Jonathan Pillow
Theory and Computation
Theory and Computation

Professor of Psychology and Neuroscience.

Faculty

Research: Statistical approaches to neural coding, computation, and dynamics
Areas of Research: Computational Neuroscience
pillow@princeton.edu
Research Lab
609-258-7848
254 PNI


Research Focus

My lab works on problems at the intersection of neuroscience, statistics, and machine learning. We develop statistical methods for characterizing structure in high dimensional data, and collaborate very closely with experimental groups to study neural systems and the computations they perform. We are also interested in the brain's ability to perform statistical inference in naturalistic tasks, and in the theoretical principles governing the function and design of sensory systems. Current research topics include sensory-motor decision making, working memory, latent variable models, dimensionality reduction, scalable methods for large-scale datasets, regression models for electrophysiology and calcium imaging data, and quantitative methods for behavior.
 
  • View complete list of Publications.
  • Positions available. Email inquiries welcome.

Selected Publications

2019

  • Wu Anqi, Koyejo O, & Pillow JW (2019). Dependent relevance determination for smooth and structured sparse regression. Journal of Machine learning Research 20 (89): 1-43. [abs]
  • Zoltowski D, Latimer KW, Yates JL, Huk AC, & Pillow JW (2019). Discrete stepping and nonlinear ramping dynamics underlie spiking responses of LIP neurons during decision-making. Neuron 102(6):1249-1258. [abs]

2018

  • Aoi M & Pillow JW (2018). Model-based targeted dimensionality reduction for neuronal population data. Advances in Neural Information Processing Systems 31, 6689-6698. [abs]
  • Charles AS, Park Mijung, Weller JP, Horwitz GD, & Pillow JW (2018). Dethroning the Fano Factor: a flexible, model-based approach to partitioning neural variability. Neural Computation 30(4):1012-1045. [abs]
  • Roy NA, Bak JH, Akrami A, Brody CD, & Pillow JW (2018). Efficient inference for time-varying behavior during learning. Advances in Neural Information Processing Systems 31, 5696-5706. [abs]
  • Zoltowski D & Pillow JW (2018). Scaling the Poisson GLM to massive neural datasets through polynomial approximations. Advances in Neural Information Processing Systems 31, 3521-3531. [abs]

2017

  • Wu Anqi, Roy NA, Keeley S, & Pillow JW (2017). Gaussian process based nonlinear latent structure discovery in multivariate spike train data Advances in Neural Information Processing Systems 30, 3496-3505 [abs]
  • Yates JL, Park Il Memming, Katz LN, Pillow JW, & Huk AC (2017). Functional dissection of signal and noise in MT and LIP during decision-making. Nature Neuroscience 20, 1285-1292.
  • Weber AI & Pillow JW (2017). Capturing the dynamical repertoire of single neurons with generalized linear models. Neural Computation.

2016

  • Bak JH, Choi JY, Akrami A, Witten IB, & Pillow JW (2016). Adaptive optimal training of animal behavior. Advances in Neural Information Processing Systems 29, 1947-1955. 
  • Latimer KL, Yates JL, Meister MLR, Huk AC, & Pillow JW (2015). Single-trial spike trains in parietal cortex reveal discrete steps during decision-making. Science 349(6244): 184-187.

2014

  • Archer E, Park I, & Pillow JW (2014). Bayesian Entropy Estimation for Countable Discrete Distributions. Journal of Machine Learning Research (accepted). [abstract]
  • Park M, Weller JP, Horwitz GD, & Pillow JW (2014). Bayesian active learning of neural firing rate maps with transformed Gaussian process priors. Neural Computation 26(8):1519-1541. [link]
  • Park Il Memming, Meister, MLR, Huk AC, & Pillow JW (2014). Encoding and decoding in parietal cortex during sensorimotor decision-making. Nature Neuroscience 17, 1395-1403.

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