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Author Zhao, Chenyuan ♦ Wysocki, Bryant T ♦ Liu, Yifang ♦ Thiem, Clare D ♦ McDonald, Nathan R. ♦ Yi, Yang
Source ACM Digital Library
Content type Text
Publisher Association for Computing Machinery (ACM)
File Format PDF
Copyright Year ©2015
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Neuromorphic computing ♦ Analog neuron ♦ Neural encoding ♦ Reservoir computing ♦ Spiking train
Abstract This article presents our research towards developing novel and fundamental methodologies for data representation using spike-timing-dependent encoding. Time encoding efficiently maps a signal's amplitude information into a spike time sequence that represents the input data and offers perfect recovery for band-limited stimuli. In this article, we pattern the neural activities across multiple timescales and encode the sensory information using time-dependent temporal scales. The spike encoding methodologies for autonomous classification of time-series signatures are explored using near-chaotic reservoir computing. The proposed spiking neuron is compact, low power, and robust. A hardware implementation of these results is expected to produce an agile hardware implementation of time encoding as a signal conditioner for dynamical neural processor designs.
Description Author Affiliation: University of Kansas (Zhao, Chenyuan); Air Force Research Laboratory (Wysocki, Bryant T; Thiem, Clare D; McDonald, Nathan R.); University of Kansa (Yi, Yang); Google Inc. (Liu, Yifang)
ISSN 15504832
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2015-09-01
Publisher Place New York
e-ISSN 15504840
Journal ACM Journal on Emerging Technologies in Computing Systems (JETC)
Volume Number 12
Issue Number 3
Page Count 21
Starting Page 1
Ending Page 21


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Source: ACM Digital Library