Hotspots of dendritic spine turnover facilitates new spines and NN sparsity (Frank et al 2018)

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Accession:227087
Model for the following publication: Adam C. Frank, Shan Huang, Miou Zhou, Amos Gdalyahu, George Kastellakis, Panayiota Poirazi, Tawnie K. Silva, Ximiao Wen, Joshua T. Trachtenberg, and Alcino J. Silva Hotspots of Dendritic Spine Turnover Facilitate Learning-related Clustered Spine Addition and Network Sparsity
Reference:
1 . Frank AC, Huang S, Zhou M, Gdalyahu A, Kastellakis G, Silva TK, Lu E, Wen X, Poirazi P, Trachtenberg JT, Silva AJ (2018) Hotspots of dendritic spine turnover facilitate clustered spine addition and learning and memory. Nat Commun 9:422 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell; Connectionist Network;
Brain Region(s)/Organism:
Cell Type(s): Abstract integrate-and-fire leaky neuron with dendritic subunits;
Channel(s):
Gap Junctions:
Receptor(s): NMDA;
Gene(s):
Transmitter(s):
Simulation Environment: C or C++ program; MATLAB;
Model Concept(s): Active Dendrites; Synaptic Plasticity;
Implementer(s): Kastellakis, George [gkastel at gmail.com];
Search NeuronDB for information about:  NMDA;
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.exrc *
an_m_to.m
an_to.m
barwitherr.m *
btagstats.m *
CImg.h *
constructs. *
constructs.cpp *
constructs.h
csvgraph.m
defaults.m
dir2.m *
gconstructs.cpp *
getspikedata.m *
getsynstate.m *
getsynstate2.m *
graphs.m *
hist_percents.m *
hist_with_errs.m *
interact.m *
kurtos.m *
lamodel
lamodel.cpp
LICENSE *
make_graphs.m *
Makefile *
matlab.mat *
mtrand.cpp *
mtrand.h *
multistats.m *
nextplot.m *
pairstrong.m *
repeated.m *
rotateXLabels.m *
run_to.sh
S2sparse.m *
savefig.m *
scratch.m *
sensitivity.m *
stats.m *
stats.py *
stderr.m *
strong2.m *
strongstrong.m *
submit_lamodel.sh *
three.m *
to.cpp
trevrolls.m *
vis.py *
weastrong.m *
wxglmodel *
wxglmodel.cpp *
wxglmodel.h *
wxmodel.cpp *
wxmodel.h *
                            
%**************************************************************************
%
%   This is a simple extension of the bar plot to include error bars.  It
%   is called in exactly the same way as bar but with an extra input
%   parameter "errors" passed first.
%
%   Parameters:
%   errors - the errors to be plotted (extra dimension used if assymetric)
%   varargin - parameters as passed to conventional bar plot
%   See bar and errorbar documentation for more details.
%
%   Output:
%   [hBar hErrorbar] = barwitherr(..) returns a vector of handles to the 
%                      barseries (hBar) and error bar (hErrorbar) objects
%
%   Symmetric Example:
%   y = randn(3,4);         % random y values (3 groups of 4 parameters) 
%   errY = 0.1.*y;          % 10% error
%   h = barwitherr(errY, y);% Plot with errorbars
%
%   set(gca,'XTickLabel',{'Group A','Group B','Group C'})
%   legend('Parameter 1','Parameter 2','Parameter 3','Parameter 4')
%   ylabel('Y Value')
%   set(h(1),'FaceColor','k');
%
%
%   Asymmetric Example:
%   y = randn(3,4);         % random y values (3 groups of 4 parameters)
%   errY = zeros(3,4,2);
%   errY(:,:,1) = 0.1.*y;   % 10% lower error
%   errY(:,:,2) = 0.2.*y;   % 20% upper error
%   barwitherr(errY, y);    % Plot with errorbars
%
%   set(gca,'XTickLabel',{'Group A','Group B','Group C'})
%   legend('Parameter 1','Parameter 2','Parameter 3','Parameter 4')
%   ylabel('Y Value')
%
%
%   Notes:
%   Ideally used for group plots with non-overlapping bars because it
%   will always plot in bar centre (so can look odd for over-lapping bars) 
%   and for stacked plots the errorbars will be at the original y value is 
%   not the stacked value so again odd appearance as is.
%
%   The data may not be in ascending order.  Only an issue if x-values are 
%   passed to the fn in which case their order must be determined to 
%   correctly position the errorbars.
%
%
%   24/02/2011  Martina F. Callaghan    Created
%   12/08/2011  Martina F. Callaghan    Updated for random x-values   
%   24/10/2011  Martina F. Callaghan    Updated for asymmetric errors
%   15/11/2011  Martina F. Callaghan    Fixed bug for assymetric errors &
%                                       vector plots
%   14/06/2013  Martina F. Callaghan    Returning handle as recommended by
%                                       Eric (see submission comments)
%   08/07/2013  Martina F. Callaghan    Only return handle if requested.
%   18/07/2013  Martina F. Callaghan    Bug fix for single group data that 
%                                       allows assymetric errors.
%                                       Also removed dot from display as
%                                       per Charles Colin comment. The 
%                                       handle can be returned to control
%                                       appearance.
%   27/08/2013  Martina F. Callaghan    Ensuring errors are always stored
%                                       as lowerErrors and upperErrors even
%                                       if symmetric.
%
%**************************************************************************

function varargout = barwitherr(errors,varargin)

% Check how the function has been called based on requirements for "bar"
if nargin < 3
    % This is the same as calling bar(y)
    values = varargin{1};
    xOrder = 1:size(values,1);
else
    % This means extra parameters have been specified
    if isscalar(varargin{2}) || ischar(varargin{2})
        % It is a width / property so the y values are still varargin{1}
        values = varargin{1};
        xOrder = 1:size(values,1);
    else
        % x-values have been specified so the y values are varargin{2}
        % If x-values have been specified, they could be in a random order,
        % get their indices in ascending order for use with the bar
        % locations which will be in ascending order:
        values = varargin{2};
        [tmp xOrder] = sort(varargin{1});
    end
end

% If an extra dimension is supplied for the errors then they are
% assymetric split out into upper and lower:
if ndims(errors) == ndims(values)+1
    lowerErrors = errors(:,:,1);
    upperErrors = errors(:,:,2);
elseif isvector(values)~=isvector(errors)
    lowerErrors = errors(:,1);
    upperErrors = errors(:,2);
else
    lowerErrors = errors;
    upperErrors = errors;
end

    
% Check that the size of "errors" corresponsds to the size of the y-values.
% Arbitrarily using lower errors as indicative.
if any(size(values) ~= size(lowerErrors))
    error('The values and errors have to be the same length')
end

[nRows nCols] = size(values);
handles.bar = bar(varargin{:}, 'EdgeColor', 'none'); % standard implementation of bar fn
hold on
hBar = handles.bar;

if nRows > 1
    hErrorbar = zeros(1,nCols);
    for col = 1:nCols
        % Extract the x location data needed for the errorbar plots:
        x = get(get(handles.bar(col),'children'),'xdata');
        % Use the mean x values to call the standard errorbar fn; the
        % errorbars will now be centred on each bar; these are in ascending
        % order so use xOrder to ensure y values and errors are too:
        hErrorbar(col) = errorbar(mean(x,1), values(xOrder,col), lowerErrors(xOrder,col), upperErrors(xOrder, col), '.k');
        set(hErrorbar(col), 'marker', 'none')
    end
else
    x = get(get(handles.bar,'children'),'xdata');
    hErrorbar = errorbar(mean(x,1), values, lowerErrors, upperErrors, '.k');
    set(hErrorbar, 'marker', 'none')
end

hold off

switch nargout
    case 1
        varargout{1} = hBar;
    case 2
        varargout{1} = hBar;
        varargout{2} = hErrorbar;
end

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