Abstract
Nowadays, the task of creating and training spiking neural networks (SNN) is extremely relevant due to their high energy efficiency achieved by implementing such networks via neuromorphic hardware.
Especially interesting is the possibility of building SNNs based on memristors, which have properties that potentially allow them to be used as analog synapses. With that in mind, it seems relevant to study spike networks built upon plasticity rules that correspond to the experimentally observed nonlinear laws of conductivity change in memristors.
Earlier it was shown that spiking neural networks trained with a biologically inspired local STDP (Spike-Timing-Dependent Plasticity) rule are capable of solving classification problems successfully. In addition, it was also demonstrated that classification problems can also be solved with spiking neural networks operating with a plasticity rule that models the change in conductivity in nanocomposite (NC) memristors.
This paper presents a continuation of the study of the applicability of memristive plasticity rules on the handwritten digit recognition problem. Two types of memristive plasticity are compared: for nanocomposite and PPX memristors. It is shown that both models can successfully solve the classification problem, and the key differences between them are identified.
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Acknowledgements
This work has been supported by the Russian Science Foundation grant No.21-11-00328 and has been carried out using computing resources of the federal collective usage center Complex for Simulation and Data Processing for Mega-science Facilities at NRC “Kurchatov Institute”, http://ckp.nrcki.ru/.
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Sboev, A., Davydov, Y., Rybka, R., Vlasov, D., Serenko, A. (2022). A Comparison of Two Variants of Memristive Plasticity for Solving the Classification Problem of Handwritten Digits Recognition. In: Klimov, V.V., Kelley, D.J. (eds) Biologically Inspired Cognitive Architectures 2021. BICA 2021. Studies in Computational Intelligence, vol 1032. Springer, Cham. https://doi.org/10.1007/978-3-030-96993-6_48
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