About this Research Topic
Neural dynamics studies neural networks through dynamic approaches or views dynamic systems as neural networks. Inspired by the patterns of neuronal connections and the evolution of neural signals in the brain, neural dynamics emerges as a powerful tool for solving time-varying and complex problems online. Originating from and influencing neuroscience, neural dynamics propels the development of brain-inspired control and computing. For example, cerebellar-like neural dynamics for intelligent control, brain-inspired neural dynamics for neuroscience data analysis, and parameterized neural dynamics for computational neuroscience. Neural dynamics models that internalize the dynamics evolution of neurons offer robustness and noise immunity, enhancing the performance of processing and decoding of neural signals. Additionally, the online solving ability of neural dynamics suits practical man-machine interaction application scenarios like the brain-machine interface. Continuous advancements in neural dynamics for brain-inspired control and computing hold great promise for the future of brain-like intelligence.
Though the neural dynamics algorithms for brain-inspired control and computing achieve plentiful progress, there are still many challenges remain, such as model simplification, stability issues, high computational demand due to high-dimensional data, and neural signal noises or artifacts.
This Research Topic aims to further advance the theoretical understanding of neural dynamics and the brain, and leverage this understanding to propel the brain-inspired applications. Theoretically, we seek to investigate the principles of brain cognition and the intersection of neuroscience theory with neural dynamics. Specially, we aim to understand brain cognitive and motor functions, translate neuroscience knowledge into technology and enhance the interpretability of neural dynamics in brain-inspired control and computing. In practical applications, we plan to dig the neural dynamics for brain-inspired control and computing technology. Our focus includes cerebellar-like motor control algorithms and brain-inspired neural networks for human-machine interaction technologies, brain-machine interface, neuroscience data analysis, and so on.
To achieve this, we encourage researchers to investigate the composition structure and functional implementation principles of multimodal brain perception cognitive, and to develop neural dynamics models and systems based on these principles for brain-inspired control and computing.
We warmly welcome authors to submit their original research papers, including innovative theories and models, along with their theoretical, experimental, and clinical applications.
Topics of interest include, but are not limited to:
- Theories in neurocircuitry related to synapse formation, emotions generation, sensory processing, and motion control
- Mechanism in brain regions related to perceptual learning, cognitive formation, memory storage, retrieval, generalization, and decision-making
- Theories in correlating artificial intelligence neural networks with biological neural systems
- Foundations of neural dynamics intersecting with brain-inspired control and computing
- Multi-channel human-machine interaction technologies driven by neural dynamics, including electromyography sensing, olfactory simulation, and tactile feedback
- Neural dynamics for closed-loop brain-machine interface for motor and consciousness disorder rehabilitation
- Medical robots supporting online learning with neural dynamics
- Applications in diagnosing and intervening in cognitive impairment-related brain disorders using neural dynamics
Though the neural dynamics algorithms for brain-inspired control and computing achieve plentiful progress, there are still many challenges remain, such as model simplification, stability issues, high computational demand due to high-dimensional data, and neural signal noises or artifacts.
This Research Topic aims to further advance the theoretical understanding of neural dynamics and the brain, and leverage this understanding to propel the brain-inspired applications. Theoretically, we seek to investigate the principles of brain cognition and the intersection of neuroscience theory with neural dynamics. Specially, we aim to understand brain cognitive and motor functions, translate neuroscience knowledge into technology and enhance the interpretability of neural dynamics in brain-inspired control and computing. In practical applications, we plan to dig the neural dynamics for brain-inspired control and computing technology. Our focus includes cerebellar-like motor control algorithms and brain-inspired neural networks for human-machine interaction technologies, brain-machine interface, neuroscience data analysis, and so on.
To achieve this, we encourage researchers to investigate the composition structure and functional implementation principles of multimodal brain perception cognitive, and to develop neural dynamics models and systems based on these principles for brain-inspired control and computing.
We warmly welcome authors to submit their original research papers, including innovative theories and models, along with their theoretical, experimental, and clinical applications.
Topics of interest include, but are not limited to:
- Theories in neurocircuitry related to synapse formation, emotions generation, sensory processing, and motion control
- Mechanism in brain regions related to perceptual learning, cognitive formation, memory storage, retrieval, generalization, and decision-making
- Theories in correlating artificial intelligence neural networks with biological neural systems
- Foundations of neural dynamics intersecting with brain-inspired control and computing
- Multi-channel human-machine interaction technologies driven by neural dynamics, including electromyography sensing, olfactory simulation, and tactile feedback
- Neural dynamics for closed-loop brain-machine interface for motor and consciousness disorder rehabilitation
- Medical robots supporting online learning with neural dynamics
- Applications in diagnosing and intervening in cognitive impairment-related brain disorders using neural dynamics
Keywords: Brain-inspired control, brain-like computing, human-machine interaction, brain-machine interfaces, neural dynamics, neural networks
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
