Stephen Keeley

Tracking the Fidelity of Internal Neural Representations with Error-In-Variables Regression

The paper introduces an error-in-variables regression framework to quantify mismatches between neural representations and measured variables, showing it accurately recovers dynamics and identifies fidelity regimes in neural data.

Improved inference of latent neural states from calcium imaging data

The paper develops improved models for inferring latent neural states from calcium imaging data by integrating spiking and calcium dynamics directly into latent variable frameworks.

Identifying signal and noise structure in neuralpopulation activity with Gaussian process factormodels

This paper introduces SNP-GPFA, a Gaussian process factor model to separate signal and noise in neural population activity, showing noise perturbs activity orthogonal to signal.