AI Article Synopsis
- Modifying neural activity is crucial for understanding brain functions and developing medical therapies, but there's a gap in the literature discussing neurobiological models from a formal control systems perspective.
- Existing control solutions are often empirical, lacking rigorous theoretical frameworks, which limits their effectiveness in applying to neurobiological systems.
- This paper aims to survey and analyze dynamical neurobiological models for potential closed-loop control schemes, providing a comprehensive guide for future discussions and research in control methodologies for neural behavior modification.
Article Abstract
Modifying neural activity is a substantial goal in neuroscience that facilitates the understanding of brain functions and the development of medical therapies. Neurobiological models play an essential role, contributing to the understanding of the underlying brain dynamics. In this context, control systems represent a fundamental tool to provide a correct articulation between model stimulus (system inputs) and outcomes (system outputs). However, throughout the literature there is a lack of discussions on neurobiological models, from the formal control perspective. In general, existing control proposals applied to this family of systems, are developed empirically, without theoretical and rigorous framework. Thus, the existing control solutions, present clear and significant limitations. The focus of this work is to survey dynamical neurobiological models that could serve for closed-loop control schemes or for simulation analysis. Consequently, this paper provides a comprehensive guide to discuss and analyze control-oriented neurobiological models. It also provides a potential framework to adequately tackle control problems that could modify the behavior of single neurons or networks. Thus, this study constitutes a key element in the upcoming discussions and studies regarding control methodologies applied to neurobiological systems, to extend the present research and understanding horizon for this field.
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| http://dx.doi.org/10.1109/RBME.2022.3180559 | DOI Listing |
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