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=createdatamatc(data,E,Fs,win)
% Helper function to create an event triggered matrix from univariate
% continuous data
% Usage: data=createdatamatc(data,E,Fs,win)
% Inputs:
% data   (input time series as a column vector) - required
% E      (events to use as triggers) - required 
% Fs     (sampling frequency of data) - required
% win    (window around triggers to use data matrix -[winl winr]) - required 
%          e.g [1 1] uses a window starting 1 * Fs samples before E and
%              ending 1*Fs samples after E.
% Note that E, Fs, and win must have consistent units 
%
% Outputs:
% data      (event triggered data)
%
if nargin < 4; error('Need all arguments'); end;
NE=length(E);
nwinl=round(win(1)*Fs);
nwinr=round(win(2)*Fs);
nE=floor(E*Fs)+1;
datatmp=[];
for n=1:NE;
    indx=nE(n)-nwinl:nE(n)+nwinr-1;
    datatmp=[datatmp data(indx)];
end
data=datatmp;