典型文献
Neural Decoding of Visual Information Across Different Neural Recording Modalities and Approaches
文献摘要:
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 tech-niques 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 reson-ance imaging recordings of brain signals.
文献关键词:
中图分类号:
作者姓名:
Yi-Jun Zhang;Zhao-Fei Yu;Jian.K.Liu;Tie-Jun Huang
作者机构:
Department of Computer Science and Engineering,Shanghai Jiao Tong University,Shanghai 200240,China;School of Computer Science,Peking University,Beijing 100190,China;Institute for Artificial Intelligence,Peking University,Beijing 100190,China;School of Computing,University of Leeds,Leeds LS2 9JT,UK;Beijing Academy of Artificial Intelligence,Beijing 100190,China
文献出处:
引用格式:
[1]Yi-Jun Zhang;Zhao-Fei Yu;Jian.K.Liu;Tie-Jun Huang-.Neural Decoding of Visual Information Across Different Neural Recording Modalities and Approaches)[J].机器智能研究(英文),2022(05):350-365
A类:
Modalities,contending,reson
B类:
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AB值:
0.657038
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