Editorial: Brain-inspired cognition and understanding for next-generation AI: Computational models, architectures and learning algorithms

Yuqi Han; Chenwei Deng; Guang Bin Huang

doi:10.3389/fnins.2023.1169027

Editorial: Brain-inspired cognition and understanding for next-generation AI: Computational models, architectures and learning algorithms

Yuqi Han, Chenwei Deng^*, Guang Bin Huang

^*Corresponding author for this work

School of Information and Electronics

Research output: Contribution to journal › Editorial

Original language	English
Article number	1169027
Journal	Frontiers in Neuroscience
Volume	17
DOIs	https://doi.org/10.3389/fnins.2023.1169027
Publication status	Published - 2023

Keywords

artificial intelligence
brain-inspired cognition
machine learning
multimodal perception
perception science

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10.3389/fnins.2023.1169027

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title = "Editorial: Brain-inspired cognition and understanding for next-generation AI: Computational models, architectures and learning algorithms",

keywords = "artificial intelligence, brain-inspired cognition, machine learning, multimodal perception, perception science",

author = "Yuqi Han and Chenwei Deng and Huang, {Guang Bin}",

year = "2023",

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language = "English",

volume = "17",

journal = "Frontiers in Neuroscience",

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publisher = "Frontiers Media SA",

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T2 - Brain-inspired cognition and understanding for next-generation AI: Computational models, architectures and learning algorithms

AU - Han, Yuqi

AU - Deng, Chenwei

AU - Huang, Guang Bin

PY - 2023

Y1 - 2023

KW - artificial intelligence

KW - brain-inspired cognition

KW - machine learning

KW - multimodal perception

KW - perception science

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DO - 10.3389/fnins.2023.1169027

M3 - Editorial

AN - SCOPUS:85152543976

SN - 1662-4548

VL - 17

JO - Frontiers in Neuroscience

JF - Frontiers in Neuroscience

M1 - 1169027

ER -

Editorial: Brain-inspired cognition and understanding for next-generation AI: Computational models, architectures and learning algorithms

Keywords

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