Neuromorphic Devices and Computing for Sensing, Memory, and Control

Neuromorphic devices are bioinspired electronic systems that mimic key structures and functions of the nervous system, enabling integration and communication between living tissues and machines. This review examines how neuromorphic devices and computing are designed to emulate the structure, organization, and function of the nervous system. For neuromorphic devices, we first describe strategies that mimic subcellular neural functions. We then highlight how device architecture recapitulates biological topology from subcellular components to brain networks. We next summarize how neuromorphic devices emulate sensory and sensorimotor (sensory-modulation) neural circuits. For neuromorphic computing, we review recent advances in artificial and biological neuromorphic computing, including spiking neural networks, bioinspired learning algorithms, and applications. We also discuss emerging biohybrid intelligence systems leveraging two- and three-dimensional biological networks as computational units. Finally, we outline key challenges, potential milestones, and future directions.

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Publication Details

Journal
Advanced Science
Published
2026-08-13
DOI
https://doi.org/10.1002/advs.77116
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
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article

Neuromorphic Devices and Computing for Sensing, Memory, and Control

Nicholas Schaffer, Xiao Yang, Zhengguang Zhu
Advanced Science
Advanced Memory and Neural Computing
article

Neuromorphic Devices and Computing for Sensing, Memory, and Control

Nicholas Schaffer, Xiao Yang, Zhengguang Zhu
article en

Abstract

Neuromorphic devices are bioinspired electronic systems that mimic key structures and functions of the nervous system, enabling integration and communication between living tissues and machines. This review examines how neuromorphic devices and computing are designed to emulate the structure, organization, and function of the nervous system. For neuromorphic devices, we first describe strategies that mimic subcellular neural functions. We then highlight how device architecture recapitulates biological topology from subcellular components to brain networks. We next summarize how neuromorphic devices emulate sensory and sensorimotor (sensory-modulation) neural circuits. For neuromorphic computing, we review recent advances in artificial and biological neuromorphic computing, including spiking neural networks, bioinspired learning algorithms, and applications. We also discuss emerging biohybrid intelligence systems leveraging two- and three-dimensional biological networks as computational units. Finally, we outline key challenges, potential milestones, and future directions.

Advanced Science
Johns Hopkins University (US), Johns Hopkins Medicine (US), Regenerative Medicine Institute (MX)
Openalex Percentile: Top 18%
Advanced Memory and Neural Computing
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Neuromorphic Devices and Computing for Sensing, Memory, and Control — Nicholas Schaffer, Xiao Yang, et al. · Advanced Science (2026) | TGRS Research Map | TGRS