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.

翻译：我们提出了用于活体神经培养物的混沌控制储备池计算（cc-RC）——一种具有独特自适应计算潜力的高动态丰富基板。为处理内在生物变异性，cc-RC结合了：（i）预训练阶段识别每种培养物的动力学特征和相空间吸引子；（ii）低功率光学混沌控制，以稳定自发和刺激诱发的活动；（iii）在此受控状态下的读出层训练。在数百个神经样本的实验中，cc-RC实现了鲁棒的学习和模式分类，与标准储备池计算相比，准确率与模型寿命均提升约300%。我们进一步提出知识移植（KT）技术，将专家培养物学得的储备映射移植到吸引子等价的学生培养物上，使训练时间缩短至分钟级同时提升性能。通过实现跨基板的可复用学习模型，KT为跨越神经群体间的知识积累与共享铺平道路，从而突破生物寿命限制。