Jonathan Pillow

Princeton Neuroscience Institute
Office Phone
254 Princeton Neuroscience Institute

Ph.D., New York University


My research sits at the interface between computational neuroscience and statistical machine learning. We develop statistical tools for making sense of high-dimensional neural datasets and seek to understand how neurons process and transmit information using spike trains. We collaborate closely with experimentalists to study neural encoding and decoding, and the transformation of neural representations between brain areas. We also develop models for the brain's ability to learn, perceive, and make decisions, and seek to uncover the theoretical principles governing the design of sensory systems.

Representative Publications

  • 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]
  • 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]
  • 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.
  • 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.
  • 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.