Chakrabarty, S. orcid.org/0000-0002-4389-8290 and Petrovici, M.A. (Cover date: 2023) Is neuromorphic computing disruptive enough to 1) advance our understanding of the brain and 2) make the design and working of (bio)electronic devices efficient and scalable? Research Directions: Bioelectronics, 1. e5. ISSN 2753-8524
Abstract
Neuromorphic computing is an approach to computing that is inspired by the structure and function of the brain. In terms of basic research, it aims at improving our understanding of biological intelligence by replicating aspects of its physical substrate – spiking neurons, synaptic plasticity etc. – and harness them towards also replicating its function. With respect to technological advances, it aims to inherit the brain’s combination of computational prowess and extreme energy efficiency; this is thought to foster a plethora of applications, from large-scale neuromorphic systems for machine learning to small-scale edge devices for signal processing and control, for example in the form of wearables for healthcare or adaptive sensors/processors for autonomous agents. The demand and usefulness of neuromorphic computing in bioelectronics is likely to increase in the future as researchers continue to explore its capabilities and develop new applications.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © The Author(s), 2023. This is an open access article under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. |
Keywords: | neuromorphic |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Biological Sciences (Leeds) > School of Biomedical Sciences (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 08 Aug 2023 13:36 |
Last Modified: | 30 Nov 2023 16:40 |
Status: | Published |
Publisher: | Cambridge University Press (CUP) |
Identification Number: | 10.1017/bel.2023.5 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:202228 |