"If the human brain were so simple that we could understand it, we would be so simple that we couldn’t.” This interesting quote from Emerson M. Pugh reflects our current position in trying to understand the human brain.
Indeed, we are currently still far away from understanding how our brain works, we require multi-disciplinary inputs and efforts to unravel its intrinsic complexity.
Basic research outputs’ applications, public health and industrial applications, such as artificial intelligence (AI) and machine learning will benefit from advances in brain research. On the other hand, AI will accelerate the research of neuroscience.
To connect neuroscience and AI we need to provide adequate models that draw from computational neuroscience.
Computational neuroscience could bridge these two fast-developing fields through adequate models representing and simulating the brain’s unique architecture and functions. To perform image and data analysis we also need to implement brain-inspired intelligence. A closed-loop may be organized relying on the iterations between neuroscience and AI.
AI should take advantage of brain research data, novel insights from neuroscience may then improve AI’s design and performance. Additionally, AI and deep learning, in particular, could be used in medical informatics for brain diseases.
Recently, genetic systems have proven to be more intelligent than assumed. Neural networks, such as a classical classifying, or associative perceptron networks, can be implemented on the scale of a simple genetic network inside the cell. The brain is a network of networks.
In addition, the integrated information theory, a theoretical framework to understand consciousness and the link between the brain and consciousness, represents a milestone in the ongoing effort to explain what consciousness is, and the reason why it might be associated with certain physical systems.
AI advances have also enhanced neuroimaging techniques and data analysis allowing for a fine investigation of brain structure (e.g. neuron morphology), function (e.g. hemodynamics) and disease (e.g. Alzheimer’s disease). Further discoveries in brain research will help to promote brain-inspired intelligence.
This Research Topic aims to provide a state-of-the-art review of the use of artificial intelligence in brain research and data analysis, integrating neuroscience, mathematics, and informatics and setting a paradigm for computational neuroscience. We welcome all types of articles focusing on:
• AI applied in neuroscience
• AI-inspired by neuroscience
• Closed-loop iterations between AI and neuroscience
"If the human brain were so simple that we could understand it, we would be so simple that we couldn’t.” This interesting quote from Emerson M. Pugh reflects our current position in trying to understand the human brain.
Indeed, we are currently still far away from understanding how our brain works, we require multi-disciplinary inputs and efforts to unravel its intrinsic complexity.
Basic research outputs’ applications, public health and industrial applications, such as artificial intelligence (AI) and machine learning will benefit from advances in brain research. On the other hand, AI will accelerate the research of neuroscience.
To connect neuroscience and AI we need to provide adequate models that draw from computational neuroscience.
Computational neuroscience could bridge these two fast-developing fields through adequate models representing and simulating the brain’s unique architecture and functions. To perform image and data analysis we also need to implement brain-inspired intelligence. A closed-loop may be organized relying on the iterations between neuroscience and AI.
AI should take advantage of brain research data, novel insights from neuroscience may then improve AI’s design and performance. Additionally, AI and deep learning, in particular, could be used in medical informatics for brain diseases.
Recently, genetic systems have proven to be more intelligent than assumed. Neural networks, such as a classical classifying, or associative perceptron networks, can be implemented on the scale of a simple genetic network inside the cell. The brain is a network of networks.
In addition, the integrated information theory, a theoretical framework to understand consciousness and the link between the brain and consciousness, represents a milestone in the ongoing effort to explain what consciousness is, and the reason why it might be associated with certain physical systems.
AI advances have also enhanced neuroimaging techniques and data analysis allowing for a fine investigation of brain structure (e.g. neuron morphology), function (e.g. hemodynamics) and disease (e.g. Alzheimer’s disease). Further discoveries in brain research will help to promote brain-inspired intelligence.
This Research Topic aims to provide a state-of-the-art review of the use of artificial intelligence in brain research and data analysis, integrating neuroscience, mathematics, and informatics and setting a paradigm for computational neuroscience. We welcome all types of articles focusing on:
• AI applied in neuroscience
• AI-inspired by neuroscience
• Closed-loop iterations between AI and neuroscience
