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 [J,Msp,Nsp]=mtfftpb(data,tapers,nfft)
% Multi-taper fourier transform - binned point process data
% Usage:
% [J,Msp,Nsp]=mtfftpb(data,tapers,nfft) - all arguments required
% Input: 
%       data   (in form samples x channels/trials or single vector) 
%       tapers (precalculated tapers from dpss)  
%       nfft   (length of padded data)
% Output:
%       J (fft in form frequency index x taper index x channels/trials)
%       Msp (number of spikes per sample in each channel)
%       Nsp (number of spikes in each channel)

if nargin < 3; error('Need all input arguments'); end;
data=change_row_to_column(data); % changes data stored as a row vector to a column vector
[N,C]=size(data); % size of data
K=size(tapers,2); % size of tapers
tapers=tapers(:,:,ones(1,C)); % add channel indices to tapers
H=fft(tapers,nfft,1); % fourier transform of the tapers
Nsp=sum(data,1); % number of spikes in each channel
Msp=Nsp'./N; % mean rate for each channel
meansp=Msp(:,ones(1,K),ones(1,size(H,1)));  % add taper and frequency indices to meansp
meansp=permute(meansp,[3,2,1]); % permute to get meansp with the same dimensions as H
data=data(:,:,ones(1,K));% add taper indices to the data
data=permute(data,[1 3 2]); % permute data to be of the same dimensions as H
data_proj=data.*tapers; % multiply data by the tapers
J=fft(data_proj,nfft,1); % fft of projected data
J=J-H.*meansp; % subtract the dc