Jonathan Pillow
Theory and Computation
Theory and Computation

Associate Professor of Psychology and the Princeton Neuroscience Institute


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


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, visual perception, adaptation, scalable methods for high-dimensional regression and classification problems, latent variable models, functional connectivity, statistical inference for detailed biophysical models, information theory, and nonparametric Bayesian statistics.
 

Selected Publications

2017

  • 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.

2013

  • Archer E, Park I, & Pillow JW (2013). Bayesian entropy estimation for binary spike train data using parametric prior knowledge. Advances in Neural Information Processing Systems 26, 1700-1708. [pdf ]
  • Knudson K & Pillow JW (2013). Spike train entropy-rate estimation using hierarchical Dirichlet process priors. Advances in Neural Information Processing Systems 26, 2076-2084. [pdf]
  • Park I, Archer E, Priebe NJ, & Pillow JW (2013). Spectral methods for neural characterization using generalized quadratic models. Advances in Neural Information Processing Systems 26, 2454-2462. [pdf]
  • Park I, Archer E, Latimer K, & Pillow JW (2013). Universal models for binary spike patterns using centered Dirichlet processes. Advances in Neural Information Processing Systems 26, 2463-2471. [pdf]
  • Park M & Pillow JW (2013). Bayesian inference for low-rank spatiotemporal neural receptive fields. Advances in Neural Information Processing Systems 26, 2688-2696. [pdf]
  • Williamson RW, Sahani M & Pillow JW (2013) Equating information-theoretic and likelihood-based methods for neural dimensionality reduction. [link]
  • Archer E, Park IM, & Pillow JW (2013). Bayesian and quasi-Bayesian estimators for mutual information from discrete data. Entropy 15(5), 1738-1755. Special Issue: Estimating Information-Theoretic Quantities from Data. [pdf]
  • Park M, Koyejo O, Ghosh J, Poldrack RA, & Pillow JW. (2013). Bayesian structure learning for functional neuroimaging. Proceedings of the 16th International Conference on Artificial Intelligence and Statistics (AISTATS), 1-9. [pdf]
  • Pillow JW, Shlens J, Chichilnisky EJ, Simoncelli EP. (2013). A model-based spike sorting algorithm for removing correlation artifacts in multi-neuron recordings. PLOS ONE. 8(5). 1-14. [pdf]

2012

  • Archer E, Park I, & Pillow JW (2012). Bayesian estimation of discrete entropy with mixtures of stick-breaking priors. Advances in Neural Information Processing Systems 25, eds. P. Bartlett and F.C.N. Pereira and C.J.C. Burges and L. Bottou and K.Q. Weinberger, 2024-2032. [pdf]
  • Park M & Pillow JW (2012). Bayesian active learning with localized priors for fast receptive field characterization. Advances in Neural Information Processing Systems 25, eds. P. Bartlett and F.C.N. Pereira and C.J.C. Burges and L. Bottou and K.Q. Weinberger, 2357-2365. [pdf]
  • Pillow JW & Scott JG (2012) Fully Bayesian inference for neural models with negative-binomial spiking. Advances in Neural Information Processing Systems (NIPS) 25, eds. P. Bartlett and F.C.N. Pereira and C.J.C. Burges and L. Bottou and K.Q. Weinberger, 1907-1915 [pdf]
  • Vidne M, Ahmadian Y, Shlens J, Pillow JW, Kulkarni J, Litke AM, Chichilnisky EJ, Simoncelli EP, & Paninski L (2012). Modeling the impact of common noise inputs on the network activity of retinal ganglion cells. J Comput Neurosci, 33(1):97-121 [pdf]

2011

  • Park I & Pillow JW (2011). Bayesian spike-triggered covariance. Advances in Neural Information Processing Systems (NIPS) 24, eds. Shawe-Taylor, J.; Zemel, R.; Bartlett, P.; Pereira, F. & Weinberger, K., 1692-1700. [ pdf]
  • Park M, Horwitz GD, & Pillow JW (2011). Active learning of neural response functions with Gaussian processes. Advances in Neural Information Processing Systems (NIPS) 24, eds. Shawe-Taylor, J.; Zemel, R.; Bartlett, P.; Pereira, F. & Weinberger, K., 2043-2051. [pdf ]
  • Park M & Pillow JW (2011). Receptive field inference with localized priors. PLoS Computational Biology 7(10), 1-16. [pdf]
  • Histed MH & Pillow JW (2011). The 8th annual computational and systems neuroscience (Cosyne) meeting Neural Systems & Circuits 1:8, 1-3 (Invited meeting review). [link]
  • Pillow JW, Ahmadian Y, & Paninski L (2011). Model-based decoding, information estimation, and change-point detection techniques for multi-neuron spike trains. Neural Computation 23:1-45. [pdf]
  • Ahmadian Y, Pillow JW, & Paninski L (2011). Efficient Markov Chain Monte Carlo Methods for Decoding Neural Spike Trains. Neural Computation 23:46-96 [pdf]

2010

  • Nirenberg S, Bomash I, Pillow JW, & Victor JD (2010) Heterogeneous response dynamics in retinal ganglion cells: the interplay of predictive coding and adaptation. J Neurophysiol 103: 3184-3194. [pdf]