pillow lab @ princeton

neural coding & computation group

Publications

    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. [pdf]

  • Linderman S, Adams R, & Pillow JW (2016). Bayesian latent structure discovery from multi-neuron recordings Advances in Neural Information Processing Systems 29. [link].

  • Cai MB, Schuck N, Pillow JW, & Niv Y (2016). A Bayesian method for reducing bias in neural representational similarity analysis Advances in Neural Information Processing Systems 29. [link]

  • Baldassano C, Chen J, Zadbood A, Pillow JW, Hasson U, & Norman KA (2016). Discovering event structure in continuous narrative perception and memory. bioRxiv preprint [link]

  • Song A*, Charles AS*, Koay SA, Gauthier JL, Thiberge SY, Pillow JW, & Tank DW (2016). Volumetric Two-photon Imaging of Neurons Using Stereoscopy (vTwINS). bioRxiv preprint. [link]

  • Weber AI & Pillow JW (2016). Capturing the dynamical repertoire of single neurons with generalized linear models. arXiv:1602.07389 preprint. [link]

  • Katz LN*, Yates JL*, Pillow JW, & Huk AC (2016). Dissociated functional significance of decision-related activity in the primate dorsal stream. Nature 535, 285–288. [abs | link | pdf]

  • Latimer KL, Yates JL, Meister MLR, Huk AC, & Pillow JW (2016). Response to Comment on "Single-trial spike trains in parietal cortex reveal discrete steps during decision-making." Science 351(6280): 1406. [link]
    Related: [Comment from Shadlen et al.]

  • Pillow JW & Park M (2016). Adaptive Bayesian methods for closed-loop neurophysiology. In Closed Loop Neuroscience, ed. A. El Hady, Elsevier. [abs | pdf | link]


  • 2015

  • Wu A , Park IM, & Pillow JW (2015). Convolutional spike-triggered covariance analysis for neural subunit models. Advances in Neural Information Processing Systems 28, 1-9. [abs | pdf ]

  • Pillow JW (2015). Explaining the especially pink elephant. Nature Neuroscience 18: 1435–1436. (News & Views on Wei & Stocker 2015). [link]

  • 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. [abs | pdf | link | SI | code]

  • Latimer KW, Huk AC, & Pillow JW (2015). Bayesian inference for latent stepping and ramping models of spike train data. Chapter in Advanced State Space Methods for Neural and Clinical Data: 160-185. [link ]

  • Williamson RW, Sahani M & Pillow JW (2015). The equivalence of information-theoretic and likelihood-based methods for neural dimensionality reduction . PLoS Computational Biology, 11(4):1-31. [abs | pdf | link ]

  • Bonnen K, Burge J, Yates J, Pillow JW, & Cormack LC (2015). Continuous psychophysics: Target-tracking to measure visual sensitivity. Journal of Vision 15(3):14, 1-16. [abs | pdf | link | code]


  • 2014

  • Park IM, Meister, MLR, Huk AC, & Pillow JW (2014). Encoding and decoding in parietal cortex during sensorimotor decision-making. Nature Neuroscience 17, 1395-1403. [abs | pdf | SI | link | code]

  • Archer E, Park I, & Pillow JW (2014). Bayesian Entropy Estimation for Countable Discrete Distributions. Journal of Machine Learning Research 15 (Oct): 2833-2868. [abs | pdf | link | code ]

  • 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. [abs | pdf | SI | link ]

  • Archer, EW, Koster U, Pillow JW, & Macke JH (2014). Low-dimensional models of neural population activity in sensory cortical circuits. Advances in Neural Information Processing Systems 27, 343-351. [abs | pdf ]

  • Grabska-Barwinska A, & Pillow JW (2014). Optimal prior-dependent neural population codes under shared input noise. Advances in Neural Information Processing Systems 27, 1880-1888. [abs | pdf ]

  • Knudson KC, Yates JL, Huk AC, Pillow JW (2014). Inferring sparse representations of continuous signals with continuous orthogonal matching pursuit. Advances in Neural Information Processing Systems 27, 1215-1223. [abs |
    pdf ]

  • Latimer KW, Chichilnisky EJ, Rieke F, Pillow JW (2014). Inferring synaptic conductances from spike trains with a biophysically inspired point process model. Advances in Neural Information Processings Systems 27, 954-962. [abs | pdf ]

  • Wu A, Park M, Koyejo OO, Pillow JW (2014). Sparse Bayesian structure learning with dependent relevance determination priors. Advances in Neural Information Processing Systems 27, 1628-1636. [abs | pdf ]


  • 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. [abs | pdf | code ]

  • Knudson K & Pillow JW (2013). Spike train entropy-rate estimation using hierarchical Dirichlet process priors. Advances in Neural Information Processing Systems 26, 2076-2084. [abs | 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. [abs | 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. [abs | pdf ]

  • Park M & Pillow JW (2013). Bayesian inference for low-rank spatiotemporal neural receptive fields. Advances in Neural Information Processing Systems 26, 2688-2696. [abs | pdf ]

  • 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. [abs | pdf | link]

  • 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. [abs | 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. [abs | pdf | link ]


  • 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. [abs | 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. [abs | 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. [abs | 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 [abs | pdf | link]


  • 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 [abs | 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 [abs | pdf]

  • Park M & Pillow JW (2011). Receptive field inference with localized priors. PLoS Computational Biology 7(10), 1-16 [abs | pdf | SI | link | code]

  • 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. [abs | pdf]

  • Ahmadian Y, Pillow JW, & Paninski L (2011). Efficient Markov Chain Monte Carlo Methods for Decoding Neural Spike Trains. Neural Computation 23:46-96 [abs | 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. [abs | pdf | link]


  • 2009

  • Pillow JW. (2009). Time-rescaling methods for the estimation and assessment of non-Poisson neural encoding models. Advances in Neural Information Processing Systems (NIPS) 22 eds. Y. Bengio, D. Schuurmans, J. Lafferty, C. K. I. Williams, A. Culotta. MIT Press. 1473-1481.  [abs | pdf]

  • Berkes P, Wood F, and Pillow JW (2009). Characterizing neural dependencies with copula models. in Advances in Neural Information Processing Systems 21 eds. D. Koller, D. Schuurmans, Y. Bengio, L. Bottou. 129-136.  [abs | pdf]


  • 2008 and earlier

  • 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.  [abs | pdf | SI | code ]

  • Pillow JW and Latham P (2008) Neural characterization in partially observed populations of spiking neurons. Advances in Neural Information Processing Systems 20. eds. J.C. Platt, D. Koller, Y. Singer, S. Roweis. 1161-1168.  [abs | pdf]

  • Pillow JW (2007) Likelihood-based approaches to modeling the neural code. In Bayesian Brain: Probabilistic Approaches to Neural Coding, eds. K Doya, S Ishii, A Pouget & R Rao. MIT press. 53-70.  [abs | pdf]

  • Paninski L, Pillow JW, and Lewi J (2007) Statistical models for neural encoding, decoding, and optimal stimulus design. In Computational Neuroscience: Theoretical Insights Into Brain Function, eds. P Cisek, T Drew, & J Kalaska.  [abs | pdf]

  • 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.   [abs | pdf | code]

  • Schwartz O, Pillow JW, Rust NC, Simoncelli EP (2006). Spike-triggered neural characterization. Journal of Vision, 6(4):484-507.  [abs | pdf | code]

  • 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.  [abs | pdf | code]

  • Paninski L, Pillow JW, Simoncelli EP. (2005). Comparing integrate-and-fire- like models given intracellular and extracellular data. Neurocomputing 65:379-385.   [abs | pdf]

  • Simoncelli EP, Paninski L, Pillow JW, Schwartz O. (2004). Characterization of neural responses with stochastic stimuli. In M Gazzaniga (ed.) The Cognitive Neurosciences, 3rd edition. MIT Press. [abs  | pdf  | code]

  • Paninski L, Pillow JW, Simoncelli EP. (2004). Maximum likelihood estimation of a stochastic integrate-and-fire neural encoding model. Neural Computation, 16:2533-2561.   [abs | pdf | code]

  • Pillow JW, Paninski L, Simoncelli EP. (2004) Maximum Likelihood Estimation of a Stochastic Integrate-and-Fire Neural Model. Advances in Neural Information Processing Systems, eds S. Thrun, L. Saul and B. Schölkopf, vol 16, May 2004, MIT Press, Cambridge MA.   [abs | pdf]

  • Pillow JW & Simoncelli, EP. (2003). Biases in white noise analysis due to non-Poisson spike generation. Neurocomputing. 52-54:109-115.   [abs | pdf]

  • Pillow JW & Rubin N. (2002). Perceptual Completion across the Vertical Meridian and the Role of Early Visual Cortex. Neuron 33(5):805-13.   [abs | pdf]

  • Zemel RS & Pillow JW (2002). A Probabilistic Network Model of Population Responses. In R Rao, B Olshausen and M Lewicki (eds.) Probabilistic Models of the Brain. MIT Press.   [abs | pdf]

  • Zemel RS & Pillow JW. (2000). Encoding multiple orientations in a recurrent network. Neurocomputing, 32-33:609-616.   [abs | pdf]