细胞样活性系统中的涌现记忆
Emergent memory in cell-like active systems
AI总结：
本研究提出了一种新的活性粒子模型，通过引入内部状态动态和环境感知，揭示了细胞样系统中环境记忆的自发涌现及其对集体行为的影响。
摘要：
在不同尺度上，从分子机器到人类人群的活性系统通常被建模为由内部生成力驱动的自推进粒子集合。然而，这些模型通常假设无记忆动态且没有内部活性力与环境的耦合。受活细胞近期展示的多时间尺度记忆效应的启发，我们引入了一个通用的理论框架，通过将内部状态动态和环境感知纳入活性粒子模型中，超越了这一范式。我们展示当代理人的自推进依赖于具有自身复杂动态的内部变量——这些变量受局部环境提示调节时，环境记忆会自发出现，并导致新的行为类别。这些包括记忆诱导响应、复杂景观中的可适应局部化、抑制运动诱导相分离以及增强的阻塞转变。我们的结果表明，非平衡体具有内部状态的最小信息处理能力，如活细胞，可以深刻影响个体和集体行为。该框架连接了细胞尺度的活动和大尺度的细胞集合智能运动，并为从合成胶体到生物集体和机器人群的系统提供了定量分析和设计的途径。
英文摘要：
Active systems across scales, ranging from molecular machines to human crowds, are usually modeled as assemblies of self-propelled particles driven by internally generated forces. However, these models often assume memoryless dynamics and no coupling of internal active forces to the environment. Here, guided by the example of living cells, which have recently been shown to display multi-timescale memory effects, we introduce a general theoretical framework that goes beyond this paradigm by incorporating internal state dynamics and environmental sensing into active particle models. We show that when the self-propulsion of an agent depends on internal variables with their own complex dynamics - modulated by local environmental cues - environmental memory spontaneously emerges and gives rise to new classes of behaviours. These include memory-induced responses, adaptable localization in complex landscapes, suppression of motility-induced phase separation, and enhanced jamming transitions. Our results demonstrate how minimal information processing capabilities, intrinsic to non-equilibrium agents with internal states like living cells, can profoundly influence both individual and collective behaviours. This framework bridges cell-scale activity and large-scale intelligent motion in cell assemblies, and opens the way to the quantitative analysis and design of systems ranging from synthetic colloids to biological collectives and robotic swarms.
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。
