Jonathan William Pillow
Adjunct Professor — Ph.D., New York University
Adjunct Assistant Professor

Interests
Neural coding, vision, mathematical modeling, and Bayesian statistics
Biography
Jonathan Pillow received a Ph.D. in Neural Science from New York University, and was a postdoctoral fellow at the Gatsby Computational Neuroscience Unit, UCL, before coming to the University of Texas. Jonathan's research interests lie at the intersection of computational neuroscience, machine learning, and human visual perception. His lab employs a variety of theoretical tools, in conjunction with psychophysical experiments, to study how neural populations represent and process information. He collaborates closely with labs devoted to neurophysiology and fMRI, applying Bayesian statistical methods to model the responses of neural populations in the visual pathway.
Current research topics include: neural decoding methods, population coding, human motion perception, theoretical models of adaptation, natural scene statistics, and unsupervised learning with spike trains.
Selected Publications (See lab site for full list of publications and PDF downloads)
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
Park M & Pillow JW (2011). Receptive field inference with localized priors. PLoS Computational Biology 7(10), 1-16.
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.
Pillow, JW, Shlens, J, Paninski, L, Sher, A, Litke, AM, Chichilnisky, EJ, Simoncelli, EP. (2008) Spatio-temporal correlations and visual signaling in a complete neuronal population. Nature 454: 995-999.
Pillow JW and Simoncelli EP. (2006). Dimensionality reduction in neural models: an information-theoretic generalization of spike-triggered average and covariance analysis. Journal of Vision, 6(4):414-428.
Pillow JW, Paninski L, Uzzell VJ, Simoncelli EP, Chichilnisky EJ. (2005). Prediction and Decoding of Retinal Ganglion Cell Responses with a Probabilistic Spiking Model. Journal of Neuroscience 25:11003-11013.
Pillow JW & Rubin N. (2002). Perceptual Completion across the Vertical Meridian and the Role of Early Visual Cortex.Neuron 33(5):805-13.
Courses
PSY 394U • Seminar In Computatnl Neurosci
44330 • Spring 2014
Meets TTH 11:00AM-12:30PM SEA 3.250
PSY 323 • Perception
43710 • Fall 2013
Meets TTH 9:30AM-11:00AM NOA 1.126
PSY 394U • Seminar In Computatnl Neurosci
43670 • Spring 2013
Meets W 9:00AM-12:00PM SEA 5.106
PSY 394U • Tpcs Statistics/Neural Coding
43680 • Spring 2013
Meets TTH 9:30AM-11:00AM SEA 5.106
PSY 323 • Perception
43255 • Fall 2012
Meets TTH 12:30PM-2:00PM NOA 1.126
PSY 323 • Perception
43145 • Fall 2011
Meets TTH 12:30PM-2:00PM NOA 1.126
PSY 394U • Meths In Computatnal Neurosci
44030 • Spring 2011
Meets F 9:00AM-12:00PM SEA 2.224
(also listed as NEU 394P)
PSY 323 • Perception
43100 • Fall 2010
Meets TTH 12:30PM-2:00PM NOA 1.126
PSY 394U • Meths In Computatnal Neurosci
44190 • Spring 2010
Meets TTH 2:00PM-3:30PM SEA 2.224
(also listed as NEU 394P)
PSY 323 • Perception
44065 • Fall 2009
Meets TTH 2:00PM-3:30PM NOA 1.124
PSY 394U • Smnr In Cognition And Perceptn
43445 • Spring 2009
Meets W 12:00PM-3:00PM SEA 5.128
Publications
Park, M. & Pillow, J.W. (2011). Receptive field inference with localized priors. PLoS Computational Biology (accepted) [abstract]
Histed MH & Pillow JW (2011). The 8th annual computational and systems neuroscience (Cosyne) meeting. Neural Systems & Circuits 1:8. (Invited meeting review)
download
Pillow, J.W., 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. [abstract |
download
Ahmadian, Y., Pillow, J.W. & Paninski, L. (2011). Efficient Markov Chain Monte Carlo Methods for Decoding Neural Spike Trains. Neural Computation 23:46-96 [abstract]
download
Nirenberg, S., Bomash, I., Pillow, J.W. & Victor, J.D. (2010) Heterogeneous response dynamics in retinal ganglion cells: the interplay of predictive coding and adaptation. J Neurophysiol 103: 3184-3194. [abstract]
Link
Pillow, J.W. (2009). Time-rescaling methods for the estimation and assessment of non-Poisson neural encoding models. Advances in Neural Information Processing Systems 22 eds. Y. Bengio, D. Schuurmans, J. Lafferty, C. K. I. Williams, A. Culotta. MIT Press. 1473-1481. [abstract]
download
Berkes, P., Wood, F. & Pillow, J. (2009, September) Characterizing neural dependencies with copula models. Advances in Neural Information Processing Systems, 21, 129-136.
download
Pillow, J., Shlens, J., Paninski, L., Sher, A., Litke, A., Chichilnisky, E. & Simoncelli, E. (2008, September) Spatio-temporal correlations and visual signaling in a complete neuronal population. Nature, 454, 995-999.
download
Pillow, J. & Latham, P. (2008, September) Neural characterization in partially observed populations of spiking neurons. Advances in Neural Information Processing Systems, 20.
download
Pillow, J. (2007) Likelihood-based modeling of neural responses. In K. Doya, S. Ishii, A. Pouget & R. Rao (Eds.), Bayesian Brain: Probabilistic Approaches to Neural Coding (pp.53-70). MIT Press.
download
Paninski, L., Pillow, J. & Lewi, J. (2007) Statistical models for neural encoding, decoding, and optimal stimulus design. In P. Cisek, T. Drew & J. Kalaska (Eds.), Computational Neuroscience: Theoretical Insights Into Brain Function. .
download
Pillow, J. & Simoncelli, E. (2006, September) Dimensionality reduction in neural models: an information-theoretic generalization of spike-triggered average and covariance analysis. Journal of Vision, 6(4), 414-428.
download
Schwartz, O., Pillow, J., Rust, N. & Simoncelli, E. (2006, September) Spike-triggered neural characterization. Journal of Vision, 6(4), 484-507.
download
Pillow, J., Paninski, L., Uzzell, V., Simoncelli, E. & Chichilnisky, E. (2005, September) Prediction and Decoding of Retinal Ganglion Cell Responses with a Probabilistic Spiking Model. J. Neurosci., 25, 11003-11013.
Simoncelli, E., Paninski, L., Pillow, J. & Schwartz, O. (2004) Characterization of neural responses with stochastic stimuli. In M. Gazzaniga (Ed.), The Cognitive Neurosciences, 3rd edition (pp.327-338). MIT Press.
Pillow, J., Paninski, L. & Simoncelli, E. (2004, September) Maximum likelihood estimation of a stochastic integrate-and-fire neural encoding model. Neural Computation, 16, 2533-2561.
Paninski, L., Pillow, J. & Simoncelli, E. (2004, September) Comparing integrate-and-fire-like models given intracellular and extracellular data. Neurocomputing, 65, 379-385.
Pillow, J., Paninski, L. & Simoncelli, E. (2004) Maximum Likelihood Estimation of a Stochastic Integrate-and-Fire Neural Model. In S. Thrun, L. Saul & B. Scholkopf (Eds.), Advances in Neural Information Processing Systems. MIT Press.
Pillow, J.W. & Rubin N. (2002). Perceptual Completion across the Vertical Meridian and the Role of Early Visual Cortex. Neuron 33(5):805-13.
download
Courses
Semester Course Unique No. Title
2014 Spr NEU 394P 57790 Meths in Computational Neurosc
2014 Spr Psy 394U 44330 Seminar In Computational
Neurosc
2014 Spr Psy 394U 44345 TPCS Statistics/Neural
Coding
2014 Spr SSC 384 59765 TPCS Statistics/Neural
Coding
2014 Spr NEU 394P 57795 TPCS Statistics/Neural
Coding
2013 Fall Psy 323 43710 Perception