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 [C,phi,S12,S1,S2,f,confC,phistd,Cerr]=coherencysegc(data1,data2,win,params)
% Multi-taper coherency, cross-spectrum and individual spectra with segmenting - continuous process
%   computed by segmenting two univariate time series into chunks
% Usage:
% [C,phi,S12,S1,S2,f,confC,phistd,Cerr]=coherencysegc(data1,data2,win,params)
% Input: 
% Note units have to be consistent. See chronux.m for more information.
%       data1 (column vector) -- required
%       data2 (column vector) -- required
%       win   (length of segments) - required
%       params: structure with fields tapers, pad, Fs, fpass, err
%       - optional
%           tapers : precalculated tapers from dpss or in the one of the following
%                    forms: 
%                    (1) A numeric vector [TW K] where TW is the
%                        time-bandwidth product and K is the number of
%                        tapers to be used (less than or equal to
%                        2TW-1). 
%                    (2) A numeric vector [W T p] where W is the
%                        bandwidth, T is the duration of the data and p 
%                        is an integer such that 2TW-p tapers are used. In
%                        this form there is no default i.e. to specify
%                        the bandwidth, you have to specify T and p as
%                        well. Note that the units of W and T have to be
%                        consistent: if W is in Hz, T must be in seconds
%                        and vice versa. Note that these units must also
%                        be consistent with the units of params.Fs: W can
%                        be in Hz if and only if params.Fs is in Hz.
%                        The default is to use form 1 with TW=3 and K=5
%	        pad		    (padding factor for the FFT) - optional (can take values -1,0,1,2...). 
%                    -1 corresponds to no padding, 0 corresponds to padding
%                    to the next highest power of 2 etc.
%			      	 e.g. For N = 500, if PAD = -1, we do not pad; if PAD = 0, we pad the FFT
%			      	 to 512 points, if pad=1, we pad to 1024 points etc.
%			      	 Defaults to 0.
%           Fs   (sampling frequency) - optional. Default 1.
%           fpass    (frequency band to be used in the calculation in the form
%                                   [fmin fmax])- optional. 
%                                   Default all frequencies between 0 and Fs/2
%           err  (error calculation [1 p] - Theoretical error bars; [2 p] - Jackknife error bars
%                                   [0 p] or 0 - no error bars) - optional. Default 0.
% Output:
%       C (magnitude of coherency - frequencies x segments if segave=0; dimension frequencies if segave=1)
%       phi (phase of coherency - frequencies x segments if segave=0; dimension frequencies if segave=1)
%       S12 (cross spectrum -  frequencies x segments if segave=0; dimension frequencies if segave=1)
%       S1 (spectrum 1 - frequencies x segments if segave=0; dimension frequencies if segave=1)
%       S2 (spectrum 2 - frequencies x segments if segave=0; dimension frequencies if segave=1)
%       f (frequencies)
%       confC (confidence level for C at 1-p %) - only for err(1)>=1
%       phistd - theoretical/jackknife (depending on err(1)=1/err(1)=2) standard deviation for phi 
%                Note that phi + 2 phistd and phi - 2 phistd will give 95% confidence
%                bands for phi - only for err(1)>=1 
%       Cerr  (Jackknife error bars for C - use only for Jackknife - err(1)=2)

if nargin < 3; error('Need data1 and data2 and size of segment'); end;
if nargin < 4; params=[]; end;
clear tapers pad fpass trialave
if nargout > 8 && err(1)~=2; 
    error('Cerr computed only for Jackknife. Correct inputs and run again');
if nargout > 6 && err(1)==0;
%   Errors computed only if err(1) is nonzero. Need to change params and run again. 
    error('When errors are desired, err(1) has to be non-zero.');
if size(data1,2)~=1 || size(data2,2)~=1; error('works for only univariate time series'); end;


dt=1/Fs; % sampling interval
T=N*dt; % length of data in seconds
E=0:win:T-win; % fictitious event triggers
win=[0 win]; % use window length to define left and right limits of windows around triggers
data1=createdatamatc(data1,E,Fs,win); % segmented data 1
data2=createdatamatc(data2,E,Fs,win); % segmented data 2
if err==0;
   [C,phi,S12,S1,S2,f]=coherencyc(data1,data2,params); % compute coherency for segmented data
elseif err(1)==1;
   [C,phi,S12,S1,S2,f,confC,phistd]=coherencyc(data1,data2,params); % compute coherency for segmented data
elseif err(1)==2;
   [C,phi,S12,S1,S2,f,confC,phistd,Cerr]=coherencyc(data1,data2,params); % compute coherency for segmented data