Task-Adaptive Physical Reservoir Computing via Tunable Molecular Communication Dynamics
Diffusion-based molecular communication (MC) channels suffer from two impairments: a fading channel memory from diffusion and the saturating, nonlinear dose response of ligand receptors. These impairments have been shown to enable the channel to serve as a physical reservoir computer, with memory and nonlinearity set by channel parameters that living cells already regulate. Since computing tasks require different balances of memory and nonlinearity, this paper investigates whether these parameters can tune the MC channel to different tasks, even when only some are adjustable. Bayesian optimization of a mean-field model of the MC channel reveals that the optimal configurations of three benchmark tasks, i.e., time-series forecasting, sine-to-square transformation, and their hybrid, differ in receptor operating point, timescale, and diffusion memory. Tuning the receptor kinetics alone yields most of the error reduction of tuning all parameters, while reusing a task-specific configuration across tasks incurs a penalty despite readout retraining. A stochastic receptor model shows that receptor noise penalizes memory-demanding tasks more than threshold-like ones, and that time-averaging readouts recover much of this loss. Lastly, a synthetic glucose case study demonstrates that a single channel can be switched during operation from forecasting the glucose level to detecting threshold crossings within tens of symbols.