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

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Accession:229279

Reference:
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]
Citations  Citation Browser
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;
Gene(s):
Transmitter(s): Gaba; Glutamate;
Simulation Environment: GENESIS;
Model Concept(s): Synaptic Integration;
Implementer(s): Jaeger, Dieter [djaeger at emory.edu];
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 data=locdetrend(data,Fs,movingwin)
%  Remove running line fit (using local linear regression)-continuous
%  processes
%  Usage: data=locdetrend(data,Fs,movingwin)
%  Inputs:
%  Note that units of Fs, movinwin have to be consistent.
%  data         (data as a matrix times x channels or a single vector) 
%  Fs           (sampling frequency) - optional. Default 1
%  movingwin    (length of moving window, and stepsize) [window winstep] - optional.
%                   Default. window=full length of data (global detrend).
%                   winstep=window -- global detrend
% 
% Output:
% data:         (locally detrended data)
data=change_row_to_column(data);
[N,C]=size(data);
if nargin < 2 || isempty(Fs); Fs=1; end;
if nargin < 3 || isempty(movingwin); movingwin=[N/Fs N/Fs]; end;
Tw=movingwin(1); Ts=movingwin(2);
if Ts>Tw; error('Use step size shorter than window size'); end;
n=round(Fs*Tw);
dn=round(Fs*Ts);
if ~isreal(data) 
  yr=real(data); 
  yi=imag(data);
  if n==N;
     yr=detrend(yr);
     yi=detrend(yi);
     data=yr+i*yi;
  else;
     for ch=1:C
         tmp=runline(yr(:,ch),n,dn); 
         yr=yr-tmp;
         tmp=runline(yi(:,ch),n,dn); 
         yi=yi-tmp;
         data(:,ch)=yr+i*yi;
     end;
  end;
else
  if n==N;
     data=detrend(data);
  else;
     for ch=1:C;
         tmp=runline(data(:,ch),n,dn); 
         data(:,ch)=data(:,ch)-tmp;
     end;
  end
end