← Back to all news
arXiv AI By Dibakar Sigdel

Learning the Brain's Dynamics as a Port-Hamiltonian System: A GNN-Surrogate Metriplectic Twin for Non-Equilibrium Cortical Dynamics and Closed-Loop Neuromodulation

Read the original on arXiv AI →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

Related stories

arXiv AI
Aug 10

A Physics-Inspired Classical Digital Twin of Cortical Dynamics: A Band-Stratified Metriplectic Port-Hamiltonian Neural Network Learned from Brain-Computer-Interface EEG

arXiv:2607. 10439v3 Announce Type: replace-cross Abstract: We present a physics-inspired classical digital twin of brain-computer- interface (BCI) data: a graph neural network constrained to a band-stratified, metriplectic port-Hamiltonian form, with parameters learned from scalp EEG recorded during rest and motor imagery.

By Dibakar Sigdel
More like this →
arXiv AI
Jul 14

Learning the Brain's Dynamics as a Port-Hamiltonian System

arXiv:2607. 10439v1 Announce Type: cross Abstract: We model human motor cortex during a wrist-extension BCI task as a port-Hamiltonian system (pHS): a conservative interconnection (gyroscopic coupling between neural phasors) plus a dissipative port (power-law energy decay driven by a GNN surrogate).

By Dibakar Sigdel
More like this →
arXiv Machine Learning
Jun 2

Torus Graphs for Large Scale Neural Phase Analysis

arXiv:2606. 00496v1 Announce Type: new Abstract: Oscillatory neural signals such as electroencephalography (EEG) and local field potentials (LFPs) show phase relationships that coordinate communication across brain regions.

By Jack Goffinet, Casey Hanks, David E. Carlson
More like this →
arXiv Machine Learning
Sep 25

Orbital Error Dynamics: Self-Organized Criticality, Ephemeral Parameter Resonance, and Non-Linear Biological Ontologies in Zero-Storage Neural Synthesis

The paper introduces Orbital Error Dynamics (OED), an analytical framework that reinterprets neural network weights as transient topological resonances rather than static matrices, derived from a complex quadratic polynomial map. It proposes the Bent Sine Wave Hypothesis to explain non‑equilibrium living systems, defines Observer Horizon Geometry in parameter space, and presents a heavy‑tailed Biomimetic Perturbed Jump Operator inspired by biological processes. Empirical tests on the Two‑Moons manifold show that procedural parameterization from a 24‑byte seed yields competitive accuracy compared to a conventional gradient baseline, while also aligning conceptually with an analog optical co‑processor.

By Volkan Da\u{g}l{\i}, Zerrin Da\u{g}l{\i}, Da\u{g}han Da\u{g}l{\i}
More like this →