Publication Type
Journal Article
Version
publishedVersion
Publication Date
4-2013
Abstract
Learning scheme is the key to the utilization of spike-based computation and the emulation of neural/synaptic behaviors toward realization of cognition. The biological observations reveal an integrated spike time- and spike rate-dependent plasticity as a function of presynaptic firing frequency. However, this integrated rate-temporal learning scheme has not been realized on any nano devices. In this paper, such scheme is successfully demonstrated on a memristor. Great robustness against the spiking rate fluctuation is achieved by waveform engineering with the aid of good analog properties exhibited by the iron oxide-based memristor. The spike-time-dependence plasticity (STDP) occurs at moderate presynaptic firing frequencies and spike-rate-dependence plasticity (SRDP) dominates other regions. This demonstration provides a novel approach in neural coding implementation, which facilitates the development of bio-inspired computing systems.
Keywords
memristor, neuromorphic, cognitive computing
Discipline
Databases and Information Systems | Electrical and Computer Engineering
Research Areas
Data Science and Engineering
Publication
Scientific Reports
Volume
4
Issue
4755
First Page
1
Last Page
6
ISSN
2045-2322
Identifier
10.1038%2Fsrep04755
Publisher
Nature Research
Citation
HE, Wei; HUANG, Kejie; NING, Ning; RAMANATHAN, Kiruthika; LI, Guoqi; JIANG, Yu; SZE, JiaYin; SHI, Luping; ZHAO, Rong; and PEI, Jing.
Enabling an integrated rate-temporal learning scheme on memristor. (2013). Scientific Reports. 4, (4755), 1-6.
Available at: https://ink.library.smu.edu.sg/sis_research/7266
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1038%2Fsrep04755