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_p = plotScatter3D(a_db, test1, test2, test3, title_str, short_title, props)

% plotScatter3D - Create a 3D scatter plot of the given three tests.
%
% Usage:
% a_p = plotScatter3D(a_db, test1, test2, test3, title_str, short_title, props)
%
% Description:
%
%   Parameters:
%	a_db: A params_tests_db object.
%	test1, test2, test3: X, Y, & Z variables.
%	title_str: (Optional) A string to be concatanated to the title.
%	short_title: (Optional) Few words that may appear in legends of multiplot.
%	props: A structure with any optional properties.
%	  LineStyle: Plot line style to use. (default: 'x')
%	  Regress: Calculate and plot a linear regression.
%	  quiet: If 1, don't include database name on title.
%		
%   Returns:
%	a_p: A plot_abstract.
%
% See also: 
%
% $Id$
%
% Author: Cengiz Gunay <cgunay@emory.edu>, 2007/11/30

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

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

col1 = tests2cols(a_db, test1);
col2 = tests2cols(a_db, test2);
col3 = tests2cols(a_db, test3);

col1_db = onlyRowsTests(a_db, ':', col1);
col2_db = onlyRowsTests(a_db, ':', col2);
col3_db = onlyRowsTests(a_db, ':', col3);

% skip NaN value rows
non_nans_rows = ~(isnan(col1_db) | isnan(col2_db));
col1_db = onlyRowsTests(col1_db, non_nans_rows, ':');
col2_db = onlyRowsTests(col2_db, non_nans_rows, ':');
col3_db = onlyRowsTests(col3_db, non_nans_rows, ':');

test_names = fieldnames(get(a_db, 'col_idx'));

if ~ exist('short_title', 'var') || isempty(short_title)
  short_title = [strrep(test_names{col1}, '_', ' ') ', ' ...
		 strrep(test_names{col2}, '_', ' ')  ', ' ...
                 strrep(test_names{col2}, '_', ' ')];
end

if ~ isfield(props, 'quiet')
  all_title = [ strrep(get(a_db, 'id'), '_', '\_') title_str ];
else
  all_title = title_str;
end


if isfield(props, 'LineStyle')
  line_style = {props.LineStyle};
else
  line_style = {};
  props.LineStyleOrder = {'x', '+', 'd', 'o', '*', 's'};
end

if isfield(props, 'Regress')
  [b,bint,r,rint,stats] = ...
      regress(get(col3_db, 'data'), [ones(dbsize(col1_db, 1), 1), ...
                      get(col1_db, 'data') get(col2_db, 'data')]);
  if ~isempty(all_title)
    all_title = [ all_title, '; '];
  end
  all_title = [ all_title, 'regress p=' sprintf('%.4f', stats(3)) ];
end

col_labels = strrep({test_names{[col1 col2 col3]}}, '_', ' ');
a_p = plot_abstract({get(col1_db, 'data'), get(col2_db, 'data'), get(col3_db, 'data'), line_style{:}}, ...
		    { col_labels{:} }, ...
		    all_title, { short_title }, 'plot3', ...
		    props); 

if isfield(props, 'Regress')
  x_lims = [min(get(col1_db, 'data')) max(get(col1_db, 'data'))];
  y_lims = [min(get(col2_db, 'data')) max(get(col2_db, 'data'))];
  % TODO: to be completed
  a_p = plot_superpose([a_p, plot_abstract({x_lims, x_lims * b(2) + b(1), 'm-'}, ...
					   { }, '', { '' }, 'patch', props)], {}, '');
end

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