TY - GEN
T1 - R(t)-Based Spike-Timing-Dependent Plasticity in Memristive Neural Networks
AU - Afrin, Farhana
AU - Cantley, Kurtis D.
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - memristor
KW - R(t) element
KW - spike triplet learning
KW - Spike-Timing-Dependent Plasticity
KW - Spiking Neural Network
UR - http://www.scopus.com/inward/record.url?scp=85156223230&partnerID=8YFLogxK
U2 - 10.1109/WMED58543.2023.10097441
DO - 10.1109/WMED58543.2023.10097441
M3 - Conference contribution
AN - SCOPUS:85156223230
T3 - IEEE Workshop on Microelectronics and Electron Devices, WMED
BT - 2023 IEEE Workshop on Microelectronics and Electron Devices, WMED 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 20th Annual IEEE Workshop on Microelectronics and Electron Devices, WMED 2023
Y2 - 31 March 2023
ER -