任务自适应的可调分子通信动态物理共振计算
Task-Adaptive Physical Reservoir Computing via Tunable Molecular Communication Dynamics
AI总结：
本文提出了一种基于可调分子通信动态的物理共振计算方法，通过双模拟方法和贝叶斯优化，实现对不同计算任务的自适应优化，展示了在混沌时间序列预测和非线性数据转换中的性能提升。
摘要：
物理共振计算（PRC）提供了一种高效的处理时间序列数据的范式，但大多数物理实现都是静态的，限制了其在狭窄任务范围内的性能。在本文中，我们通过计算机模拟证明，一个经典的分子通信（MC）通道可以作为高度灵活且任务自适应的PRC，其计算特性具有可重新配置性。使用双模拟方法——一个计算效率高的确定性平均场模型和一个高保真度的粒子基随机模型（Smoldyn）——我们展示，通过调整通道的生物物理参数，如配体-受体动力学和扩散动力学，可以将共振器优化以适应不同的计算类别。我们采用贝叶斯优化高效地导航这个高维参数空间，识别出离散的操作模式。我们的结果揭示了一个明显的权衡：参数集富含通道记忆的在混沌时间序列预测任务（如Mackey Glass）中表现优异，而促进强受体非线性的模式在非线性数据转换中更优。我们进一步证明，后处理方法通过减轻内在分子噪声提高了随机共振器的性能。这些发现使MC通道不仅成为计算基质，而且成为可调、生物启发计算系统的设计蓝图，为未来湿实验室AI实现提供了清晰的优化框架。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
