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Titolo:
Dynamic stochastic synapses as computational units
Autore:
Maass, W; Zador, AM;
Indirizzi:
Graz Tech Univ, Inst Theoret Comp Sci, A-8010 Graz, Austria Graz Tech Univ Graz Austria A-8010 heoret Comp Sci, A-8010 Graz, Austria Salk Inst Biol Studies, La Jolla, CA 92037 USA Salk Inst Biol Studies La Jolla CA USA 92037 dies, La Jolla, CA 92037 USA
Titolo Testata:
NEURAL COMPUTATION
fascicolo: 4, volume: 11, anno: 1999,
pagine: 903 - 917
SICI:
0899-7667(19990515)11:4<903:DSSACU>2.0.ZU;2-P
Fonte:
ISI
Lingua:
ENG
Soggetto:
NEOCORTICAL PYRAMIDAL NEURONS; LONG-TERM POTENTIATION; TRANSMITTER RELEASE; EXCITATORY SYNAPSES; VISUAL-CORTEX; PROBABILITY; HIPPOCAMPUS; PLASTICITY; FACILITATION; DEPRESSION;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Life Sciences
Engineering, Computing & Technology
Citazioni:
31
Recensione:
Indirizzi per estratti:
Indirizzo: Maass, W Graz Tech Univ, Inst Theoret Comp Sci, A-8010 Graz, Austria Graz Tech Univ Graz Austria A-8010 mp Sci, A-8010 Graz, Austria
Citazione:
W. Maass e A.M. Zador, "Dynamic stochastic synapses as computational units", NEURAL COMP, 11(4), 1999, pp. 903-917

Abstract

In most neural network models, synapses are treated as static weights thatchange only with the slow time scales of learning. It is well known, however, that synapses are highly dynamic and show use-dependent plasticity overa wide range of time scales. Moreover, synaptic transmission is an inherently stochastic process: a spike arriving at a presynaptic terminal triggersthe release of a vesicle of neurotransmitter from a release site with a probability that can be much less than one. We consider a simple model for dynamic stochastic synapses that can easilybe integrated into common models for networks of integrate-and-fire neurons (spiking neurons). The parameters of this model have direct interpretations in terms of synaptic physiology. We investigate the consequences of the model for computing with individual spikes and demonstrate through rigoroustheoretical results that the computational power of the network is increased through the use of dynamic synapses.

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Documento generato il 09/07/20 alle ore 20:58:08