Hypothesis-Driven Identification of Neural Algorithms With Dynamical Structure-Preserving Manifolds
Year: 2026
Type: preprint
Abstract: Neuroscience aims to uncover the algorithms by which the brain builds and manipulates rich internal representations of objects, agents, and places. Existing modeling frameworks have had limited success in identifying these algorithms. Here, we present Dynamical Structure-Preserving Manifolds (dSPMs), a multilevel framework that programs, without training, recurrent neural networks to enable hypothesis-driven identification of the brain’s algorithms that generate symbolic, structure-preserving representations of the world. We apply dSPM to identify an algorithm for physical scene prediction in the dorsomedial frontal cortex (DMFC) of macaques intercepting a ball on a Pong-like virtual gameboard. dSPM better explained DMFC than alternatives, made predictions confirmed in neural data, and revealed an algorithm of DMFC that we term ‘manifold-based simulation’: perceived initial conditions rapidly collapse neural activity onto a low-dimensional manifold whose geometry encodes a physics-based representation of the scene, supporting far-out predictions without traditional next-step simulation. dSPM supports effective exploration of the computational foundations of biological intelligence.
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7222719332