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Wilson Geisler, Director SEA 4.328A, Mailcode A8000, Austin, TX 78712 • 512-471-5380

Jonathan William Pillow

Assistant Professor Ph.D., New York University

Jonathan William Pillow

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Biography

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.

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)
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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 |
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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]
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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]
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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]
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Berkes, P., Wood, F. & Pillow, J. (2009, September) Characterizing neural dependencies with copula models. Advances in Neural Information Processing Systems, 21, 129-136.
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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.
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Pillow, J. & Latham, P. (2008, September) Neural characterization in partially observed populations of spiking neurons. Advances in Neural Information Processing Systems, 20.
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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.
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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. .
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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.
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Schwartz, O., Pillow, J., Rust, N. & Simoncelli, E. (2006, September) Spike-triggered neural characterization. Journal of Vision, 6(4), 484-507.
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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.
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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 

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