Chemical reservoir computation in a self-organizing reaction network

• 成果类型: Article
• 署名作者: Baltussen, Mathieu G.; de Jong, Thijs J.; Duez, Quentin; Robinson, William E.; Huck, Wilhelm T. S.
• 署名单位: Radboud University Nijmegen
• 刊物名称: Nature
• ISSN/ISSBN: 0028-3923
• DOI: 10.1038/s41586-024-07567-x
• 发表日期: 2024-07-18
• 关键词: neural-network; systems; logic; physics; learns

摘要

Chemical reaction networks, such as those found in metabolism and signalling pathways, enable cells to process information from their environment1,2. Current approaches to molecular information processing and computation typically pursue digital computation models and require extensive molecular-level engineering3. Despite considerable advances, these approaches have not reached the level of information processing capabilities seen in living systems. Here we report on the discovery and implementation of a chemical reservoir computer based on the formose reaction4. We demonstrate how this complex, self-organizing chemical reaction network can perform several nonlinear classification tasks in parallel, predict the dynamics of other complex systems and achieve time-series forecasting. This in chemico information processing system provides proof of principle for the emergent computational capabilities of complex chemical reaction networks, paving the way for a new class of biomimetic information processing systems. A chemical reservoir computer based on the formose reaction has been discovered that can perform several nonlinear classification tasks in parallel, predict the dynamics of other complex systems and achieve time-series forecasting.