{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,29]],"date-time":"2025-12-29T00:22:56Z","timestamp":1766967776030,"version":"3.48.0"},"reference-count":27,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2025,12,27]],"date-time":"2025-12-27T00:00:00Z","timestamp":1766793600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Vietnam Ministry of Education and Training and hosted by University of Economics Ho Chi Minh City\u2014UEH, Vietnam","award":["B2026-KSA-03"],"award-info":[{"award-number":["B2026-KSA-03"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Biomimetics"],"abstract":"<jats:p>Bio-cybernetic organisms combine biological locomotion with electronic control but face significant challenges regarding individual variability and stimulus habituation. This study introduces an Adaptive Neuro-Fuzzy Inference System (ANFIS) designed to dynamically calibrate to individual Gromphadorhina portentosa specimens. Using a miniaturized neural controller, we compared ANFIS\u2019s performance against natural behavior and non-adaptive control methods. Results demonstrate ANFIS\u2019s superiority: obstacle navigation efficiency reached 81% (compared to 42% for non-adaptive methods), and effective behavioral modulation was sustained for 47 min (versus 26 min). Furthermore, the system achieved 73% target acquisition in complex terrain and maintained stimulus responsiveness 3.5-fold longer through sophisticated habituation compensation. Biocompatibility assessments confirmed interface functionality over 14-day periods. This research establishes foundational benchmarks for arthropod bio-cybernetics, demonstrating that adaptive neuro-fuzzy architectures significantly outperform conventional methods, enabling robust bio-hybrid platforms suitable for confined-space search-and-rescue operations.<\/jats:p>","DOI":"10.3390\/biomimetics11010013","type":"journal-article","created":{"date-parts":[[2025,12,28]],"date-time":"2025-12-28T23:54:36Z","timestamp":1766966076000},"page":"13","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Mitigating Neural Habituation in Insect Bio-Bots: A Dual-Timescale Adaptive Control Approach"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-1116-3523","authenticated-orcid":false,"given":"Le Minh","family":"Triet","sequence":"first","affiliation":[{"name":"Institute of Intelligent and Interactive Technologies, University of Economics Ho Chi Minh City\u2014UEH, Ho Chi Minh City 70000, Vietnam"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0526-0736","authenticated-orcid":false,"given":"Nguyen","family":"Truong Thinh","sequence":"additional","affiliation":[{"name":"Institute of Intelligent and Interactive Technologies, University of Economics Ho Chi Minh City\u2014UEH, Ho Chi Minh City 70000, Vietnam"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,27]]},"reference":[{"key":"ref_1","unstructured":"Holzer, R., and Shimoyama, I. 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