Understanding collective behavior in biological systems through potential field mechanisms
Abstract Collective behavior in biological systems emerges from local interactions among individuals, enabling groups to adapt to dynamic environments. Traditional modeling approaches, such as bottom-up and top-down models, have limitations in accurately representing these complex interactions. We p...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-88440-3 |
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| author | Junqiao Zhang Qiang Qu Xuebo Chen |
| author_facet | Junqiao Zhang Qiang Qu Xuebo Chen |
| author_sort | Junqiao Zhang |
| collection | DOAJ |
| description | Abstract Collective behavior in biological systems emerges from local interactions among individuals, enabling groups to adapt to dynamic environments. Traditional modeling approaches, such as bottom-up and top-down models, have limitations in accurately representing these complex interactions. We propose a novel potential field mechanism that integrates local interactions and environmental influences to explain collective behavior. This study introduces dynamic potential fields, where individuals perceive and respond to local potential fields generated by environmental cues and other individuals. We develop a mathematical framework combining distributed learning and swarm control to simulate and analyze collective behavior under varying conditions. Our simulations span a variety of environmental conditions, including standard environments where organisms interact under typical conditions, high noise environments where interactions are disrupted by random fluctuations, high density environments with increased competition for space, high risk environments featuring areas of strong negative potential field, and multiple resource environments with varying degrees of resource availability. These simulations demonstrate the adaptability and resilience of biological groups to changing and challenging conditions. Results reveal how potential fields facilitate the emergence of stable and coordinated behaviors, providing insights into self-organization, cooperation, and competition in nature. This framework enhances our understanding of collective behavior and has implications for bio-robotics, distributed systems, and complex networks. |
| format | Article |
| id | doaj-art-dcace86d528b417daff74431cbc0522e |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-dcace86d528b417daff74431cbc0522e2025-02-02T12:17:31ZengNature PortfolioScientific Reports2045-23222025-01-0115112210.1038/s41598-025-88440-3Understanding collective behavior in biological systems through potential field mechanismsJunqiao Zhang0Qiang Qu1Xuebo Chen2School of Electronics and Information Engineering, University of Science and Technology LiaoningSchool of Electronics and Information Engineering, University of Science and Technology LiaoningSchool of Electronics and Information Engineering, University of Science and Technology LiaoningAbstract Collective behavior in biological systems emerges from local interactions among individuals, enabling groups to adapt to dynamic environments. Traditional modeling approaches, such as bottom-up and top-down models, have limitations in accurately representing these complex interactions. We propose a novel potential field mechanism that integrates local interactions and environmental influences to explain collective behavior. This study introduces dynamic potential fields, where individuals perceive and respond to local potential fields generated by environmental cues and other individuals. We develop a mathematical framework combining distributed learning and swarm control to simulate and analyze collective behavior under varying conditions. Our simulations span a variety of environmental conditions, including standard environments where organisms interact under typical conditions, high noise environments where interactions are disrupted by random fluctuations, high density environments with increased competition for space, high risk environments featuring areas of strong negative potential field, and multiple resource environments with varying degrees of resource availability. These simulations demonstrate the adaptability and resilience of biological groups to changing and challenging conditions. Results reveal how potential fields facilitate the emergence of stable and coordinated behaviors, providing insights into self-organization, cooperation, and competition in nature. This framework enhances our understanding of collective behavior and has implications for bio-robotics, distributed systems, and complex networks.https://doi.org/10.1038/s41598-025-88440-3Collective behaviorBiological systemsPotential fieldIndividual interactionsDistributed learning |
| spellingShingle | Junqiao Zhang Qiang Qu Xuebo Chen Understanding collective behavior in biological systems through potential field mechanisms Scientific Reports Collective behavior Biological systems Potential field Individual interactions Distributed learning |
| title | Understanding collective behavior in biological systems through potential field mechanisms |
| title_full | Understanding collective behavior in biological systems through potential field mechanisms |
| title_fullStr | Understanding collective behavior in biological systems through potential field mechanisms |
| title_full_unstemmed | Understanding collective behavior in biological systems through potential field mechanisms |
| title_short | Understanding collective behavior in biological systems through potential field mechanisms |
| title_sort | understanding collective behavior in biological systems through potential field mechanisms |
| topic | Collective behavior Biological systems Potential field Individual interactions Distributed learning |
| url | https://doi.org/10.1038/s41598-025-88440-3 |
| work_keys_str_mv | AT junqiaozhang understandingcollectivebehaviorinbiologicalsystemsthroughpotentialfieldmechanisms AT qiangqu understandingcollectivebehaviorinbiologicalsystemsthroughpotentialfieldmechanisms AT xuebochen understandingcollectivebehaviorinbiologicalsystemsthroughpotentialfieldmechanisms |