[2509.01799] Optimal information injection and transfer mechanisms for active matter reservoir computing

Nonlinear Sciences > Adaptation and Self-Organizing Systems arXiv:2509.01799 (nlin) [Submitted on 1 Sep 2025 (v1), last revised 2 Mar 2026 (this version, v2)] Title:Optimal information injection and transfer mechanisms for active matter reservoir computing Authors:Mario U. Gaimann, Miriam Klopotek View a PDF of the paper titled Optimal information injection and transfer mechanisms for active matter reservoir computing, by Mario U. Gaimann and Miriam Klopotek View PDF Abstract:Reservoir computing (RC) is a state-of-the-art machine learning method that makes use of the power of dynamical systems (the reservoir) for real-time inference. When using biological complex systems as reservoir substrates, it serves as a testbed for basic questions about bio-inspired computation -- of how self-organization generates proper spatiotemporal patterning. Here, we use a simulation of an active matter system, driven by a chaotically moving input signal, as a reservoir. So far, it has been unclear whether such complex systems possess the capacity to process information efficiently and independently of the method by which it was introduced. We find that when switching from a repulsive to an attractive driving force, the system completely changes the way it computes, while the predictive performance landscapes remain nearly identical. The nonlinearity of the driver's injection force improves computation by decoupling the single-agent dynamics from that of the driver. Triggered are the (re-)gro...