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 [idx rows_idx] = anyRows(db, rows)

% anyRows - Returns db rows matching any of the given rows.
%
% Usage:
% idx = anyRows(db, rows)
%
% Description:
% The db rows are compared to each row and row indices succeeding any of
% these comparisons are returned.
%
% Parameters:
%	db: A tests_db object.
%	rows: Row array, matrix or database to be compared with db rows.
%		
% Returns:
%	idx: A logical column vector of matching db row indices. 
%	rows_idx: Indices of rows entries corresponding to each db
%		row. Non-matching entries were left as NaN.
%
% Example:
%  >> db(anyRows(db(:, 'trial'), [12; 46; 37]), :)
% returns a db with rows having trial equal to any of the given values.
%
% See also: compareRows, eq, tests_db
%
% $Id$
%
% Author: Cengiz Gunay <cgunay@emory.edu>, 2004/09/17

% 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: optimize by using sort

% Compare two dbs?
if isa(rows, 'tests_db')
  rows = rows.data;
end

% sanity check
if dbsize(db, 2) ~= size(rows, 2)
  error(['Rows contains ' num2str(size(rows, 2)) ' columns, but db has ' ...
         num2str(dbsize(db, 2)) ' columns. They must match for comparison.']);
end

% prepare variables for faster processing
num_rows = size(rows, 1);
num_db_rows = size(db.data, 1);
ones_matx = ones(num_db_rows, 1);
idx = false(num_db_rows, 1);
rows_idx = repmat(NaN, num_db_rows, 1);

db_data = db.data;

% DISABLED: look for optimization.
% unfortunately this is not even faster due to the memory requirement :(
if false && dbsize(db, 2) == 1 && num_db_rows * num_rows < 10000000
  idx = all(abs(( db.data * ones(1, num_rows) - (rows * ones_matx')')) <= ...
            eps(0), 2);
else

  % Calculate multiple rows by tail recursion
  for row_num=1:num_rows
    % Find doesn't work in two dimension comparisons
    % Thus, use algorithm:

    % - duplicate row to a matrix of same size with db
    % - subtract from db
    matching_db_rows = ...
        all(abs(db_data - (ones_matx * rows(row_num, :))) <= eps(0), 2);
    rows_idx(matching_db_rows) = row_num;
    idx = idx | matching_db_rows;
  end

end


end

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