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Spike-Time-Dependent Encoding for Neuromorphic Processors

Spike-Time-Dependent Encoding for Neuromorphic Processors Spike-Time-Dependent Encoding for Neuromorphic Processors CHENYUAN ZHAO, University of Kansas BRYANT T. WYSOCKI, Air Force Research Laboratory YIFANG LIU, Google Inc. CLARE D. THIEM and NATHAN R. MCDONALD, Air Force Research Laboratory YANG YI, University of Kansa 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. Categories and Subject Descriptors: B.7.1 [Integrated Circuits]: Types and Design Styles--Advanced technologies General Terms: Design, Performance Additional Key Words and Phrases: Neuromorphic computing, neural encoding, analog neuron, spiking train, reservoir computing ACM Reference Format: Chenyuan Zhao, Bryant T. Wysocki, Yifang Liu, Clare D. Thiem, Nathan R. McDonald, http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Journal on Emerging Technologies in Computing Systems (JETC) Association for Computing Machinery

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2015 by ACM Inc.
ISSN
1550-4832
DOI
10.1145/2738040
Publisher site
See Article on Publisher Site

Abstract

Spike-Time-Dependent Encoding for Neuromorphic Processors CHENYUAN ZHAO, University of Kansas BRYANT T. WYSOCKI, Air Force Research Laboratory YIFANG LIU, Google Inc. CLARE D. THIEM and NATHAN R. MCDONALD, Air Force Research Laboratory YANG YI, University of Kansa 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. Categories and Subject Descriptors: B.7.1 [Integrated Circuits]: Types and Design Styles--Advanced technologies General Terms: Design, Performance Additional Key Words and Phrases: Neuromorphic computing, neural encoding, analog neuron, spiking train, reservoir computing ACM Reference Format: Chenyuan Zhao, Bryant T. Wysocki, Yifang Liu, Clare D. Thiem, Nathan R. McDonald,

Journal

ACM Journal on Emerging Technologies in Computing Systems (JETC)Association for Computing Machinery

Published: Sep 21, 2015

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