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 = vertcat(db, varargin)

% vertcat - Vertical concatanation [db;with_db;...] operator.
%
% Usage:
% a_db = vertcat(db, with_db, ...)
%
% Description:
%   Concatanates rows of with_db to rows of db. Overrides the built-in
% vertcat function that is called when [db;with_db] is executed. If the 
% first argument is a array of DBs, then this functionality is not needed;
% built-in vertcat is called.
%
%   Parameters:
%	db: A tests_db object.
%	with_db: A tests_db object whose rows are concatanated to db.
%		
%   Returns:
%	a_db: A tests_db that contains rows of db and with_db.
%
% See also: vertcat, tests_db
%
% $Id: vertcat.m 1335 2012-04-19 18:04:32Z cengique $
%
% Author: Cengiz Gunay <cgunay@emory.edu>, 2005/01/25

% 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.

% TODO: change this function to loop over all inputs and preallocate a
% big matrix to speed up inserting rows

% if input is already a row of DBs, allow building a DB matrix
if length(db) > 1
  a_db = builtin('vertcat', db, varargin{:});
else

% Recurse to support variable number of inputs
if length(varargin) > 1
  with_db = vertcat(varargin{1}, varargin{2:end});
elseif length(varargin) == 0
  a_db = db;
  return;
else
  with_db = varargin{1};
end

% return the other one if one of the dbs is empty
if prod(dbsize(db)) == 0
  a_db = with_db;
  return;
elseif prod(dbsize(with_db)) == 0
  a_db = db;
  return;
end

% check for column consistency
[col_names, wcol_names] = checkConsistentCols(db, with_db);

% concat row names
num_db_rows = dbsize(db, 1);
db_rows = get(db, 'row_idx');
wdb_rows = get(with_db, 'row_idx');
db = set(db, 'row_idx', ...
             mergeStructs(db_rows, ...
                          cell2struct(num2cell(cell2mat(struct2cell(wdb_rows)) + ...
                                               num_db_rows), fieldnames(wdb_rows))));

% concatenate and preserve column order of first DB
a_db = set(db, 'data', [ get(db, 'data'); ...
			get(onlyRowsTests(with_db, ':', col_names), 'data') ] );
end

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