Trainability, Expressivity and Interpretability in Gated Neural ODEs

TL;DR

This paper introduces gated neural ODEs (gnODEs) with adaptive timescales, demonstrating their enhanced memory, interpretability, and expressivity in modeling complex dynamics and real-world tasks.

Contribution

The paper extends neural ODEs with gating mechanisms for adaptive timescales, improving interpretability and expressivity while maintaining modeling power.

Findings

gnODEs effectively learn continuous attractors for memory tasks.

Reduced-dimensional gnODEs remain powerful and more interpretable.

Gating improves performance on real-world dynamical tasks.

Abstract

Understanding how the dynamics in biological and artificial neural networks implement the computations required for a task is a salient open question in machine learning and neuroscience. In particular, computations requiring complex memory storage and retrieval pose a significant challenge for these networks to implement or learn. Recently, a family of models described by neural ordinary differential equations (nODEs) has emerged as powerful dynamical neural network models capable of capturing complex dynamics. Here, we extend nODEs by endowing them with adaptive timescales using gating interactions. We refer to these as gated neural ODEs (gnODEs). Using a task that requires memory of continuous quantities, we demonstrate the inductive bias of the gnODEs to learn (approximate) continuous attractors. We further show how reduced-dimensional gnODEs retain their modeling power while greatly improving interpretability, even allowing explicit visualization of the structure of learned attractors. We introduce a novel measure of expressivity which probes the capacity of a neural network to generate complex trajectories. Using this measure, we explore how the phase-space dimension of the nODEs and the complexity of the function modeling the flow field contribute to expressivity. We see that a more complex function for modeling the flow field allows a lower-dimensional nODE to capture a given target dynamics. Finally, we demonstrate the benefit of gating in nODEs on several real-world tasks.