Biological reservoir computing: harnessing living neurons for AI

This work demonstrates that living neuronal networks can serve as effective computational substrates within a reservoir computing framework. By leveraging the intrinsic dynamics of biological systems, we open a promising pathway towards neuromorphic architectures that combine energy efficiency, adaptability, and biological plausibility. Our experiments on static pattern recognition tasks confirm the feasibility of this approach and highlight its potential for future applications in AI and neuroscience. Notably, our preliminary study on this topic, presented at the ICCV 2025 Workshop “2nd Workshop on Human-inspired Computer Vision”, received a Best Paper Award, and subsequent work was published at ICONIP 2025, underscoring the originality and scientific relevance of this research. Moving forward, we aim to extend BRC to more complex tasks and explore learning mechanisms within the biological reservoir, paving the way for adaptive bio-hybrid system.