@inproceedings{75e377290b854b16a12da0c9ea40de27,
title = "R(t)-Based Spike-Timing-Dependent Plasticity in Memristive Neural Networks",
abstract = "Inspired by the human brain, neuromorphic computation should be extremely efficient at very large scales due to inherent parallelism, scalability, and fault and failure tolerance. Spike- Timing-Dependent Plasticity (STDP) is one of the most biologically plausible synaptic learning behaviors. The proposed generic model of time-varying resistance, or R(t) elements in this work can produce STDP in electronic spiking neural networks with memristive synapses that is very similar to that observed in biology. Both pair-based and triplet-based STDP is verified with the proposed generic R(t) model.",
keywords = "memristor, R(t) element, spike triplet learning, Spike-Timing-Dependent Plasticity, Spiking Neural Network",
author = "Farhana Afrin and Cantley, \{Kurtis D.\}",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 20th Annual IEEE Workshop on Microelectronics and Electron Devices, WMED 2023 ; Conference date: 31-03-2023",
year = "2023",
doi = "10.1109/WMED58543.2023.10097441",
language = "English",
series = "IEEE Workshop on Microelectronics and Electron Devices, WMED",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2023 IEEE Workshop on Microelectronics and Electron Devices, WMED 2023",
address = "United States",
}