The paper studies how reservoir‑computing associative memories can reliably recall dynamical memories even when their basins of attraction are riddled and unpredictable. It finds that these basins have an octopus‑like shape, with a robust head near the attractor and thin, intertwined tentacles that span state space. By using cue‑driven generalized synchronization, the system bypasses the unpredictable tentacular regions and is driven into the robust basin head, establishing a quantitative link between cue duration, synchronization rate, and basin‑head radius.
By Ling-Wei Kong, Ying-Cheng Lai
arXiv:2608. 04028v1 Announce Type: cross Abstract: Echo-state networks enable efficient temporal learning by fixing the recurrent dynamics and training only a linear readout.
By Jyotiranjan Beuria, Amit Shukla
arXiv:2606. 24396v1 Announce Type: new Abstract: Large Transformer models function as Dense Associative Memories (DAMs), retrieving knowledge via high-dimensional attractor dynamics driven by the self-attention mechanism \citep{ramsauer2020hopfield, wu2024attention}.
By Kanishk Awadhiya
arXiv:2607. 10439v2 Announce Type: replace-cross Abstract: We model human motor cortex, recorded during rest and motor-imagery BCI conditions, as a port-Hamiltonian system: a conservative interconnection (skew-symmetric coupling between band-limited neural phasors) together with a dissipative port whose state-dependent decay is set by a graph-neural-network surrogate.
By Dibakar Sigdel
The paper introduces flux‑form spatiotemporal neural operators for predicting coarse‑grained dynamics of multiscale PDEs without relying on closure models. It learns a surrogate evolution operator from filtered high‑fidelity data, using Fourier convolution for spatial mixing and a causal kernel with time‑lag attention for temporal mixing. The method incorporates a flux‑form inductive bias to maintain conservation and provides a data‑driven rule for selecting memory length, achieving stable, accurate long‑horizon rollouts on benchmark equations and turbulent flow simulations.
By Junfeng Chen
arXiv:2607. 17858v1 Announce Type: cross Abstract: Reservoir computing exploits nonlinear dynamical systems to encode temporal inputs into high-dimensional state space representations.
By Mohab Abdalla, Damien Rontani
The paper introduces ELiSe, a model that leverages cortical network scaffolds and dendritic compartments to learn complex non‑Markovian spatio‑temporal patterns using only local, always‑on, phase‑free synaptic plasticity. It demonstrates the model’s ability to acquire and replay intricate sequences, exemplified by a birdsong learning mock‑up, and shows robustness to external disturbances and flexibility in parameter settings.
By Laura Kriener, Kristin V\"olk, Ben von H\"unerbein, Federico Benitez, Walter Senn, Mihai A. Petrovici
arXiv:2607. 24420v1 Announce Type: cross Abstract: Reservoir computing has emerged as an efficient machine learning framework for predicting time series generated by dynamical systems.
By Arthur S Powanwe
arXiv:2606. 09929v1 Announce Type: cross Abstract: Physical reservoir computing harnesses nonlinear mechanical dynamics but, by convention, freezes the substrate and trains only a linear readout, presuming the substrate is not usefully trainable.
By Caleb Munigety
arXiv:2608.30946v1 Announce Type: new
Abstract: Closed-loop human-AI systems generate high-dimensional behavioural trajectories whose collective dynamics remain obscure. Using 297,915 learners' adapt...
By Minlin Wu (Tianli Qiming AI Research Institute, Sichuan Qiming Daren Technology Co., Ltd., Chengdu, China), Xu Fang (Tianli Qiming AI Research Institute, Sichuan Qiming Daren Technology Co., Ltd., Chengdu, China), Yicheng Zhang (Swiss AI Laboratories, Blonay, Switzerland), Chenyu Zhou (Tianli Qiming AI Research Institute, Sichuan Qiming Daren Technology Co., Ltd., Chengdu, China), Zhiyi Liu (Tianli Qiming AI Research Institute, Sichuan Qiming Daren Technology Co., Ltd., Chengdu, China)
arXiv:2509. 24122v3 Announce Type: replace Abstract: At the heart of time-series forecasting (TSF) lies a fundamental challenge: how can models efficiently and effectively capture long-range temporal dependencies across ever-growing sequences?
By Hongbo Liu, Jia Xu
arXiv:2607. 18552v1 Announce Type: cross Abstract: Quantum reservoir computing (QRC) uses the dynamics of a fixed or weakly tuned quantum system to transform temporal and sequential inputs into measured features, while training is typically confined to a classical readout.
By Shehbaz Tariq, Muhammad Talha, Arshid Ali, Muhammad Diyan, Symeon Chatzinotas