Continual learning improves reliability in molecular communication systems
Continual Learning for Reliable In-Sequence Molecular Communication
Machine Learning
Summary
Molecular communication is a way that tiny biological machines can send messages using molecules in a specific order. The authors developed a new technique called Affine Memory Replay that helps computers learn how well these messages are sent and received across different conditions. This method remembers past situations and creates new examples to keep improving without forgetting old knowledge. Their tests show it works better than older approaches, keeping accuracy high while adapting to new challenges.
What this means in practice
- •For bio-nanotech engineers: Evaluate and improve communication protocols between nanoscale biological devices facing varied experimental conditions.
- •For communication protocol developers: Design adaptable molecular communication systems that maintain reliable message sequencing despite changing environments.
Authors
Siddhant Setia, Junichi Suzuki, Tadashi Nakano
Abstract
This paper considers reliable in-sequence molecular communication, where bio-nanomachines exchange molecule-encoded information in a specified order, and investigates a new simulation-based method to incrementally evaluate communication protocols by gradually increasing the complexity of experimental settings. The proposed method, called Affine Memory Replay (AMR), builds a continual learning (CL) model to estimate communication performance between bio-nanomachines. AMR stores past simulation samples and synthesizes additional replay samples through interpolation and extrapolation between past samples. Through incremental performance estimation tasks, it replays both past and synthetic samples to regularize model updates, helping the model adapt to new experimental settings while retaining knowledge from earlier ones. Experimental results show that AMR significantly outperforms existing CL methods in maintaining overall estimation accuracy across sequentially learned estimation tasks. It effectively balances memory stability and learning plasticity under moderate training cost.