arXiv Machine Learning

Basin Geometry and Reliable Recall of Dynamical Memories in Reservoir Computing

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.

arXiv AI

6d ago

ELiSe: Efficient Learning of Sequences in Structured Recurrent Networks

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 AI

Sep 15

Feasibility and Memory Mechanisms of Chern-Simons Context Reservoir Computation

arXiv:2609.13315v1 Announce Type: cross Abstract: We investigate whether a Chern-Simons (CS) context reservoir is a viable computational substrate and whether evolving its gauge connection provides a...

By Jyotiranjan Beuria, Venkatesh H. Chembrolu

arXiv AI

Aug 6

Lindblad-Inspired Multi-Timescale Reservoir Computing with Separable Rotation and Dissipation

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

benchmarks

arXiv Machine Learning

Jun 24

Parallel Manifold Steering: Efficient Adaptation of Large Associative Memories via Residual Energy Shaping

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

llms fine-tuning

arXiv Machine Learning

Jul 28

Frequency-Based Reservoir computing

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 AI

Jun 10

Between Amnesia and Chaos: A Memory Stability Expressivity Trilemma for Trainable Dissipative Oscillator Networks

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 AI

Aug 11

Deep Residual Echo State Networks: exploring residual orthogonal connections in untrained Recurrent Neural Networks

arXiv:2508. 21172v3 Announce Type: replace-cross Abstract: Echo State Networks (ESNs) are a particular type of untrained Recurrent Neural Networks (RNNs) within the Reservoir Computing (RC) framework, popular for their fast and efficient learning.

By Matteo Pinna, Andrea Ceni, Claudio Gallicchio

arXiv Machine Learning

Jun 5

Unraveling the Hidden Dynamical Structure in Recurrent Neural Policies

arXiv:2602. 01196v2 Announce Type: replace Abstract: Recurrent neural policies are widely used in partially observable control and meta-RL tasks.

By Jin Li, Yue Wu, Mengsha Huang, Yuhao Sun, Hao He, Xianyuan Zhan

reinforcement-learning

arXiv Machine Learning

Jul 21

Beyond the Edge of Chaos: Stability-Expressivity Transfer in Reservoir Forecasting

arXiv:2607. 17909v1 Announce Type: cross Abstract: The edge-of-chaos heuristic has long served as a guiding principle for designing reservoir computers, yet its relevance to machine performance remains elusive.

By Yao Du, Xingang Wang

agents

arXiv Machine Learning

Jul 21

Organization of computation in reservoir computing

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

arXiv Machine Learning

Aug 6

Echo Flow Networks

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

benchmarks

arXiv AI

Aug 18

PhyxMamba: Chaotic System Reconstruction from Short Context Observations with Generative State-Space Models

arXiv:2505. 23863v3 Announce Type: replace-cross Abstract: Understanding chaotic dynamics is a fundamental problem across scientific disciplines, including climate science, neuroscience, and fluid dynamics, yet direct experimentation and intervention in such systems are often infeasible.

By Chang Liu, Bohao Zhao, Jingtao Ding, Huandong Wang, Yong Li

rag

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