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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for i in 0 1 2 3 4 5 6 7 8 9
do
	#echo ./lamodel -S 198$1 $*
	./lamodel -S 198$i -P 2 -p 6 -i 1 -T $1  -d $2 -s "_sim$2"
	./lamodel -S 198$i -P 2 -p 6 -i 1 -T $1 -c  -d $2 -s "_sim$2"
	python stats.py N400.B20.I12.i1.P2.p6.T$1.S198$i.sn_sim$2 NO 0
	python stats.py N400.B20.I12.i1.P2.p6.T$1.S198$i.sc_sim$2 NO 0
	echo Done
done



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