Jonathan William Pillow
Assistant Professor — Ph.D., New York University
- E-mail: firstname.lastname@example.org
- Phone: (512) 232-1923
- Office: SEA 4.104
- Campus Mail Code: A8000
Jonathan Pillow'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, neural population coding, psychophysics and modeling of human motion perception, theoretical models of adaptation, natural scene statistics, and supervised and unsupervised learning with spike trains.
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)
Berkes, P., Wood, F. & Pillow, J. (2009, September) Characterizing neural dependencies with copula models. Advances in Neural Information Processing Systems, 21, 129-136.
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.
Pillow, J. & Latham, P. (2008, September) Neural characterization in partially observed populations of spiking neurons. Advances in Neural Information Processing Systems, 20.
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.
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. .
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.
Schwartz, O., Pillow, J., Rust, N. & Simoncelli, E. (2006, September) Spike-triggered neural characterization. Journal of Vision, 6(4), 484-507.
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.
Semester Course Unique No. Title
2014 Spr NEU 394P 57790 Meths in Computational Neurosc
2014 Spr Psy 394U 44330 Seminar In Computational
2014 Spr Psy 394U 44345 TPCS Statistics/Neural
2014 Spr SSC 384 59765 TPCS Statistics/Neural
2014 Spr NEU 394P 57795 TPCS Statistics/Neural
2013 Fall Psy 323 43710 Perception