Molecular Communication Channel as a Physical Reservoir Computer

2025-04-23

AI Summary

Molecular communication (MC) channels exhibit strong memory and nonlinearity due to diffusion and receptor binding kinetics, traditionally regarded as detrimental to reliable information transmission. Method: This work reconceives the MC channel as a physical reservoir computer (PRC), directly harnessing its intrinsic temporal binding dynamics as high-dimensional virtual nodes; only a linear ridge regression readout is required for chaotic time-series prediction—eliminating conventional complex coding/decoding schemes. Contribution/Results: Validated via mean-field modeling and particle-level simulations, the approach achieves significantly lower single-step normalized root-mean-square error (NRMSE) than baselines on Mackey–Glass and NARMA10 benchmarks. It reveals a non-monotonic dependence of performance on system parameters and identifies an optimal operational regime. This demonstrates the viability of low-complexity, biologically inspired physical computing, leveraging the channel’s native biophysical properties *in situ* as computational resources.

Technology Category

Machine Learning: Bio-inspired LearningCognitive Modeling & Cognitive Systems: Neural Spike CodingMultiagent Systems: Agent Communication

Application Category

Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystemsUser Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systemsSystems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Molecular Communication (MC) channels inherently possess significant memory and nonlinear dynamics due to diffusion and receptor kinetics, often posing challenges for reliable data transmission. This work reconceptualizes these intrinsic properties as computational resources by framing a canonical point-to-point MC channel, consisting of ligand diffusion and reversible ligand-receptor binding at a spherical receiver, as a physical reservoir computer (PRC). We utilize the time-varying fraction of bound receptors as the reservoir's internal state, employing time-multiplexing to generate high-dimensional virtual nodes without explicit recurrence. Only a linear readout layer is trained via ridge regression. Through deterministic mean-field modeling and particle-based spatial stochastic simulations, we demonstrate the MC system's capability for complex temporal processing by successfully performing next-step prediction on standard chaotic time-series benchmarks (Mackey-Glass and NARMA10). Performance, quantified by Normalized Root Mean Square Error (NRMSE), exhibits a non-monotonic dependence on key system parameters (receptor kinetic rates, diffusion coefficient, transmitter-receiver distance), revealing optimal operational regimes. These findings validate the potential of using MC channel as effective and low-complexity computational substrate.

Problem

• Utilizing molecular communication channels for reservoir computing
• Transforming diffusion and receptor kinetics into computational resources
• Achieving complex temporal processing with low-complexity linear readout

Innovation

• Utilizes molecular communication channel as reservoir computer
• Employs time-multiplexing for high-dimensional virtual nodes
• Trains only linear readout layer via ridge regression