Robust transmission in the inhibitory Purkinje Cell to Cerebellar Nuclei pathway (Abbasi et al 2017)

 Download zip file 
Help downloading and running models
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;
/
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_histogram_db = histogram(db, col, num_bins, props)

% histogram - Returns histogram of chosen database column.
%
% Usage:
% a_histogram_db = histogram(db, col, num_bins, props)
%
% Description:
%   Generates a histogram_db object with rows corresponding to histogram
% entries. If an array of DBs is given, finds and uses common histogram bin centers.
%
%   Parameters:
%	db: A tests_db object.
%	col: Column to find the histogram.
%	num_bins: Number of histogram bins (Optional, default=100), or
%		  vector of histogram bin centers.
%	props: A structure with any optional properties.
%	  normalized: If 1, normalize histogram counts.
%
%   Returns:
%	a_histogram_db: A histogram_db object containing the histogram.
%
% Example:
% >> a_hist_db = histogram(my_db, 'spike_width');
% >> plot(a_hist_db);
%
% See also: histogram_db, tests_db, hist
%
% $Id$
%
% 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.

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

if ~ exist('num_bins', 'var') || isempty(num_bins)
  num_bins = 100;
end

num_dbs = length(db);
if num_dbs > 1
  % If given array of DBs, find maximal bounds to create bins accordingly
  min_val = +Inf;
  max_val = -Inf;
  for db_num=1:num_dbs
    col_db = onlyRowsTests(db(db_num), ':', col);
    col_db = onlyRowsTests(col_db, ~ isnan(col_db.data) & ~ isinf(col_db.data), 1);
    bounds_data = get(statsBounds(col_db), 'data');
    min_val = min(min_val, bounds_data(2));
    max_val = max(max_val, bounds_data(3));
  end

  % If a scalar, then apply limits and get bins
  if length(num_bins) == 1
    num_bins = min_val + (1:num_bins - 1) .* (max_val - min_val) ./ (num_bins - 1);
  end

  % It's not A histogram db anymore
  [a_histogram_db(1:num_dbs)] = deal(histogram_db);
  for db_num=1:num_dbs
    % recurse
    a_histogram_db(db_num) = histogram(db(db_num), col, num_bins, props);
  end  

else

col_db = onlyRowsTests(db, ':', col);

% Remove NaN or Inf values 
col_db = onlyRowsTests(col_db, ~ isnan(col_db.data) & ~ isinf(col_db.data), 1);
%col_db = col_db( ~ isnan(col_db(:, 1)), 1);
% I don't know why the above doesn't work!? 
% [ because matlab doesn't call member funcs from here]

% If any rows left
if dbsize(col_db, 1) > 0
  [hist_results bins] = hist(col_db.data, num_bins);
else
  
  if length(num_bins) > 1
    % if bin edges were given
    bins = num_bins;
  else
    % if number of bin was given
    bins = zeros(1, num_bins);
  end
  hist_results = zeros(1, length(bins));
end

if isfield(props, 'normalized') && props.normalized == 1
  hist_results = hist_results ./ max(hist_results);
end

col_name_cell = fieldnames(col_db.col_idx);
col_name = col_name_cell{1};

a_histogram_db = histogram_db(col_name, bins', hist_results', ...
			      [ col_name ' of ' db.id ], props);
end

Loading data, please wait...