Computing with Living Neurons: Chaos-Controlled Reservoir Computing with Knowledge Transplant
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arXiv
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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866914442045816832 |
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| author | Kim, Seung Hyun Dou, Zhi Upadhyay, Gaurav Pattanaik, Anay Maslov, Leo Varshney, Lav Beggs, John Gritton, Howard Gazzola, Mattia |
| author_facet | Kim, Seung Hyun Dou, Zhi Upadhyay, Gaurav Pattanaik, Anay Maslov, Leo Varshney, Lav Beggs, John Gritton, Howard Gazzola, Mattia |
| contents | We introduce chaos-controlled Reservoir Computing (cc-RC) for living neural cultures: dynamically rich substrates of unique potential for adaptive computation. To account for intrinsic biological variability, cc-RC combines: (i) pre-training identification of each culture's dynamical signature and phase-portrait attractor; (ii) low-power optical chaos control to stabilize spontaneous and stimulus-evoked activity; (iii) readout training within this controlled regime. Across hundreds of neural samples, cc-RC enables robust learning and pattern classification, improving both accuracy and model longevity by approximately 300% over standard RC. We further propose Knowledge Transplant (KT), for which the reservoir map learned by an expert culture is transplanted to an attractor-equivalent student culture, reducing training time to minutes while improving performance. By enabling cross-substrate, reusable learned models, KT paves the way for knowledge accumulation and sharing across neural populations, transcending biological lifespan limits. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_02552 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Computing with Living Neurons: Chaos-Controlled Reservoir Computing with Knowledge Transplant Kim, Seung Hyun Dou, Zhi Upadhyay, Gaurav Pattanaik, Anay Maslov, Leo Varshney, Lav Beggs, John Gritton, Howard Gazzola, Mattia Neural and Evolutionary Computing Emerging Technologies We introduce chaos-controlled Reservoir Computing (cc-RC) for living neural cultures: dynamically rich substrates of unique potential for adaptive computation. To account for intrinsic biological variability, cc-RC combines: (i) pre-training identification of each culture's dynamical signature and phase-portrait attractor; (ii) low-power optical chaos control to stabilize spontaneous and stimulus-evoked activity; (iii) readout training within this controlled regime. Across hundreds of neural samples, cc-RC enables robust learning and pattern classification, improving both accuracy and model longevity by approximately 300% over standard RC. We further propose Knowledge Transplant (KT), for which the reservoir map learned by an expert culture is transplanted to an attractor-equivalent student culture, reducing training time to minutes while improving performance. By enabling cross-substrate, reusable learned models, KT paves the way for knowledge accumulation and sharing across neural populations, transcending biological lifespan limits. |
| title | Computing with Living Neurons: Chaos-Controlled Reservoir Computing with Knowledge Transplant |
| topic | Neural and Evolutionary Computing Emerging Technologies |
| url | https://arxiv.org/abs/2604.02552 |