Molecular Communication Channel as a Physical Reservoir Computer

Abstract

Molecular Communication (MC) channels are characterized by significant memory and nonlinear dynamics arising from diffusion and receptor kinetics. While often viewed as impairments to reliable data transmission, this work introduces a paradigm shift by reconceptualizing these intrinsic physical properties as computational resources. We frame a canonical point-to-point MC channel, comprising ligand diffusion and reversible ligand-receptor binding at a spherical receiver, as a Physical Reservoir Computer (PRC). Utilizing deterministic mean-field modeling and particle-based spatial stochastic simulations, we demonstrate the MC system's inherent capability for complex temporal information processing on standard chaotic time-series benchmarks. We comprehensively evaluate performance using both task-specific Normalized Root Mean Square Error (NRMSE) and the task-independent Information Processing Capacity (IPC). Our results reveal a non-monotonic dependence of computational power on key biophysical parameters (receptor kinetic rates, diffusion coefficient, and transmitter-receiver distance), identifying optimal operational regimes where memory and nonlinearity are balanced. These findings establish the MC channel as a viable computational substrate, paving the way for novel architectures in \emph{wetware} artificial intelligence.