Model of memory linking through memory allocation (Kastellakis et al. 2016)

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Accession:206249
Here, we present a simplified, biophysically inspired network model that incorporates multiple plasticity processes and explains linking of information at three different levels: (a) learning of a single associative memory (b) rescuing of a weak memory when paired with a strong one and (c) linking of multiple memories across time. By dissecting synaptic from intrinsic plasticity and neuron-wide from dendritically restricted protein capture, the model reveals a simple, unifying principle: Linked memories share synaptic clusters within the dendrites of overlapping populations of neurons
Reference:
1 . Kastellakis G, Silva AJ, Poirazi P (2016) Linking Memories across Time via Neuronal and Dendritic Overlaps in Model Neurons with Active Dendrites. Cell Rep 17:1491-1504 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism:
Cell Type(s): Abstract integrate-and-fire leaky neuron with dendritic subunits;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: C or C++ program; C or C++ program (web link to model);
Model Concept(s): Active Dendrites;
Implementer(s): Kastellakis, George [gkastel at gmail.com];
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stdmodel
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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