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;
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codes
pandora-matlab-1.4compat2
classes
@tests_db
private
.cvsignore *
abs.m
addColumns.m
addLastRow.m
addRow.m
allocateRows.m
anyRows.m
approxMappingLIBSVM.m
approxMappingNNet.m
approxMappingSVM.m
assignRowsTests.m
checkConsistentCols.m
compareRows.m
corrcoef.m
cov.m
crossProd.m
dbsize.m
delColumns.m
diff.m
display.m
displayRows.m
displayRowsCSV.m
displayRowsTeX.m
end.m
enumerateColumns.m
eq.m
factoran.m
fillMissingColumns.m
ge.m
get.m *
getColNames.m
groupBy.m
gt.m
histogram.m
invarValues.m
isinf.m
isnan.m
isnanrows.m
joinRows.m
kmeansCluster.m
le.m
lt.m
matchingRow.m
max.m
mean.m
meanDuplicateRows.m
min.m
minus.m
mtimes.m
ne.m
noNaNRows.m
onlyRowsTests.m
physiol_bundle.m
plot.m
plot_abstract.m
plot_bars.m
plotBox.m
plotCircular.m
plotCovar.m
plotImage.m
plotrow.m
plotrows.m
plotScatter.m
plotScatter3D.m
plotTestsHistsMatrix.m
plotUITable.m
plotUniquesStats2D.m
plotUniquesStatsBars.m
plotUniquesStatsStacked3D.m
plotXRows.m
plotYTests.m
plus.m
princomp.m
processDimNonNaNInf.m
rankMatching.m
rdivide.m
renameColumns.m
rop.m
rows2Struct.m
set.m *
setProp.m *
setRows.m
shufflerows.m
sortrows.m
sqrt.m
statsAll.m
statsBounds.m
statsMeanSE.m
statsMeanStd.m
std.m
subsasgn.m
subsref.m
sum.m
swapRowsPages.m
tests_db.m
tests2cols.m
tests2idx.m
tests2log.m
testsHists.m
times.m
transpose.m
uminus.m
unique.m
uop.m
vertcat.m
                            
function a_coefs_db = corrcoef(db, props)

% corrcoef - Calculates a correlation coefficient matrix by comparing cols.
%
% Usage:
% a_coefs_db = corrcoef(db, cols, props)
%
% Parameters:
%	db: A tests_db object.
%	cols: Columns to be compared.
%	props: A structure with any optional properties.
%	  skipCoefs: If 1, coefficients of less confidence than %95 
%			will be skipped. (default=1)
%	  alpha: Skip coefs with p values lower than this (default=0.05).
%		
% Returns:
%	a_coefs_db: A tests_3D_db of the coefficients.
%
% Description:
%
% See also: tests_db, corrcoefs_db
%
% $Id$
%
% Author: Cengiz Gunay <cgunay@emory.edu>, 2008/04/25

% Copyright (c) 2007 Cengiz Gunay <cengique@users.sf.net>.
% This work is licensed under the Academic Free License ("AFL")
% v. 3.0. To view a copy of this license, please look at the COPYING
% file distributed with this software or visit
% http://opensource.org/licenses/afl-3.0.php.

if ~ exist('props', 'var')
  props = struct([]);
end

if isfield(props, 'skipCoefs')
  skipCoefs = props.skipCoefs;
else
  skipCoefs = 1;
end

% Obsolete, always need to remove NaNs
if isfield(props, 'excludeNaNs')
  excludeNaNs = props.excludeNaNs;
else
  excludeNaNs = 1;
end

if isfield(props, 'alpha')
  alpha = props.alpha;
else
  alpha = 0.05; % for 95%
end

% ignore NaNs
[coef_data, p, rlo, rup] = corrcoef(get(db, 'data'), 'rows', 'complete');

if skipCoefs
  insignificant = p > alpha;
  coef_data(insignificant) = NaN;
  rlo(insignificant) = NaN;
  rup(insignificant) = NaN;
end

% Create the coefficient database
col_names = getColNames(db);

% Check if any coefs left
if all(all(isnan(coef_data)))
  warning('tests_db:corrCoef:no_coefs', 'No coefficients found.');
end

a_coefs_db = ...
    tests_3D_db(cat(3, coef_data, rlo, rup), ...
                col_names, col_names, {'corr_coefs', 'rlo', 'rup'}, ...
                [ 'Correlations in ' ...
                  properTeXLabel(get(db, 'id')) ], props);


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