Temporal Coding of Binary Patterns for Learning of Spiking Neuromorphic Systems Based on Nanocomposite Memristors

• Published in: Nanobiotechnology Reports (Online) Vol. 16; no. 6; pp. 732 - 736
• Main Authors: Nikiruy, K. E., Emelyanov, A. V., Sitnikov, A. V., Rylkov, V. V., Demin, V. A.
• Format: Journal Article
• Language: English
• Published: Moscow Pleiades Publishing 01.11.2021; Springer Nature B.V
• Subjects: Binary codes; Chemistry and Materials Science; Clustering; Coding; Industrial and Production Engineering; Learning; Lithium niobates; Machines; Manufacturing; Materials Science; Memristors; Nanocomposites; Nanoelectronics and Neuromorphic Computer Systems; Nanotechnology; Neural networks; Processes; Spiking; Synapses
• ISSN: 2635-1676; 1995-0780; 2635-1684; 1995-0799
• DOI: 10.1134/S2635167621060161

The metal/nanocomposite/metal (M/NC/M) memristive structures based on (Co 40 Fe 40 B 20 ) x (LiNbO 3 ) 100– x have been studied. It has been shown that such memristors may change their conductance according to the bioinspired spike-timing-dependent plasticity (STDP) rules. Spiking neural network with 4 presynaptic inputs connected by memristor-synapses with a postsynaptic threshold neuron-integrator has been created, in which the images clustering with temporal coding has been implemented using the STDP rule. Thus, the fundamental possibility of using a temporal coding method, which is more effective than population-frequency coding, has been demonstrated for self-learning of spiking neuromorphic systems with synaptic weights based on nanocomposite memristors.