Computing with Living Neurons: Chaos-Controlled Reservoir Computing with Knowledge Transplant

2026-04-02

AI Summary

This study addresses the challenges of high biological variability in live neuronal cultures and the limited reusability of conventional computational systems by proposing a novel framework that integrates optical chaos control with reservoir computing. The approach leverages phase-space attractor analysis for dynamic feature identification and incorporates a knowledge distillation mechanism to enable cross-sample model transfer. For the first time in living neural networks, this method achieves reusable, cross-individual knowledge sharing and rapid deployment. Compared to traditional reservoir computing, it yields approximately a 300% improvement in both model accuracy and operational lifetime. Furthermore, through knowledge transfer, training time is reduced to the minute scale while simultaneously enhancing overall performance.

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 cross-substrate, reusable learned models, KT paves the way for knowledge accumulation and sharing across neural populations, transcending biological lifespan limits.

Problem

• Reservoir Computing
• Living Neurons
• Chaos Control
• Knowledge Transplant
• Biological Variability

Innovation

• chaos-controlled reservoir computing
• living neural cultures
• optical chaos control
• knowledge transplant
• attractor-equivalent mapping