ModelDB is moving. Check out our new site at The corresponding page is

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

 Download zip file 
Help downloading and running models

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]
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 [S,f,varS,C,Serr]=mtspectrumsegc(data,win,params,segave)
% Multi-taper segmented spectrum for a univariate continuous process
% Usage:
% [S,f,varS,C,Serr]=mtspectrumsegc(data,win,params,segave)
% Input: 
% Note units have to be consistent. See chronux.m for more information.
%       data (single channel) -- required
%       win  (duration of the segments) - required. 
%       params: structure with fields tapers, pad, Fs, fpass, err, trialave
%       - 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.
%           trialave - not used
%       segave - optional 0 for don't average over segments, 1 for average - default
%       1
% Output:
%       S       (spectrum in form frequency x segments if segave=0; in the form frequency if segave=1)
%       f       (frequencies)
%       varS    (variance of the log spectrum)
%       C       (covariance matrix of the log spectrum - frequency x
%       frequency matrix)
%       Serr    (error bars) only for err(1)>=1

if nargin < 2; error('Need data and segment information'); end;
if size(data,2)~=1; error('works for only univariate time series'); end;
if nargin < 3 ; params=[]; end;
if nargin < 4 || isempty(segave); segave=1; end;
[tapers,pad,Fs,fpass,err,trialave,params]=getparams(params); clear trialave params
if nargout==4 && err(1)==0; 
%   Errors can't be computed if err(1)=0. Need to change params and run again.
    error('When Serr is desired, err(1) has to be non-zero.');
N=size(data,1); % length of segmented data
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
data=createdatamatc(data,E,Fs,win); % segmented data
N=size(data,1); % length of segmented data
tapers=dpsschk(tapers,N,Fs); % check tapers
J=mtfftc(data,tapers,nfft,Fs); % compute tapered fourier transforms
J=J(findx,:,:); % restrict to specified frequencies
S=squeeze(mean(conj(J).*J,2)); % spectra of non-overlapping segments (average over tapers)
if segave==1; SS=squeeze(mean(S,2)); else; SS=S;end; % mean of the spectrum averaged across segments
if nargout > 2
    lS=log(S); % log spectrum for nonoverlapping segments
    varS=var(lS',1)'; % variance of log spectrum
%     varS=var(lS',1)';% variance of the log spectrum R13
    if nargout > 3
       C=cov(lS'); % covariance matrix of the log spectrum
       if nargout==5; 

Loading data, please wait...