Estimating Noise Correlations Across Continuous Conditions With Wishart Processes

TL;DR

This paper introduces Wishart process models to estimate neural noise covariance across continuous conditions, improving accuracy and enabling predictions in unseen scenarios with limited trials.

Contribution

The authors develop and validate a novel Wishart process approach for estimating neural noise correlations across conditions with few trials, outperforming standard methods.

Findings

Wishart models produce smoother covariance estimates across stimulus parameters.

They enable noise correlation estimation in unseen conditions.

The approach improves Fisher information estimation in neural data.

Abstract

The signaling capacity of a neural population depends on the scale and orientation of its covariance across trials. Estimating this "noise" covariance is challenging and is thought to require a large number of stereotyped trials. New approaches are therefore needed to interrogate the structure of neural noise across rich, naturalistic behaviors and sensory experiences, with few trials per condition. Here, we exploit the fact that conditions are smoothly parameterized in many experiments and leverage Wishart process models to pool statistical power from trials in neighboring conditions. We demonstrate that these models perform favorably on experimental data from the mouse visual cortex and monkey motor cortex relative to standard covariance estimators. Moreover, they produce smooth estimates of covariance as a function of stimulus parameters, enabling estimates of noise correlations in entirely unseen conditions as well as continuous estimates of Fisher information--a commonly used measure of signal fidelity. Together, our results suggest that Wishart processes are broadly applicable tools for quantification and uncertainty estimation of noise correlations in trial-limited regimes, paving the way toward understanding the role of noise in complex neural computations and behavior.