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- Title
Enabling an Integrated Rate-temporal Learning Scheme on Memristor.
- Authors
Wei He; Kejie Huang; Ning Ning; Ramanathan, Kiruthika; Guoqi Li; Yu Jiang; JiaYin Sze; Luping Shi; Rong Zhao; Jing Pei
- 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.
- Subjects
MEMRISTORS; COGNITIVE ability; MATERIAL plasticity; PRESYNAPTIC receptors; NEURAL codes
- Publication
Scientific Reports, 2014, p1
- ISSN
2045-2322
- Publication type
Article
- DOI
10.1038/srep04755