Memory engine: Self-organized coherence from internal feedback

Titelangaben

Sarkar, Aranyak:
Memory engine: Self-organized coherence from internal feedback.
In: Physical Review E. Bd. 112 (2025) . - 054111.
ISSN 2470-0053
DOI: https://doi.org/10.1103/t7nk-4p57

Volltext

Link zum Volltext (externe URL):

Angaben zu Projekten

Abstract

We present a continuous-space realization of the coupled memory graph process, a minimal non-Markovian framework in which coherence emerges through internal feedback. A single Brownian particle evolves on a viscoelastic substrate that records its trajectory as a scalar memory field and exerts local forces via the gradient of accumulated imprints. This autonomous, closed-loop dynamics generates structured, phase-locked motion without external forcing. The system is governed by coupled integro-differential equations: the memory field evolves as a spatiotemporal convolution of the particle's path, while its velocity responds to the gradient of this evolving field. Simulations reveal a sharp transition from unstructured diffusion to coherent burst-trap cycles, controlled by substrate stiffness and marked by multimodal speed distributions, directional locking, and spectral entrainment. This coherence point aligns across three axes: (i) saturation of memory energy, (ii) peak transfer entropy, and (iii) a bifurcation in transverse stability. We interpret this as the emergence of a memory engine—a self-organizing mechanism converting stored memory into predictive motion—illustrating that coherence arises not from tuning, but from coupling.