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Noise-assisted persistence and recovery of memory state in a memristive spiking neuromorphic networkSurazhevsky, I.A.; Demin, V.A.; Ilyasov, A.I.; Emelyanov, A.V.; Nikiruy, K.E.; Rylkov, V.V.; Shchanikov, S.A.; Bordanov, I.A.; Gerasimova, S.A.; Guseinov, D.V.; Malekhonova, N.V.; Pavlov, D.A.; Belov, A.I.; Mikhaylov, A.N.; Kazantsev, V.B.; Valenti, D.; Spagnolo, B.; Kovalchuk, M.V.
Chaos, solitons and fractals, 20/May , Letnik: 146Journal Article
We investigate the constructive role of an external noise signal, in the form of a low-rate Poisson sequence of pulses supplied to all inputs of a spiking neural network, consisting in maintaining for a long time or even recovering a memory trace (engram) of the image without its direct renewal (or rewriting). In particular, this unique dynamic property is demonstrated in a single-layer spiking neural network consisting of simple integrate-and-fire neurons and memristive synaptic weights. This is carried out by preserving and even fine-tuning the conductance values of memristors in terms of dynamic plasticity, specifically spike-timing-dependent plasticity-type, driven by overlapping pre- and postsynaptic voltage spikes. It has been shown that the weights can be to a certain extent unreliable, due to such characteristics as the limited retention time of resistive state or the variation of switching voltages. Such a noise-assisted persistence of memory, on one hand, could be a prototypical mechanism in a biological nervous system and, on the other hand, brings one step closer to the possibility of building reliable spiking neural networks composed of unreliable analog elements.
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