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URN to cite this document: urn:nbn:de:bvb:703-epub-9179-2
Title data
Sarkar, Aranyak:
Memory engine : Self-organized coherence from internal feedback.
In: Physical Review E.
Vol. 112
(2025)
.
- 054111.
ISSN 2470-0053
DOI der Verlagsversion: https://doi.org/10.1103/t7nk-4p57
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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.
Further data
| Item Type: | Article in a journal |
|---|---|
| DDC Subjects: | 500 Science > 530 Physics |
| Institutions of the University: | Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Physics > Chair Experimental Physics I - Physics of Living Matter Faculties Faculties > Faculty of Mathematics, Physics und Computer Science Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Physics |
| Language: | English |
| Originates at UBT: | Yes |
| URN: | urn:nbn:de:bvb:703-epub-9179-2 |
| Date Deposited: | 11 May 2026 10:26 |
| Last Modified: | 11 May 2026 10:27 |
| URI: | https://epub.uni-bayreuth.de/id/eprint/9179 |
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