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 obj = addColumns(obj, test_names, test_columns)

% addColumns - Inserts new columns to tests_db.
%
% Usage 1:
% obj = addColumns(obj, test_names, test_columns)
%
% Usage 2:
% obj = addColumns(obj, b_obj)
%
% Parameters:
%   obj, b_obj: A tests_db object.
%   test_names: A single string or a cell array of test names to be added.
%   test_columns: Data matrix of columns to be added.
%		
% Returns:
%   obj: The tests_db object that includes the new columns.
%
% Description:
%   Adds new test columns to the database and returns the new DB.
% Usage 2 concatanates two DBs columnwise. This operation is 
% expensive in the sense that the whole database matrix needs to be 
% enlarged just to add a single new column. The method of allocating
% a matrix, filling it up, and then providing it to the tests_db 
% constructor is the preferred method of creating tests_db objects. 
% This method may be used for measures obtained by operating on raw measures.
%
% See also: allocateRows, tests_db
%
% $Id$
%
% Author: Cengiz Gunay <cgunay@emory.edu>, 2005/09/30

% 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 isa(test_names, 'tests_db')
  to_db = test_names;
  if dbsize(to_db, 1) == 0
    warning('tests_db/addColumns: Ignoring empty db');
    return;
  end
  test_names = fieldnames(get(to_db, 'col_idx'));
  test_columns = get(to_db, 'data');
elseif ischar(test_names)
  % if it's a string, just encapsulate in cell array
  test_names = { test_names };
end

if (dbsize(obj, 1) > 0 && size(test_columns, 1) ~= dbsize(obj, 1))
  error(['Number of rows in column (' num2str(size(test_columns, 1)) ') ', ...
	 'does not match rows in DB (' num2str(dbsize(obj, 1)) ').']);
end

if length(test_names) ~= size(test_columns, 2)
  error(['Number of test names (' num2str(length(test_names)) ') ', ...
	 'does not match columns in matrix (' num2str(size(test_columns, 2)) ').']);
end

existing_cols = ismember(test_names, fieldnames(get(obj, 'col_idx')));
if any(existing_cols)
  error('tests_db:col_exists', ...
	['Column(s) ' test_names{existing_cols} ' already exist in DB.']);
end

% Add the column
new_col_id = dbsize(obj, 2) + 1;
obj.data = cat(2, obj.data, test_columns);

% Update the meta-data
new_col_idx = get(obj, 'col_idx');
if isempty(new_col_idx)
  new_col_idx = struct;
end
for test_num = 1:length(test_names)
  new_col_idx.(test_names{test_num}) = new_col_id + test_num - 1;
end

obj = set(obj, 'col_idx', new_col_idx);

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