Nboot = 10;
  bevk = 0;
  sevk = 0;
  for b=1:Nboot
    indx = floor(NT*rand(1,NT)) + 1;
    evktmp = mean(data(indx,:));
    if N > 4
      evktmp = locsmooth(evktmp,N,fix(N/2));
    bevk = bevk + evktmp;
    sevk = sevk + evktmp.^2;
  stdevk = sqrt((sevk/Nboot - bevk.^2/Nboot^2));
  Err = stdevk;

V = smdata;
if plt ~= 'n'
  hold on
  mn = mean(smdata);
  ax = get(gca,'xlim');
  line(ax,mn*[1 1],'color','k')
  if err
    line(ax,(mn+2*mean(stdevk))*[1 1],'color','r')
    line(ax,(mn-2*mean(stdevk))*[1 1],'color','r')
    hold off