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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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 *
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wxglmodel *
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function la_run {
	qsub -v "LAPARAMS=$LAPARAMS" submit_lamodel.sh
	#./lamodel $LAPARAMS  
	#sleep 1
}


function run_weaks {
		LAPARAMS="$WEAKS -P 3  -T $ws -S 191$run -s three${SUFF}_${ws}_${run}  "
		la_run 

		LAPARAMS="$WEAKS -P 3  -n -T $ws -S 191$run -s three${SUFF}N_${ws}_${run}  "
		la_run 

		LAPARAMS="$WEAKS -P 3  -T $ws -S 191$run -s three${SUFF}G_${ws}_${run}  -G"
		la_run 

		LAPARAMS="$WEAKS -P 3  -T $ws -S 191$run -s three${SUFF}L_${ws}_${run}  -L"
		la_run 

}

for ws in 60  120 180 300  1440;  do
	for run in  {0..10}; do

	WEAKS="-w 1 -w 2 -w 3"
	SUFF="www"
	run_weaks 

	WEAKS="-w 1  -w 3"
	SUFF="wsw"
	run_weaks 

	WEAKS="-w 2 "
	SUFF="sws"
	run_weaks 

		#LAPARAMS=" -P 2  -T $ws -S 1980$run -s "three2N_${ws}_${run}"   -n"
	#	qsub -v "LAPARAMS=${LAPARAMS}" submit_lamodel.sh
	#	sleep 1
	#	LAPARAMS=" -P 2  -T $ws -S 1980$run -s "three2NL_${ws}_${run}"  -n -L "
	#	qsub -v "LAPARAMS=${LAPARAMS}" submit_lamodel.sh
	#	sleep 1

		#LAPARAMS=" -P 2  -T $ws -S 1980$run -s "three2Alt_${ws}_${run}" -C "
		#qsub -v "LAPARAMS=${LAPARAMS}" submit_lamodel.sh
		#LAPARAMS="-P 2  -T $ws -S 1980$run -s "three2LAlt_${ws}_${run}"  -C -L"
		#qsub -v "LAPARAMS=${LAPARAMS}" submit_lamodel.sh
		#LAPARAMS=" -P 2  -T $ws -S 1980$run -s "three2NAlt_${ws}_${run}"  -C -n"
		#qsub -v "LAPARAMS=${LAPARAMS}" submit_lamodel.sh
		#LAPARAMS=" -P 2  -T $ws -S 1980$run -s "three2NLAlt_${ws}_${run}" -C -n -L "
		#qsub -v "LAPARAMS=${LAPARAMS}" submit_lamodel.sh
		#sleep 1
	done
done




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