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 data=rmlinesc(data,params,p,plt,f0)
% removes significant sine waves from data (continuous data).
% Usage: data=rmlinesc(data,params,p,plt,f0)
%  Inputs:  
% Note that units of Fs, fpass have to be consistent.
%       data        (data in [N,C] i.e. time x channels/trials or a single vector) - required.
%       params      structure containing parameters - params has the
%       following fields: tapers, Fs, fpass, pad
%           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
%	        Fs 	        (sampling frequency) -- optional. Defaults to 1.
%               fpass       (frequency band to be used in the calculation in the form
%                                   [fmin fmax])- optional. 
%                                   Default all frequencies between 0 and Fs/2
%	        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.
%	    p		    (P-value for F-test) - optional. Defaults to 0.05/N
%	    where N is data length. This corresponds to a false detect
%	    probability of approximately 0.05
%       plt         (y/n for plot and no plot respectively)
%       f0          frequencies at which you want to remove the
%                   lines - if unspecified the program uses the f statistic
%                   to determine appropriate lines.
%  Outputs: 
%       data        (data with significant lines removed)
if nargin < 2 || isempty(params); params=[]; end;
clear pad fpass err trialave
if nargin < 3 || isempty(p);p=0.05/N; else; user_specified_pval=1; end;
if nargin < 4 || isempty(plt); plt='n'; end;
if nargin < 5; f0=[]; end;
if isempty(f0) && user_specified_pval==1; p=p/N; end;
%params.tapers=dpsschk(tapers,N,Fs); % calculate the tapers

% [Fval,A,f,sig] = ftestc(data,params,p,'n');
% fmax=FindPeaks(Fval,sig);
% datasine=data;
% for ch=1:C;
%     fsig=f(fmax(ch).loc);
%     Nf=length(fsig);
%     fprintf('The significant lines for channel %d and the amplitudes are \n',ch);
%     for nf=1:Nf;
%         fprintf('%12.8f\n',fsig(nf));
%         fprintf('%12.8f\n',real(A(fmax(ch).loc(nf),ch)));
%         fprintf('%12.8f\n',imag(A(fmax(ch).loc(nf),ch))); 
%         fprintf('\n');
%     end;
%     datasine(:,ch)=exp(i*2*pi*(0:N-1)'*fsig/Fs)*A(fmax(ch).loc,ch)+exp(-i*2*pi*(0:N-1)'*fsig/Fs)*conj(A(fmax(ch).loc,ch));
% end;
% % subplot(211); plot(data); hold on; plot(datasine,'r');
% datan=data-datasine;
% subplot(212); plot(datan);
if nargout==0 || strcmp(plt,'y'); 
   subplot(321); plot(f,10*log10(S1));xlabel('frequency Hz'); ylabel('Spectrum dB'); title('Original spectrum');
   subplot(323); plot(f,Fval); line(get(gca,'xlim'),[sig sig],'Color','r'); xlabel('frequency Hz');ylabel('F-statistic');
   subplot(325);plot(f,10*log10(S1),f,10*log10(S2));xlabel('frequency Hz'); ylabel('Spectrum dB'); title('Original and cleaned spectra');
   subplot(322); plot((1:size(data,1))/params.Fs,data); xlabel('time s');  title('Original data');
   subplot(324); plot((1:size(datan,1))/params.Fs,datan);xlabel('time s'); title('Cleaned data');