This research topic aims to showcase the application of control science methods in neuroscience research and the inspiration neuroscience provides for modern control theory and artificial intelligence.
The central theme of this issue encompasses the utilization of various interdisciplinary approaches in neuroscience research. This includes, but is not limited to, methods such as nonlinear system dynamics, control theory, artificial neural networks, machine learning, and more. Additionally, it delves into the principles of perception, behavior, neural activity, decision mechanisms, and their contributions to fields such as engineering and artificial intelligence.
This issue extensively explores the mutual promotion between control science methods and neuroscience. The topics covered include but are not limited to:
- Network Topology Analysis of Neural Networks. Investigate the structural aspects of neural networks by applying methods from control science to analyze network topology. This involves studying the connectivity patterns, clustering, and centrality of nodes within the neural network.
- Functional Connectivity Modeling: Use control science techniques to model the functional interactions between different brain regions. Explore how changes in one region's activity affect others and apply complex network and nonlinear system analysis to understand the dynamics of information flow, from the micro-scale of synoptics to macro scale of brain network.
- Adaptive Control Mechanisms: Study how adaptive control mechanisms influence the adaptability of neural networks. Investigate the ability of the brain to self-regulate and adjust its connectivity patterns based on external stimuli or changes in the environment.
- Control-Enhanced Perception and Decision Mechanisms: Investigate the integration of control science principles into the study of perception, behavior, and decision mechanisms. Explore how enhancing the controllability of neural processes influences the accuracy and efficiency of decision-making, shedding light on the dynamic interplay between control mechanisms and cognitive functions.
We hope this research topic becomes a valuable resource for researchers and practitioners in the fields of control science, neuroscience, and other interdisciplinary studies. These papers offer insights into various interdisciplinary technologies applicable to these domains, emphasizing the significance of collaboration between neuroscientists and engineers in addressing real-world challenges and overcoming research hurdles.
Keywords:
artificial neural networks, complex networks, interdisciplinary neural science, perception neuroscience
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.
This research topic aims to showcase the application of control science methods in neuroscience research and the inspiration neuroscience provides for modern control theory and artificial intelligence.
The central theme of this issue encompasses the utilization of various interdisciplinary approaches in neuroscience research. This includes, but is not limited to, methods such as nonlinear system dynamics, control theory, artificial neural networks, machine learning, and more. Additionally, it delves into the principles of perception, behavior, neural activity, decision mechanisms, and their contributions to fields such as engineering and artificial intelligence.
This issue extensively explores the mutual promotion between control science methods and neuroscience. The topics covered include but are not limited to:
- Network Topology Analysis of Neural Networks. Investigate the structural aspects of neural networks by applying methods from control science to analyze network topology. This involves studying the connectivity patterns, clustering, and centrality of nodes within the neural network.
- Functional Connectivity Modeling: Use control science techniques to model the functional interactions between different brain regions. Explore how changes in one region's activity affect others and apply complex network and nonlinear system analysis to understand the dynamics of information flow, from the micro-scale of synoptics to macro scale of brain network.
- Adaptive Control Mechanisms: Study how adaptive control mechanisms influence the adaptability of neural networks. Investigate the ability of the brain to self-regulate and adjust its connectivity patterns based on external stimuli or changes in the environment.
- Control-Enhanced Perception and Decision Mechanisms: Investigate the integration of control science principles into the study of perception, behavior, and decision mechanisms. Explore how enhancing the controllability of neural processes influences the accuracy and efficiency of decision-making, shedding light on the dynamic interplay between control mechanisms and cognitive functions.
We hope this research topic becomes a valuable resource for researchers and practitioners in the fields of control science, neuroscience, and other interdisciplinary studies. These papers offer insights into various interdisciplinary technologies applicable to these domains, emphasizing the significance of collaboration between neuroscientists and engineers in addressing real-world challenges and overcoming research hurdles.
Keywords:
artificial neural networks, complex networks, interdisciplinary neural science, perception neuroscience
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.