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
distributionPlot
exportfig
figs
mtrand
README
allgraphs.m
allrun.m
an_brtest.m
an_stats.m
anmulti.py
ansims.py
barwitherr.m
btagstats.m
CImg.h
constructs.cpp
constructs.h
defaults.m
dir2.m
getspikedata.m
getsynstate.m
getsynstate2.m
graphs.m
hist_percents.m
hist_with_errs.m
interact.m
intexp_constructs.cpp
job_sims.sh
kurtos.m
lamodel.cpp
LICENSE *
make_graphs.m
Makefile
matlab.mat
mtest.py
mtrand.cpp
mtrand.h
multi.py
multistats.m
nextplot.m
pairstrong.m
repeated.m
rotateXLabels.m
run_1.sh
run_2strong.sh
run_2weak.sh
run_3.sh
run_all.sh
run_brov.sh
run_brtest.sh
run_btag.sh
run_dir.sh
run_ep.sh
run_gp.sh
run_gp2.sh
run_mult.sh
run_Nsparse.sh
run_pairstrong.sh
run_rep.sh
run_sims.sh
run_sparse.sh
run_sparseS2.sh
runloc.sh
runmany.sh
S2sparse.m
savefig.m
scratch.m
sensitivity.m
stats.m
stats.py
stderr.m
strong2.m
strongstrong.m
submit_lamodel.sh
three.m
trevrolls.m
vis.py
weastrong.m
wxglmodel
wxglmodel.cpp
wxglmodel.h
wxmodel.cpp
wxmodel.h
                            
This is the model used in 

Kastellakis G, Silva AJ, Poirazi P., (2016) Linking memories across
time via neuronal and dendritic overlaps in model neurons with active
dendrites. Cell Reports

Instructions:

To make the main model binary:

	% make lamodel

To run the simulations used in the paper

	% bash  run_all.sh


The data analysis requires Matlab version R2014a  or newer
To generate all the graphs, run the matlab script make_graphs.m
The graphs will be placed in the ./figs subdirectory

The main is licensed under the  GPLv3 license (see LICENSE).

The matlab code makes use of the open source exportfig matlab package (see exportfig/LICENSE for details ).

This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.

This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU General Public License for more details.

You should have received a copy of the GNU General Public License
along with this program.  If not, see <http://www.gnu.org/licenses/>.

20161007 Changes to allgraphs.m, make_graphs.m, run_2strong.sh, and
run_btag.sh supplied by George Kastellakis.

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