Max Planck Institute for Dynamics and Self-Organisation, Göttingen 37077, Germany.
Center for Protein Assemblies (CPA), Physik-Department, Technische Universität München, Garching 85748, Germany.
Phys Rev Lett. 2022 Jul 8;129(2):028101. doi: 10.1103/PhysRevLett.129.028101.
The continuous adaptation of networks like our vasculature ensures optimal network performance when challenged with changing loads. Here, we show that adaptation dynamics allow a network to memorize the position of an applied load within its network morphology. We identify that the irreversible dynamics of vanishing network links encode memory. Our analytical theory successfully predicts the role of all system parameters during memory formation, including parameter values which prevent memory formation. We thus provide analytical insight on the theory of memory formation in disordered systems.
网络（如我们的脉管系统）的持续适应确保了在面临不断变化的负载时网络具有最佳的性能。在这里，我们表明，适应动力学使网络能够记住施加的负载在其网络形态中的位置。我们发现，消失的网络链接的不可逆动力学编码了记忆。我们的分析理论成功地预测了记忆形成过程中所有系统参数的作用，包括阻止记忆形成的参数值。因此，我们为无序系统中记忆形成的理论提供了分析见解。
