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;
/
tomodel
exportfig
.ignore
README.md *
.gitignore *
append_pdfs.m *
copyfig.m *
crop_borders.m *
eps2pdf.m *
export_fig.m *
fix_lines.m *
ghostscript.m *
im2gif.m *
isolate_axes.m *
LICENSE *
pdf2eps.m *
pdftops.m *
print2array.m *
print2eps.m *
read_write_entire_textfile.m *
user_string.m *
using_hg2.m *
                            
%ISOLATE_AXES Isolate the specified axes in a figure on their own
%
% Examples:
%   fh = isolate_axes(ah)
%   fh = isolate_axes(ah, vis)
%
% This function will create a new figure containing the axes/uipanels
% specified, and also their associated legends and colorbars. The objects
% specified must all be in the same figure, but they will generally only be
% a subset of the objects in the figure.
%
% IN:
%    ah - An array of axes and uipanel handles, which must come from the
%         same figure.
%    vis - A boolean indicating whether the new figure should be visible.
%          Default: false.
%
% OUT:
%    fh - The handle of the created figure.

% Copyright (C) Oliver Woodford 2011-2013

% Thank you to Rosella Blatt for reporting a bug to do with axes in GUIs
% 16/3/2012 Moved copyfig to its own function. Thanks to Bob Fratantonio
% for pointing out that the function is also used in export_fig.m.
% 12/12/12 - Add support for isolating uipanels. Thanks to michael for
% suggesting it.
% 08/10/13 - Bug fix to allchildren suggested by Will Grant (many thanks!).
% 05/12/13 - Bug fix to axes having different units. Thanks to Remington
% Reid for reporting the issue.

function fh = isolate_axes(ah, vis)
% Make sure we have an array of handles
if ~all(ishandle(ah))
    error('ah must be an array of handles');
end
% Check that the handles are all for axes or uipanels, and are all in the same figure
fh = ancestor(ah(1), 'figure');
nAx = numel(ah);
for a = 1:nAx
    if ~ismember(get(ah(a), 'Type'), {'axes', 'uipanel'})
        error('All handles must be axes or uipanel handles.');
    end
    if ~isequal(ancestor(ah(a), 'figure'), fh)
        error('Axes must all come from the same figure.');
    end
end
% Tag the objects so we can find them in the copy
old_tag = get(ah, 'Tag');
if nAx == 1
    old_tag = {old_tag};
end
set(ah, 'Tag', 'ObjectToCopy');
% Create a new figure exactly the same as the old one
fh = copyfig(fh); %copyobj(fh, 0);
if nargin < 2 || ~vis
    set(fh, 'Visible', 'off');
end
% Reset the object tags
for a = 1:nAx
    set(ah(a), 'Tag', old_tag{a});
end
% Find the objects to save
ah = findall(fh, 'Tag', 'ObjectToCopy');
if numel(ah) ~= nAx
    close(fh);
    error('Incorrect number of objects found.');
end
% Set the axes tags to what they should be
for a = 1:nAx
    set(ah(a), 'Tag', old_tag{a});
end
% Keep any legends and colorbars which overlap the subplots
lh = findall(fh, 'Type', 'axes', '-and', {'Tag', 'legend', '-or', 'Tag', 'Colorbar'});
nLeg = numel(lh);
if nLeg > 0
    set([ah(:); lh(:)], 'Units', 'normalized');
    ax_pos = get(ah, 'OuterPosition');
    if nAx > 1
        ax_pos = cell2mat(ax_pos(:));
    end
    ax_pos(:,3:4) = ax_pos(:,3:4) + ax_pos(:,1:2);
    leg_pos = get(lh, 'OuterPosition');
    if nLeg > 1;
        leg_pos = cell2mat(leg_pos);
    end
    leg_pos(:,3:4) = leg_pos(:,3:4) + leg_pos(:,1:2);
    ax_pos = shiftdim(ax_pos, -1);
    % Overlap test
    M = bsxfun(@lt, leg_pos(:,1), ax_pos(:,:,3)) & ...
        bsxfun(@lt, leg_pos(:,2), ax_pos(:,:,4)) & ...
        bsxfun(@gt, leg_pos(:,3), ax_pos(:,:,1)) & ...
        bsxfun(@gt, leg_pos(:,4), ax_pos(:,:,2));
    ah = [ah; lh(any(M, 2))];
end
% Get all the objects in the figure
axs = findall(fh);
% Delete everything except for the input objects and associated items
delete(axs(~ismember(axs, [ah; allchildren(ah); allancestors(ah)])));
end

function ah = allchildren(ah)
ah = findall(ah);
if iscell(ah)
    ah = cell2mat(ah);
end
ah = ah(:);
end

function ph = allancestors(ah)
ph = [];
for a = 1:numel(ah)
    h = get(ah(a), 'parent');
    while h ~= 0
        ph = [ph; h];
        h = get(h, 'parent');
    end
end
end

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