Department of Neurology
Biology | Computational Biology | Computational Neuroscience | Computer Sciences
The ability to define the unique features of an input stimulus needed to control switch-like behavior in biological systems is an important problem in computational biology and medicine. We show in this study how highly complex and intractable optimization problems can be simplified by restricting the search to the signal's extrema as key feature points, and evolving the extrema features towards optimal solutions that closely match solutions derived from gradient-based methods. Our results suggest a model-independent approach for solving a class of optimization problems related to controlling switch-like state transitions.
Computational models, Computational neuroscience, Dynamical systems
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Copyright © The Author(s) 2018. Open Access: This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
DOI of Published Version
Sci Rep. 2018 Feb 21;8(1):3403. doi: 10.1038/s41598-018-21761-8. Link to article on publisher's site
Chang JT, Paydarfar D. (2018). Evolution of extrema features reveals optimal stimuli for biological state transitions. Open Access Publications by UMMS Authors. https://doi.org/10.1038/s41598-018-21761-8. Retrieved from https://escholarship.umassmed.edu/oapubs/3392
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.