Zhang, Y-J, Yu, Z-F, Liu, JK orcid.org/0000-0002-5391-7213 et al. (1 more author) (2022) Neural Decoding of Visual Information Across Different Neural Recording Modalities and Approaches. Machine Intelligence Research, 19 (5). pp. 350-365. ISSN 2731-538X
Abstract
Vision plays a peculiar role in intelligence. Visual information, forming a large part of the sensory information, is fed into the human brain to formulate various types of cognition and behaviours that make humans become intelligent agents. Recent advances have led to the development of brain-inspired algorithms and models for machine vision. One of the key components of these methods is the utilization of the computational principles underlying biological neurons. Additionally, advanced experimental neuroscience techniques have generated different types of neural signals that carry essential visual information. Thus, there is a high demand for mapping out functional models for reading out visual information from neural signals. Here, we briefly review recent progress on this issue with a focus on how machine learning techniques can help in the development of models for contending various types of neural signals, from fine-scale neural spikes and single-cell calcium imaging to coarse-scale electroencephalography (EEG) and functional magnetic resonance imaging recordings of brain signals.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © The Author(s) 2022. This is an open access article under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0) (https://creativecommons.org/licenses/by/4.0/) |
Keywords: | Neural decoding, machine learning, deep learning, visual decoding, brain-inspired vision |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 29 Jul 2022 13:43 |
Last Modified: | 25 Jun 2023 23:04 |
Status: | Published |
Publisher: | Springer |
Identification Number: | 10.1007/s11633-022-1335-2 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:189228 |