Document Type
Conference Proceeding
Publication Date
2023
Abstract
Brain-inspired neuromorphic computation can be extremely efficient at very large scales due to inherent parallelism, scalability, and fault and failure tolerance. One widely used, biologically plausible synaptic learning mechanism is spike-timing-dependent plasticity (STDP). The proposed generic model of time-varying resistance, or R(t) elements in this work, can produce classical and beyond classical STDP in electronic spiking neural networks with memristive synapses. Hebbian and Anti-Hebbian STDP is verified with the proposed generic R(t) model by tuning the R(t) function. By appropriately placing R(t) functions with selective resistance values, symmetric or non-classical STDP learning behavior is achieved.
Copyright Statement
© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. https://doi.org/10.1109/MWSCAS57524.2023.10405895
Publication Information
Afrin, Farhana and Cantley, Kurtis D. (2023). "Investigating R(t) Functions for Spike-Timing-Dependent Plasticity in Memristive Neural Networks". In 2023 IEEE 66th International Midwest Symposium on Circuits and Systems (MWSCAS) (pp. 659-663). IEEE. https://doi.org/10.1109/MWSCAS57524.2023.10405895