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_db, varargout] = std(a_db, sflag, dim)

% std - Returns the std of the data matrix of a_db. Ignores NaN values.
%
% Usage:
% [a_db, n] = std(a_db, sflag, dim)
%
% Description:
%   Does a recursive operation over dimensions in order to remove NaN values.
% This takes considerable amount of time compared with a straightforward std
% operation. 
%
%   Parameters:
%	a_db: A tests_db object.
%	dim: Work down dimension.
%		
%   Returns:
%	a_db: The DB with std values.
%	n: (Optional) Numbers of non-NaN rows included in calculating each column.
%
% See also: std, tests_db
%
% $Id$
%
% Author: Cengiz Gunay <cgunay@emory.edu>, 2004/10/06

% 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('dim', 'var')
  dim = 1; % Go down rows by default
end

if ~ exist('sflag', 'var')
  sflag = 0; % Normalize by N-1 by default
end

% Always do row stds
order = 1:length(dbsize(a_db));
if dim ~= 1
  order(dim) = 1;
  order(1) = dim;
  data = permute(a_db.data, order);
else
  data = a_db.data;
end

% Allocate results array
db_size = size(data);
s = repmat(NaN, [1 db_size(2:end)]);

% Do a loop over EACH other dimension (!)
[s, n] = recstd(data, sflag, length(db_size));

if dim ~= 1
  s = ipermute(s, order);
end

a_db = set(a_db, 'id', [ 'Mean of ' get(a_db, 'id') ]);
a_db = set(a_db, 'data', s);

nout = max(nargout,1) - 1;

if nout > 0
  varargout{1} = n;
end

% Recursive std needed for stripping NaNs in each dimension
% s is the std, and n is the number of non-NaN rows used to obtain it.
function [s, n] = recstd(data, sflag, dim)
  if dim == 1
    sdata = data(~isnan(data(:)) & ~isinf(data(:)));
    n = size(sdata, 1);
    if n == 0
      % If no data exists, give it NaN value instead of an empty
      % matrix.
      s = NaN;
    else
      s = std(sdata, sflag, 1);
    end
  else
    for num=1:size(data, dim)
      % Otherwise recurse
      [dims{1:(dim-1)}] = deal(':');
      dims{dim} = num;
      [s(dims{:}) n(dims{:})]  = recstd(data(dims{:}), sflag, dim - 1);
    end
  end

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