Investigating R(t) Functions for Spike-Timing-Dependent Plasticity in Memristive Neural Networks

Farhana Afrin; Kurtis D. Cantley

Investigating R(t) Functions for Spike-Timing-Dependent Plasticity in Memristive Neural Networks

Farhana Afrin, Kurtis D. Cantley

Electrical Engineering Department

Boise State University

Research output: Chapter in Book/Report/Conference proceeding › Chapter

1 Scopus citations

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.

Original language	American English
Title of host publication	2023 IEEE 66th International Midwest Symposium on Circuits and Systems (MWSCAS)
State	Published - 1 Jan 2023

Keywords

Anti-Hebbian
Hebbian
R(t) element
Spike-Timing-Dependent Plasticity
Spiking Neural Network
memristor

EGS Disciplines

Electrical and Computer Engineering

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Investigating R(t) Functions for Spike-Timing-Dependent PlasticitFinal published version, 731 KBLicense: Unspecified

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title = "Investigating R(t) Functions for Spike-Timing-Dependent Plasticity in Memristive Neural Networks",

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.",

keywords = "Anti-Hebbian, Hebbian, R(t) element, Spike-Timing-Dependent Plasticity, Spiking Neural Network, memristor",

author = "Farhana Afrin and Cantley, {Kurtis D.}",

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language = "American English",

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AU - Afrin, Farhana

AU - Cantley, Kurtis D.

N1 - 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

PY - 2023/1/1

Y1 - 2023/1/1

N2 - 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.

AB - 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.

KW - Anti-Hebbian

KW - Hebbian

KW - R(t) element

KW - Spike-Timing-Dependent Plasticity

KW - Spiking Neural Network

KW - memristor

UR - https://scholarworks.boisestate.edu/electrical_facpubs/568

M3 - Chapter

BT - 2023 IEEE 66th International Midwest Symposium on Circuits and Systems (MWSCAS)

ER -

Investigating R(t) Functions for Spike-Timing-Dependent Plasticity in Memristive Neural Networks

Abstract

Keywords

EGS Disciplines

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