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_stacked_plot = ...
      plotUniquesStatsStacked3D(a_db, unique_test1, unique_test2, ...
                                unique_test3, stat_test, title_str, props)

% plotUniquesStatsStacked3D - Stack of 2D image plots of a column mean at unique values of three other columns.
%
% Usage:
% a_stacked_plot = plotUniquesStatsStacked3D(a_db, unique_test1, unique_test2, 
% 					unique_test3, stat_test, title_str, props)
%
% Parameters:
%   a_db: A tests_db.
%   unique_test1, unique_test2: Columns whose unique values make up the X
%   		& Y of the 2D image plot.
%   unique_test3: Column whose unique values make up stacked dimension.
%   stat_test: Column for which statsMeanSTD will be calculated for each
%   		unique value.
%   props: A structure with any optional properties.
% 	(rest passed to plotUniquesStats2D and plot_stack).
% 
% Description:
%
% Returns:
%	a_stacked_plot: A plot_abstract object to be plotted.
%
% See also: tests_db, sortedUniqueValues, statsMeanStd, plot_abstract, plotImage
%
% $Id$
%
% Author: Cengiz Gunay <cgunay@emory.edu>, 2008/04/15

  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_test1, unique_test2, unique_test3, ...
                    stat_test});

% find unique values of all columns
sorted_unique_vals1 = ...
    sortedUniqueValues(get(sortrows(onlyRowsTests(a_db, ':', unique_test1), ...
                                    unique_test1), 'data'));
sorted_unique_vals2 = ...
    sortedUniqueValues(get(sortrows(onlyRowsTests(a_db, ':', unique_test2), ...
                                    unique_test2), 'data'));
sorted_unique_vals3 = ...
    sortedUniqueValues(get(sortrows(onlyRowsTests(a_db, ':', unique_test3), ...
                                    unique_test3), 'data'));

num_stack_uniques = length(sorted_unique_vals3);

stack_var_name = getColNames(onlyRowsTests(a_db, ':', unique_test3));
stack_var_name = properTeXLabel(stack_var_name{1});

% 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

default_image_props = props;
default_image_props.uniqueVals1 = sorted_unique_vals1;
default_image_props.uniqueVals2 = sorted_unique_vals2;

% determine population mean and deviation to be used for all 2D plots
% consistently
if isfield(props, 'popMean')
  if isnan(props.popMean)
    pop_stats_db = feval(stats_func, onlyRowsTests(a_db, ':', stat_test));
    if strcmp(stats_func, 'statsMeanStd')
      default_image_props.popMean = ...
          get(onlyRowsTests(pop_stats_db, stats_row, stat_test), 'data');
      default_image_props.popDev = ...
          2*get(onlyRowsTests(pop_stats_db, 'STD', stat_test), 'data');
    elseif strcmp(stats_func, 'statsBounds')
      max_val = ...
          get(onlyRowsTests(pop_stats_db, 'max', stat_test), 'data');
      mean_val = ...
          get(onlyRowsTests(pop_stats_db, 'mean', stat_test), 'data');
      default_image_props.popMean = ...
          mean_val + (max_val - mean_val) / 2;
      default_image_props.popDev = ...
        (max_val - mean_val) / 2;
    end
      
  end
end

if isfield(props, 'quiet')
  default_image_props.quiet = props.quiet;
end

s_plots = {};
for stack_val_num = 1:num_stack_uniques
  stack_idx = ...
      onlyRowsTests(a_db, ':', unique_test3) == ...
      sorted_unique_vals3(stack_val_num);
  
  image_props = default_image_props;
  if isfield(props, 'colorbarPos') && strcmp(props.colorbarPos, 'right') ...
      && stack_val_num == num_stack_uniques
    image_props.colorbar = 1;
  end
  
  an_image_plot = ...
      plotUniquesStats2D(onlyRowsTests(a_db, stack_idx, ':'), unique_test1, ...
                         unique_test2, stat_test, '', ...
                         image_props);
  axis_labels = get(an_image_plot, 'axis_labels');
  s_plots = ...
      [ s_plots, ...
        { set(an_image_plot, 'axis_labels', ...
                    { [ axis_labels{1} ' (' stack_var_name '=' ...
                      num2str(sorted_unique_vals3(stack_val_num)) ')' ], ...
                      axis_labels{2} }) } ];
end

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

% adjust relative sizes to give slack to last plot
if isfield(props, 'colorbarPos') && strcmp(props.colorbarPos, 'right')
  relative_sizes = ones(num_stack_uniques, 1);
  relative_sizes(end) = 1.3;
  props.relativeSizes = relative_sizes;  
end

% TODO: make orientation optional
a_stacked_plot = ...
    plot_stack(s_plots, [Inf Inf Inf Inf], 'x', '', ... % 
               mergeStructs(props, struct('yLabelsPos', 'left', 'yTicksPos', ...
                                          'left')))

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