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

 Download zip file 
Help downloading and running models
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];
/
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 *
                            
function la_run {
	#qsub -v "LAPARAMS=$LAPARAMS" submit_lamodel.sh
	#sleep 1
	./lamodel $LAPARAMS  
	#sleep 1
}




COND=weakstrong

for ws in 60 120 180 300 1440; do
	for run in {0..9}; do
	

		LAPARAMS="-P 2  -w 1 -n  -T $ws -S 1980$run -s ${COND}N_0_${ws}_${run}  "
		la_run

		LAPARAMS="-P 2 -w 2   -n  -T $ws -S 1980$run -s ${COND}N_${ws}_0_${run}  "
		la_run

		LAPARAMS="-P 2 -w 1  -n  -T $ws -S 1980$run -s ${COND}NL_0_${ws}_${run}  -L  "
		la_run

		LAPARAMS="-P 2 -w 2  -n   -T $ws -S 1980$run -s ${COND}NL_${ws}_0_${run}  -L   "
		la_run

		LAPARAMS="-P 2 -w 1  -n  -T $ws -S 1980$run -s ${COND}NG_0_${ws}_${run}  -G  "
		la_run

		LAPARAMS="-P 2 -w 2  -n   -T $ws -S 1980$run -s ${COND}NG_${ws}_0_${run}  -G   "
		la_run




		LAPARAMS="-P 2  -w 1   -T $ws -S 1980$run -s ${COND}_0_${ws}_${run}  "
		la_run

		LAPARAMS="-P 2 -w 2     -T $ws -S 1980$run -s ${COND}_${ws}_0_${run}  "
		la_run

		LAPARAMS="-P 2 -w 1    -T $ws -S 1980$run -s ${COND}L_0_${ws}_${run}  -L  "
		la_run

		LAPARAMS="-P 2 -w 2     -T $ws -S 1980$run -s ${COND}L_${ws}_0_${run}  -L   "
		la_run

		LAPARAMS="-P 2 -w 1    -T $ws -S 1980$run -s ${COND}G_0_${ws}_${run}  -G  "
		la_run

		LAPARAMS="-P 2 -w 2     -T $ws -S 1980$run -s ${COND}G_${ws}_0_${run}  -G   "
		la_run




	done
done



Loading data, please wait...