Plasticity in memristive devices for spiking neural networks
Résumé
Memristive devices present a new device technology allowing for the realization of compact non-volatile memories. Some of them are already in the process of industrialization. Additionally, they exhibit complex multilevel and plastic behaviors, which make them good candidates for the implementation of artificial synapses in neuromorphic engineering. However, memristive effects rely on diverse physical mechanisms, and their plastic behaviors differ strongly from one technology to another. Here, we present measurements performed on different memristive devices and the opportunities that they provide. We show that they can be used to implement different learning rules whose properties emerge directly from device physics: real time or accelerated operation, deterministic or stochastic behavior, long term or short term plasticity. We then discuss how such devices might be integrated into a complete architecture. These results highlight that there is no unique way to exploit memristive devices in neuromorphic systems. Understanding and embracing device physics is the key for their optimal use.
Mots clés
short term memory
semiconductor
action potential
controlled study
electric potential
electric resistance
human
learning
learning algorithm
long term memory
memristive device
nanodevice
nerve cell network
nerve cell plasticity
neurotransmitter release
Review
spiking neural network
stochastic model
synapse
torque
waveform
memristor
neuromorphic engineering
plasticity
hardware neural network
Domaines
Sciences de l'ingénieur [physics]Origine | Fichiers éditeurs autorisés sur une archive ouverte |
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