Noise-assisted persistence and recovery of memory state in a memristive spiking neuromorphic network

• Published in: Chaos, solitons and fractals solitons and fractals, 2021-05, Vol.146, p.110890, Article 110890
• Main Authors: Surazhevsky, 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.
• Format: Article
• Language: English
• ISSN: 0960-0779; 1873-2887
• DOI: 10.1016/j.chaos.2021.110890

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.