arXiv Machine Learning
Dynamical stability for dense patterns in attractor neural networks
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
arXiv Machine Learning
Sep 15
Random matrix theory of sparse neuronal networks with heterogeneous timescales
arXiv:2512.12767v2 Announce Type: replace-cross Abstract: Training recurrent neuronal networks consisting of excitatory (E) and inhibitory (I) units with additive noise for working memory computation...
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.
arXiv Machine Learning
Jun 4
Drift-Diffusion Matching: Embedding dynamics in latent manifolds of asymmetric neural networks
arXiv:2602. 14885v2 Announce Type: replace-cross Abstract: Recurrent neural networks (RNNs) provide a theoretical framework for understanding computation in biological neural circuits, yet classical results, such as Hopfield's model of associative memory, rely on symmetric connectivity that restricts network dynamics to gradient-like flows.
arXiv Machine Learning
Jul 2
Geometric and dynamical analysis of attractor boundaries and storage limits in kernel Hopfield networks
arXiv:2605. 00366v4 Announce Type: replace-cross Abstract: High-capacity associative memories based on Kernel Logistic Regression (KLR) exhibit strong storage capabilities, but the dynamical and geometric mechanisms underlying their stability remain poorly understood.
arXiv Machine Learning
Jun 18
On the Stability of the Jacobian Matrix in Deep Neural Networks
arXiv:2506. 08764v3 Announce Type: replace Abstract: Deep neural networks are known to suffer from exploding or vanishing gradients as depth increases, a phenomenon closely tied to the spectral behavior of the input-output Jacobian.
arXiv Machine Learning
Sep 16
Information Geometric Self-Organization at the Edge of Stability in High-Capacity Kernel Associative Memories
arXiv:2609.16827v1 Announce Type: new Abstract: High-capacity associative memories based on Kernel Logistic Regression (KLR) exhibit exceptional storage capabilities and robustness. Previous empirica...