About this Research Topic
This Research Topic aims to explore and highlight how brain circuits can be modeled as intelligent systems, building on the fundamental principles and methodologies outlined by notable proponents such as Karl Lashley, Walter Freeman, and Karl Pribram. We invite empirical and theoretical submissions that contribute to a better understanding of the brain's complex dynamics and its broad learning capabilities, together with advancements in Cognitive Optimization and Prediction (COPN), as defined in the NSF solicitation on Emerging Frontiers Research Initiation of 2007, which still presents a paradigm for new research and future directions.
Our primary focus is to foster cross-disciplinary dialogue and extend our understanding of cognition beyond the mere biological interpretation of data to a more integrated perspective with a unifying mathematical framework. Such multi-disciplinary insights have the potential to not only elucidate the workings of the brain but also inspire advancements in fields like machine learning and collective intelligence. Because of the great progress that has been made since 2007, we hope that papers in this Research Topic might demonstrate the kind of research community that would encourage revisiting this direction of research.
We welcome various types of investigations, including empirical studies, theoretical perspectives, and reviews. Among these, we particularly encourage contributions presenting novel mathematical models and algorithmic tools that can link biological data with machine learning and other intelligence-boosting strategies. Investigations into the brain's ability to maximize reinforcement and effectively predict, interact, and compensate for varied environments are also of great interest.
Through this Research Topic, we aim to push the boundaries of our current understanding of brain dynamics, exploring how these insights might be applied to foster collective intelligence at the mammalian level and beyond. We look forward to contributions that shed new light on these multifaceted topics.
Our primary focus is to foster cross-disciplinary dialogue and extend our understanding of cognition beyond the mere biological interpretation of data to a more integrated perspective with a unifying mathematical framework. Such multi-disciplinary insights have the potential to not only elucidate the workings of the brain but also inspire advancements in fields like machine learning and collective intelligence. Because of the great progress that has been made since 2007, we hope that papers in this Research Topic might demonstrate the kind of research community that would encourage revisiting this direction of research.
We welcome various types of investigations, including empirical studies, theoretical perspectives, and reviews. Among these, we particularly encourage contributions presenting novel mathematical models and algorithmic tools that can link biological data with machine learning and other intelligence-boosting strategies. Investigations into the brain's ability to maximize reinforcement and effectively predict, interact, and compensate for varied environments are also of great interest.
Through this Research Topic, we aim to push the boundaries of our current understanding of brain dynamics, exploring how these insights might be applied to foster collective intelligence at the mammalian level and beyond. We look forward to contributions that shed new light on these multifaceted topics.
Keywords: Brain Circuits, brain dynamics, Collective Intelligence, machine learning, systems 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.
