pillow lab @ princeton

neural coding & computation group

The Pillow lab is a computational neuroscience and machine learning group at Princeton University. We use mathematical, computational, and statistical methods to study neural systems and behavior.


Our research focuses on statistical methods for characterizing neural population responses and extracting structure from high-dimensional neural data. We collaborate closely with experimental groups to study how information is encoded, decoded, and processed in different brain areas. We also build statistical models of human perceptual and decision-making behavior, and seek to understand the theoretical principles governing the design of neural systems.

Current research topics include:

  • - multivariate point process models
  • - latent variable models for spike train and imaging data
  • - receptive field estimation
  • - scalable methods for high-dimensional data
  • - inference for detailed biophysical models
  • - motion perception
  • - perceptual decision making
  • - adaptation
  • - information theory and estimation of information-theoretic quantities
  • - spike sorting
  • - statistical signal processing for calcium imaging data
  • - active learning / closed-loop experimental design
  • - Bayesian optimization

See our lab blog for more information about our latest activities.