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A nonlinear model representing the tribological problem of a thin solid lubricant layer between two sliding periodic surfaces is used to analyze the phenomenon of hysteresis at pinning/depinning around a moving state rather than around a…

Materials Science · Physics 2009-11-13 N. Manini , G. E. Santoro , E. Tosatti , A. Vanossi

Matching animal-like flexibility in recognition and the ability to quickly incorporate new information remains difficult. Limits are yet to be adequately addressed in neural models and recognition algorithms. This work proposes a…

Computer Vision and Pattern Recognition · Computer Science 2012-06-26 Tsvi Achler

The brain combines short- and long-term memory to process, store, and recall multiple different pieces of information. Inspired by this and recent results on multifunctional and parameter-aware learning, we extend a new machine learning…

Chaotic Dynamics · Physics 2026-01-30 Daniel Köglmayr , Miralem Spahic , Andrew Flynn , Christoph Räth

Hysteresis is a ubiquitous phenomenon in science and engineering; its modeling and identification are crucial for understanding and optimizing the behavior of various systems. We develop an ordinary differential equation-based recurrent…

Consider a set of $n$ mobile entities, called robots, located and operating on a continuous circle, i.e., all robots are initially in distinct locations on a circle. The \textit{gathering} problem asks to design a distributed algorithm that…

Distributed, Parallel, and Cluster Computing · Computer Science 2023-02-16 Satakshi Ghosh , Avisek Sharma , Pritam Goswami , Buddhadeb Sau

LLMs are trained once, then deployed into a world that never stops changing. External memory compensates for this, but most systems manage it explicitly rather than letting it adapt on its own. Biological memory works differently: coupled…

Machine Learning · Computer Science 2026-05-08 Andreas Pattichis , Constantine Dovrolis

Magnetic thin films exhibit a strong variation in properties depending on their degree of disorder. Recent coherent x-ray speckle experiments on magnetic films have measured the loss of correlation between configurations at opposite fields…

Other Condensed Matter · Physics 2009-11-11 J. M. Deutsch , Trieu Mai

We examine the dynamics of a two-dimensional stripe, bubble, and crystal forming system interacting with a periodic one-dimensional substrate under an applied drive that is rotated with respect to the substrate periodicity direction $x$. We…

Soft Condensed Matter · Physics 2025-03-12 C. Reichhardt , C. J. O. Reichhardt

Initially, the logistic map became popular as a simplified model for population growth. In spite of its apparent simplicity, as the population growth-rate is increased the map exhibits a broad range of dynamics, which include bifurcation…

Adaptation and Self-Organizing Systems · Physics 2020-06-23 Caracé Gutiérrez , Cecilia Cabeza , Nicolás Rubido

A model of the columnar functional organization of neocortical association areas is studied. The neuronal network is composed of many Hebbian autoassociators, or modules, each of which interacts with a relatively small number of the others.…

Disordered Systems and Neural Networks · Physics 2020-05-14 Carlo Fulvi Mari

An intrinsic feature of disordered and out-of-equilibrium materials, such as glasses, is the dependence of their properties on their history. An important example is rheological memory, in which disordered solids obtain properties based on…

Soft Condensed Matter · Physics 2025-03-04 Yihao Chen , Simon A. Rogers , Suresh Narayanan , James L. Harden , Robert L. Leheny

Systems with many stable configurations abound in nature, both in living and inanimate matter. Their inherent nonlinearity and sensitivity to small perturbations make them challenging to study, particularly in the presence of external…

Adaptation and Self-Organizing Systems · Physics 2023-12-12 Hridesh Kedia , Deng Pan , Jean-Jacques Slotine , Jeremy L. England

Spin-glass models of associative memories are a cornerstone between statistical physics and theoretical neuroscience. In these networks, stochastic spin-like units interact through a synaptic matrix shaped by local Hebbian learning. In…

Disordered Systems and Neural Networks · Physics 2025-04-08 Gianni V. Vinci , Andrea Galluzzi , Maurizio Mattia

Disordered many-body systems exhibit a wide range of emergent phenomena across different scales. These complex behaviors can be utilized for various information processing tasks such as error correction, learning, and optimization. Despite…

Disordered Systems and Neural Networks · Physics 2023-08-04 Weishun Zhong

Complex behaviors often sit at a critical threshold between order and disorder. But not all disorder is created equal. Disorder can be trivial or constrained, and correlated disorder can even be topological. Crucially, constrained disorder…

Statistical Mechanics · Physics 2025-12-29 Priyanka Priyanka , Cristiano Nisoli , Yair Shokef

Classical open systems with balanced gain and loss, i.e. parity-time ($\mathcal{PT}$) symmetric systems, have attracted tremendous attention over the past decade. Their exotic properties arise from exceptional point (EP) degeneracies of…

Quantum Physics · Physics 2021-02-17 Zachary A. Cochran , Avadh Saxena , Yogesh N. Joglekar

Hysteresis dynamics has been described in a vast number of biological experimental studies. Many such studies are phenomenological and a mathematical appreciation has not attracted enough attention. In the paper, we explore the nature of…

Adaptation and Self-Organizing Systems · Physics 2021-03-02 Liang Chen , Sue Ann Campbell

A simple one-dimensional spring-block chain with asymmetric interactions is considered to model an idealized single-lane highway traffic. The main elements of the system are blocks (modeling cars), springs with unidirectional interactions…

Data Analysis, Statistics and Probability · Physics 2011-11-04 Ferenc Jarai-Szabo , Bulcsu Sandor , Zoltan Neda

This work focuses on the training dynamics of one associative memory module storing outer products of token embeddings. We reduce this problem to the study of a system of particles, which interact according to properties of the data…

Machine Learning · Computer Science 2024-03-01 Vivien Cabannes , Berfin Simsek , Alberto Bietti

Griffiths phases are typically associated with quenched disorder, while frustration gives rise to multistability and spin-glass behavior. Whether extended criticality can arise in other contexts remains an open question. Here, we show that…

Disordered Systems and Neural Networks · Physics 2026-05-15 Lorenzo Lucarini , Sandro Meloni , Pablo Villegas