Hypothesis-Driven Identification of Neural Algorithms With Dynamical Structure-Preserving Manifolds.

Abstract

Neuroscience aims to uncover the algorithms by which the brain builds and manipulates rich internal representations of objects, agents, and places. Existing reverse-engineering frameworks remain ineffective for identifying these algorithms. Here, we present Dynamical Structure-Preserving Manifolds (dSPMs)---a novel multilevel reverse-engineering 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 around. 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.