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

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Accession:229279

References:
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;
Gene(s):
Transmitter(s): Gaba; Glutamate;
Simulation Environment: GENESIS;
Model Concept(s): Synaptic Integration;
Implementer(s): Jaeger, Dieter [djaeger at emory.edu];
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 y_line=runline(y,n,dn)
% Running line fit (local linear regression)
%
% Usage: y_line=runline(y,n,dn);
%
% Inputs: 
% y: input 1-d time series (real)
% n: length of running window in samples
% dn: stepsize of window in samples
% 
% Outputs:
% y_line: local line fit to data
y=y(:);
nt=length(y);
y_line=zeros(nt,1);
norm=y_line;
nwin=ceil((nt-n)/dn);
yfit=zeros(nwin,n);
xwt=((1:n)-n/2)/(n/2);
wt=(1-abs(xwt).^3).^3;
for j=1:nwin, 
	tseg=y(dn*(j-1)+1:dn*(j-1)+n);
	y1=mean(tseg); 
	y2=mean((1:n)'.*tseg)*2/(n+1);
	a=(y2-y1)*6/(n-1); b=y1-a*(n+1)/2;
	yfit(j,:)=(1:n)*a+b;
	y_line((j-1)*dn+(1:n))=y_line((j-1)*dn+(1:n))+(yfit(j,:).*wt)';
	norm((j-1)*dn+(1:n))=norm((j-1)*dn+(1:n))+wt';
end
mask=find(norm>0); y_line(mask)=y_line(mask)./norm(mask);
indx=(nwin-1)*dn+n-1;
npts=length(y)-indx+1;
y_line(indx:end)=(n+1:n+npts)'*a+b;

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