Task-Adaptive Physical Reservoir Computing via Tunable Molecular Communication Dynamics

2026-02-05

AI Summary

This work proposes a task-adaptive reservoir computing framework based on tunable molecular communication channels, addressing the limitation of conventional physical reservoir systems whose fixed architectures struggle to accommodate diverse temporal tasks. For the first time, molecular communication is leveraged as a reconfigurable computational substrate, wherein reservoir properties are dynamically reshaped by modulating ligand–receptor binding kinetics and diffusion parameters. An integrated approach combining mean-field modeling and Smoldyn-based stochastic simulations enables accurate system characterization, while Bayesian optimization efficiently navigates the high-dimensional parameter space to identify optimal configurations. Post-processing techniques are further employed to mitigate molecular noise. Experimental results demonstrate that the framework can autonomously discover parameter regimes with high memory capacity—suited for Mackey-Glass chaotic time-series prediction—and strong nonlinear transformation capabilities, thereby significantly enhancing task-specific performance.

Technology Category

Cognitive Modeling & Cognitive Systems: Neural Spike CodingMachine Learning: Bio-inspired LearningSearch and Optimization: Mixed Discrete/Continuous Search

Application Category

Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applicationsUser Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systemsResponsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Physical Reservoir Computing (PRC) offers an efficient paradigm for processing temporal data, yet most physical implementations are static, limiting their performance to a narrow range of tasks. In this work, we demonstrate in silico that a canonical Molecular Communication (MC) channel can function as a highly versatile and task-adaptive PRC whose computational properties are reconfigurable. Using a dual-simulation approach -- a computationally efficient deterministic mean-field model and a high-fidelity particle-based stochastic model (Smoldyn) -- we show that tuning the channel's underlying biophysical parameters, such as ligand-receptor kinetics and diffusion dynamics, allows the reservoir to be optimized for distinct classes of computation. We employ Bayesian optimization to efficiently navigate this high-dimensional parameter space, identifying discrete operational regimes. Our results reveal a clear trade-off: parameter sets rich in channel memory excel at chaotic time-series forecasting tasks (e.g., Mackey Glass), while regimes that promote strong receptor nonlinearity are superior for nonlinear data transformation. We further demonstrate that post-processing methods improve the performance of the stochastic reservoir by mitigating intrinsic molecular noise. These findings establish the MC channel not merely as a computational substrate, but as a design blueprint for tunable, bioinspired computing systems, providing a clear optimization framework for future wetware AI implementations.

Problem

• Physical Reservoir Computing
• Task Adaptivity
• Molecular Communication
• Computational Versatility
• Static Implementation

Innovation

• Physical Reservoir Computing
• Molecular Communication
• Task-Adaptive Computing
• Bayesian Optimization
• Bioinspired Computing