Self-sustained green neuromorphic interfaces

Tianda Fu; Xiaomeng Liu; Shuai Fu; Trevor Woodard; Hongyan Gao; Derek R. Lovley; Jun Yao

doi:10.1038/s41467-021-23744-2

Nature Communications (Jun 2021)

Self-sustained green neuromorphic interfaces

Tianda Fu,
Xiaomeng Liu,
Shuai Fu,
Trevor Woodard,
Hongyan Gao,
Derek R. Lovley,
Jun Yao

Affiliations

Tianda Fu: Department of Electrical and Computer Engineering, University of Massachusetts
Xiaomeng Liu: Department of Electrical and Computer Engineering, University of Massachusetts
Shuai Fu: Department of Electrical and Computer Engineering, University of Massachusetts
Trevor Woodard: Department of Microbiology, University of Massachusetts
Hongyan Gao: Department of Electrical and Computer Engineering, University of Massachusetts
Derek R. Lovley: Department of Microbiology, University of Massachusetts
Jun Yao: Department of Electrical and Computer Engineering, University of Massachusetts

DOI: https://doi.org/10.1038/s41467-021-23744-2
Journal volume & issue: Vol. 12, no. 1
pp. 1 – 8

Abstract

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For bio-inspired neuromorphic interfaces to emulate biological signal processing and self-sustainability, the mismatch between sensing and computing signals must be addressed. Here, the authors report sensor-driven, integrated neuromorphic systems with signal matching at the biological level.

Published in Nature Communications

ISSN: 2041-1723 (Online)
Publisher: Nature Portfolio
Country of publisher: United Kingdom
LCC subjects: Science
Website: https://www.nature.com/ncomms/

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