Computing with Living Neurons: Chaos-Controlled Reservoir Computing with Knowledge Transplant
Seung Hyun Kim, Zhi Dou, Gaurav Upadhyay, Anay Pattanaik, Leo Maslov, Lav Varshney, John Beggs, Howard Gritton, and Mattia Gazzola

TL;DR
This paper presents chaos-controlled reservoir computing with living neural cultures, enhancing adaptive computation, robustness, and longevity, and introduces Knowledge Transplant for rapid, reusable model transfer across neural substrates.
Contribution
It introduces chaos-controlled reservoir computing for neural cultures and proposes Knowledge Transplant for fast, cross-culture model sharing and transfer.
Findings
cc-RC improves accuracy and longevity by ~300% over standard RC.
Knowledge Transplant reduces training time to minutes.
Cross-substrate models enable knowledge sharing across neural populations.
Abstract
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…
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