Features and dimensions: Motion estimation in fly vision

TL;DR

This paper investigates how fly visual neurons compute motion by identifying low-dimensional features in high-dimensional sensory signals, revealing strategies for optimal motion estimation amidst noise.

Contribution

It introduces a method to characterize neural sensitivity to low-dimensional subspaces, advancing understanding of feature selectivity in complex sensory processing.

Findings

Neurons are most sensitive within a low-dimensional subspace of signals.

The neural response mapping exhibits nonlinear structure.

Results support the hypothesis of optimal motion estimation strategies.

Abstract

We characterize the computation of motion in the fly visual system as a mapping from the high dimensional space of signals in the retinal photodetector array to the probability of generating an action potential in a motion sensitive neuron. Our approach to this problem identifies a low dimensional subspace of signals within which the neuron is most sensitive, and then samples this subspace to visualize the nonlinear structure of the mapping. The results illustrate the computational strategies predicted for a system that makes optimal motion estimates given the physical noise sources in the detector array. More generally, the hypothesis that neurons are sensitive to low dimensional subspaces of their inputs formalizes the intuitive notion of feature selectivity and suggests a strategy for characterizing the neural processing of complex, naturalistic sensory inputs.