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 *
                            
%CROP_BORDERS Crop the borders of an image or stack of images
%
%   [B, v] = crop_borders(A, bcol, [padding])
%
%IN:
%   A - HxWxCxN stack of images.
%   bcol - Cx1 background colour vector.
%   padding - scalar indicating how many pixels padding to have. Default: 0.
%
%OUT:
%   B - JxKxCxN cropped stack of images.
%   v - 1x4 vector of start and end indices for first two dimensions, s.t.
%       B = A(v(1):v(2),v(3):v(4),:,:).

function [A, v] = crop_borders(A, bcol, padding)
if nargin < 3
    padding = 0;
end
[h, w, c, n] = size(A);
if isscalar(bcol)
    bcol = bcol(ones(c, 1));
end
bail = false;
for l = 1:w
    for a = 1:c
        if ~all(col(A(:,l,a,:)) == bcol(a))
            bail = true;
            break;
        end
    end
    if bail
        break;
    end
end
bcol = A(ceil(end/2),w,:,1);
bail = false;
for r = w:-1:l
    for a = 1:c
        if ~all(col(A(:,r,a,:)) == bcol(a))
            bail = true;
            break;
        end
    end
    if bail
        break;
    end
end
bcol = A(1,ceil(end/2),:,1);
bail = false;
for t = 1:h
    for a = 1:c
        if ~all(col(A(t,:,a,:)) == bcol(a))
            bail = true;
            break;
        end
    end
    if bail
        break;
    end
end
bcol = A(h,ceil(end/2),:,1);
bail = false;
for b = h:-1:t
    for a = 1:c
        if ~all(col(A(b,:,a,:)) == bcol(a))
            bail = true;
            break;
        end
    end
    if bail
        break;
    end
end
% Crop the background, leaving one boundary pixel to avoid bleeding on resize
v = [max(t-padding, 1) min(b+padding, h) max(l-padding, 1) min(r+padding, w)];
A = A(v(1):v(2),v(3):v(4),:,:);
end

function A = col(A)
A = A(:);
end

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