Throughout history, neuroscience has had a significant impact on the development of artificial intelligence (AI), such as Perceptron - the first artificial neural network, and computer vision models. More recently, there have been growing trends to leverage neuroscience principles to push AI forward. Meanwhile, neuroscience is entering a new era of large-scale high-resolution data and trying to decipher the network level mechanism of the information processing in the brain than ever before. Numerous computational models can be used for this goal, but recently, new efforts have been made that apply deep neural network models to explain neural representations and learning dynamics. Despite the growing need for cross-disciplinary research between two fields, however, cultural differences between them make communication difficult for researchers in both fields.
The goal of this Research Topic is to close the gap between AI and the brain researchers. Through interdisciplinary research, AI researchers will gain a better understanding of how neuroscientific findings should be translated into brain-inspired AI. They will learn which areas of neuroscience can impact on which problems in AI, and how the descriptions of mechanisms in neuroscience can be translated into functional, “machine learning-accessible” descriptions. Neuroscientists will gain novel insights into the information processing mechanism of the brain that would not have been discovered solely from the neuroscientific perspective. They will be able to build neural theory that capture the essence of the brain dynamics in more abstract level, beyond detailed descriptions of the neural components and mechanisms.
We welcome submissions of original research articles, reviews, or perspectives related to interdisciplinary approaches toward bridging the gap between AI and neuroscience. Potential topics include but are not limited to:
• Developing AI algorithms inspired by neuroscientific discoveries.
• Novel insights and explanations for neural mechanisms inspired by deep neural networks.
• Comparative analysis of information coding and processing between AI and the brain.
• Translation of the neuroscientific description of the brain mechanism into the “machine learning accessible” descriptions.
• Suggestions of the neuroscientific knowledge that could potentially address the challenges faced by modern AI.
Throughout history, neuroscience has had a significant impact on the development of artificial intelligence (AI), such as Perceptron - the first artificial neural network, and computer vision models. More recently, there have been growing trends to leverage neuroscience principles to push AI forward. Meanwhile, neuroscience is entering a new era of large-scale high-resolution data and trying to decipher the network level mechanism of the information processing in the brain than ever before. Numerous computational models can be used for this goal, but recently, new efforts have been made that apply deep neural network models to explain neural representations and learning dynamics. Despite the growing need for cross-disciplinary research between two fields, however, cultural differences between them make communication difficult for researchers in both fields.
The goal of this Research Topic is to close the gap between AI and the brain researchers. Through interdisciplinary research, AI researchers will gain a better understanding of how neuroscientific findings should be translated into brain-inspired AI. They will learn which areas of neuroscience can impact on which problems in AI, and how the descriptions of mechanisms in neuroscience can be translated into functional, “machine learning-accessible” descriptions. Neuroscientists will gain novel insights into the information processing mechanism of the brain that would not have been discovered solely from the neuroscientific perspective. They will be able to build neural theory that capture the essence of the brain dynamics in more abstract level, beyond detailed descriptions of the neural components and mechanisms.
We welcome submissions of original research articles, reviews, or perspectives related to interdisciplinary approaches toward bridging the gap between AI and neuroscience. Potential topics include but are not limited to:
• Developing AI algorithms inspired by neuroscientific discoveries.
• Novel insights and explanations for neural mechanisms inspired by deep neural networks.
• Comparative analysis of information coding and processing between AI and the brain.
• Translation of the neuroscientific description of the brain mechanism into the “machine learning accessible” descriptions.
• Suggestions of the neuroscientific knowledge that could potentially address the challenges faced by modern AI.
