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_bar_plot = plotUniquesStatsBars(a_db, unique_test, stat_test, title_str, props)

% plotUniquesStatsBars - Creates a mean-STD bar plot of a column for unique values of another column.
%
% Usage:
% a_bar_plot = plotUniquesStatsBars(a_db, unique_test, stat_test, title_str, props)
%
% Parameters:
%   a_db: A tests_db.
%   unique_test: Column for which to generate bars for each of its unique values.
%   stat_test: Column for which statsMeanSTD will be calculated for each bar.
%   props: A structure with any optional properties.
%	popMean: If specified, plot a dotted line specifying the
%		population mean. If NaN, calculated from a_db.
% 	yLims: two-element vector for specifying y axis limits showing
% 		interesting part of the bar plot.
%	uniqueVals: Use these unique values for unique_test.
% 	(rest passed to plot_bars [and plot_superpose if popMean]).
%		
% Description:
%
%   Returns:
%	a_bar_plot: A plot_abstract object to be plotted.
%
% Example:
% >> plotFigure(plotUniquesStatsBars(triplet_param_success_db, 'F_tau_m', ...
%                                'successDefault', 'across triplets', ...
%                                struct('fixedSize', [4 2], 'yLims', [.7 .9], ...
%                                       'popMean', 0.82, 'quiet', 1)))
%
% See also: tests_db, sortedUniqueValues, statsMeanStd, plot_bars
%
% $Id$
%
% Author: Cengiz Gunay <cgunay@emory.edu>, 2008/04/14

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

if ~ exist('title_str', 'var')
  title_str = '';
end

% keep only columns we care about
a_db = onlyRowsTests(a_db, ':', {unique_test, stat_test});

% use given stats func
if isfield(props, 'statsFunc')
  stats_func = props.statsFunc;
else
  stats_func = 'statsMeanStd';
end

% use given stats row
if isfield(props, 'statsRow')
  stats_row = props.statsRow;
else
  stats_row = 'mean';
end

% find unique values of column
if isfield(props, 'uniqueVals')
  sorted_unique_vals = props.uniqueVals;
else
  sorted_unique_vals = ...
      sortedUniqueValues(get(sortrows(onlyRowsTests(a_db, ':', unique_test), ...
                                      unique_test), 'data'));
end

%  get stats for first unique value
a_stats_db = ...
    find_stats_for_unique(sorted_unique_vals(1));

for unique_num = 2:length(sorted_unique_vals)
  a_stats_db = ...
      compareStats(a_stats_db, ...
                   find_stats_for_unique(sorted_unique_vals(unique_num)));
end

if isfield(props, 'quiet')
  all_title = properTeXLabel(title_str);
else
  all_title = ...
      properTeXLabel([lower(get(a_db, 'id')) title_str ]);
end

if isfield(props, 'yLims')
  props.axisLimits = [NaN NaN props.yLims];
end

a_bar_plot = ...
    plot_bars(a_stats_db, all_title, ...
              mergeStructs(props, struct('pageVariable', unique_test, 'quiet', 1, ...
                                         'tightLimits', 1, 'grid', 1))); %
  
% draw a dotted line showing population mean
if isfield(props, 'popMean')
  pop_mean = props.popMean;
  if isnan(pop_mean)
    pop_stats_db = statsMeanStd(onlyRowsTests(a_db, ':', stat_test));
    pop_mean = ...
        get(onlyRowsTests(pop_stats_db, 'mean', stat_test), 'data');
  end

  a_line_plot = ...
      plot_abstract({[.5 length(sorted_unique_vals) + .5 ], ...
                     [ pop_mean, pop_mean ], ...
                     ':', 'Color', [.3 .3 .3], 'LineWidth', 3}, {}, '', ...
                    {}, 'plot');
  a_bar_plot = ...
      plot_superpose({a_bar_plot, a_line_plot }, {}, ' ', ...
                     mergeStructs(props, struct('tightLimits', 0, 'noLegends', 1)));
end

% this function returns NaN stats if no uniques were found
function a_stats_db = ...
    find_stats_for_unique(unique_val)

a_unique_db = onlyRowsTests(a_db, ...
                           onlyRowsTests(a_db, ':', unique_test) == ...
                           unique_val, ':');
% if empty, create a one-row NaN DB with same columns
if dbsize(a_unique_db, 2) == 0
  a_unique_db = tests_db(repmat(NaN, 1, dbsize(a_db, 2)), ...
                         getColNames(a_db), {}, '', get(a_db, 'props'));

end

a_stats_db = feval(stats_func, a_unique_db);

end % function find_stats_for_unique

end % function plotUniquesStatsBars

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