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]
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 [SS,tau]=mtspectrum_of_spectrumc(data,win,tapers2spec,params)
% Multi-taper segmented, second spectrum (spectrum of the log spectrum) for a continuous process
% This routine computes the second spectrum by explicitly evaluating the
% Fourier transform (since the spectrum is symmetric in frequency, it uses
% a cosine transform)
% Usage:
% [SS,tau]=mtspectrum_of_spectrumc(data,win,tapers2spec,params)
% Input: 
% Note units have to be consistent. See chronux.m for more information.
%       data (single channel) -- required
%       win  (duration of the segments) - required. 
%       tapers2spec (tapers used for the spectrum of spectrum computation) -
%       required in the form [use TW K] - Note that spectrum of the
%       spectrum involves computing two Fourier transforms. While the first
%       transform (of the original data) is always computed using the
%       multi-taper method, the current routine allows the user to specify 
%       whether or not to use this method for the second transform. use=1
%       means use tapers, use=anything other than 1 means do not use the
%       multitaper method. If use=1, then tapers2spec controls the
%       smoothing for the second Fourier transform. Otherwise, a direct
%       Fourier transform is computed.
%       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
% Output:
%       SS       (second spectrum in form frequency x segments x trials x channels 
%                if segave=0; in the form frequency x trials x channels if segave=1)
%       tau      (frequencies)
if nargin < 3; error('Need data,segment duration and taper information'); end;
if nargin < 4 ; params=[]; end;
if Ntr==1; error('cannot compute second spectrum with just one trial'); end;
dt=1/Fs; % sampling interval
T=N*dt; % length of data in seconds
E=0:win:T-win; % fictitious event triggers
datatmp=createdatamatc(data(:,1,1),E,Fs,[0 win]); % segmented data
Ninseg=size(datatmp,1); % number of samples in segments
for nc=1:NC;
    for ntr=1:Ntr;
if use==1;
   params.fpass=[0 params.Fs/2];

for nc=1:NC;
    for ntr=1:Ntr;
        if use==1;
            s=repmat(s,[1 NF]).*cosinefunc;
    %         subplot(221); plot(s(:,1));
    %         subplot(222); plot(s(:,10));
    %         subplot(223); plot(s(:,100));
    %         subplot(224); plot(s(:,120));
    %         pause
%         plot(tau,s)
%         pause

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