pillow lab @ princeton

Publications

Pre-prints


• Revisiting the high-dimensional geometry of population responses in visual cortex
  Pospisil, D, and Pillow JW (2024).
  bioRxiv 2024.02.16.580726; doi: https://doi.org/10.1101/2024.02.16.580726
  


• From connectome to effectome: learning the causal interaction map of the fly brain
  Pospisil D, Aragon M, & Pillow JW (2023).
  bioRxiv 2023.10.31.564922; doi: https://doi.org/10.1101/2023.10.31.564922
  


• Olfactory learning alters navigation strategies and behavioral variability in C. elegans
  Chen KS, Pillow JW, & Leifer AM (2023).
  arXiv:2311.07117 [q-bio.NC]
  


• Compact deep neural network models of visual cortex
  Cowley BR, Stan PL, Pillow JW, & Smith MA (2023).
  bioRxiv 2023.11.22.568315; doi: https://doi.org/10.1101/2023.11.22.568315
  


• Neural population dynamics underlying evidence accumulation in multiple rat brain regions
  DePasquale B, Brody CD, & Pillow JW (2021).
  bioRxiv 2021.10.28.465122; doi: https://doi.org/10.1101/2021.10.28.465122
  


• Bayesian efficient coding
  Park, Il Memming & Pillow JW (2020).
  bioRxiv 178418; doi: https://doi.org/10.1101/178418
  


• Recurrent dynamics of prefrontal cortex during context-dependent decision-making
  Cohen Z, DePasquale B, Aoi MC, & JW Pillow (2020).
  bioRxiv 2020.11.27.401539; doi: https://doi.org/10.1101/2020.11.27.401539
  


• A comparison of deep learning and linear-nonlinear cascade approaches to neural encoding
  Moskovitz TH, Roy NA, & Pillow JW (2018).
  bioRxiv 463422; doi: https://doi.org/10.1101/463422
  


2024


• Pre-existing visual responses in a projection-defined dopamine population explain individual learning trajectories
  A Pan-Vazquez, Y Sanchez Araujo, B McMannon, M Louka, A Bandi, L Haetzel, International Brain Laboratory, JW Pillow, ND Daw, IB Witten (2024). Current Biology (accepted). [bioRxiv]
  


• Sparse-coding variational auto-encoders
  Geadah V*, Barello G*, Greenidge D, Charles AS, & Pillow JW (2024).
  Neural Computation (accepted). [bioRxiv]
  


• Mapping model units to visual neurons reveals population code for social behaviour
  Cowley, BR, Calhoun AJ, Rangarajan N, Ireland E, Turner MH, Pillow JW, & Murthy M (2024).
  Nature. [abs | pdf | link | bibtex]
  


• Cross Talk opposing view: Marr's three levels of analysis are not useful as a framework for neuroscience
  Pillow JW (2024). J Physiol, 602: 1915-1917. [link | pdf | bibtex]
  Opposing view from Máté Lengyel: CrossTalk proposal
  My rebuttal: [link | pdf]
  


• Parsing neural dynamics with infinite recurrent switching linear dynamical systems
  Geadah V, International Brain Lab, & Pillow JW (2024).
  Intl. Conf. on Learning Representations (ICLR). [abs | pdf | link | bibtex]
  


• Modeling state-dependent communication between brain regions with switching nonlinear dynamical systems
  Karniol-Tambour O, Zoltowski DM, Diamanti EM, Pinto L, Brody CD, Tank DW, & Pillow JW (2024).
  Intl. Conf. on Learning Representations (ICLR) [abs | pdf | link | bibtex]
  


• Bayesian Active Learning for Discrete Latent Variable Models
  Jha A, Ashwood ZC, & Pillow JW (2024).
  Neural Computation 36 (3): 437-474. [abs | pdf | link | code | bibtex ]
  

Efficient decoding of large-scale neural population responses with Gaussian-process multiclass regression
Greenidge CD, Scholl B, Yates J, & Pillow JW (2024).
Neural Computation 36(2): 175-226. [abs | pdf | link | code | bibtex]


2023


• Spectral learning of Bernoulli linear dynamical systems models for decision-making
  Stone IR*, Sagiv Y*, Park IM, Pillow JW (2023).
  Transactions on Machine Learning Research 2835-8856. [abs | pdf | link | code | bibtex]
  


• Temporal integration is a robust feature of perceptual decisions
  Hyafil A, de la Rocha J, Pericas C, Katz LN, Huk AC, & Pillow JW (2023).
  eLife 12:e84045. [abs | link | bibtex]
  


• A general framework for inferring Bayesian ideal observer models from psychophysical data
  Manning TS, Naecker BN, McLean IR, Rokers B, Pillow JW, Cooper EQ (2023).
  eNeuro 10(1):1-17. [abs | pdf | link | bibtex]
  


• Scalable variational inference for low-rank spatio-temporal receptive fields
  Duncker L, Ruda KM, Field GD, & Pillow JW (2023).
  Neural Computation 35(6): 995-1027. [abs | pdf | link |code | bibtex]
  


• 
  2022
  
  
• Dynamic inverse reinforcement learning for characterizing animal behavior
  Ashwood Z*, Jha A*, & Pillow JW (2022).
  Advances in Neural Information Processing Systems (NeurIPS) 35. [abs | pdf | link | bibtex]
  


• Extracting computational mechanisms from neural data using low-rank RNNs
  Valente A, Pillow JW, & Ostojic S (2022).
  Advances in Neural Information Processing Systems (NeurIPS) 35. [abs | pdf | link | bibtex]
  


• Correcting motion induced fluorescence artifacts in two-channel neural imaging
  Creamer MS, Chen KS, Leifer AM, & Pillow JW (2022).
  PloS Computational Biology 18(9):1-14. [abs | pdf | link | code | bibtex ]
  


• Probing the relationship between linear dynamical systems and low-rank recurrent neural network models
  Valente A, Ostojic S, & Pillow JW (2022).
  Neural Computation 34(9): 1871–1892. [abs | pdf | link | bibtex]
  


• Running reduces firing but improves coding in rodent higher-order visual cortex
  Christensen AJ & Pillow JW (2022).
  Nature Communications 13(1):1676. [abs | link | pdf | bibtex]
  


• Detecting and Correcting False Transients in Calcium Imaging
  Gauthier JL, Koay SA, Nieh EH, Tank DW, Pillow JW, & Charles AS. (2022).
  Nature Methods 19(4):470–478 [link | pdf | code | bibtex ]
  


• Opponent control of behavior by dorsomedipal striatal pathways depends on task demands and internal state
  Bolkan SS*, Stone IR*, Pinto L, Ashwood ZC, Garcia JMI, Herman AL, Singh P, Bandi A, Cox J, Zimmerman CA, Cho JR, Engelhard B, Pillow JW, & Witten IB (2022).
  Nature Neuroscience 25(3): 345–357. [abs | link | pdf | code:data-anls | code:glm-hmm | bibtex]
  


• Mice alternate between discrete strategies during perceptual decision-making
  Ashwood ZC, Roy NA, Stone, IR, The International Brain Laboratory, Urai AE, Churchland, AK, Pouget A, & Pillow JW (2022).
  Nature Neuroscience 25(2): 201–212. [abs | link | pdf | code | bibtex]
  


• Loss-calibrated expectation propagation for approximate Bayesian decision-making
  Morais MJ & Pillow JW (2022).
  arXiv:2201.03128 [stat.ML]
  


• Majority of choice-related variability in perceptual decisions is present in early sensory cortex
  Morais MJ, Michelson C, Chen Y, Pillow JW, & Seidmann E (2022).
  bioRxiv 207357; doi: https://doi.org/10.1101/207357
  


2021


• Brain kernel: a new spatial covariance function for fMRI data
  Wu A, Nastase SA, Baldassano CA, Turk-Brown NB, Norman KA, Engelhardt BE, & Pillow JW. (2021).
  NeuroImage 245 (118580) [abs | link | pdf | code | bibtex]
  


• Factor-analytic Inverse Regression for High-dimension, Small-sample Dimensionality Reduction
  Jha* A, Morais* MJ, & Pillow JW. (2021).
  Proceedings of the 38th International Conference on Machine Learning (ICML), PMLR 139:4850-4859. [abs | link | pdf | code | bibtex]
  


• Inferring Latent Dynamics Underlying Neural Population Activity via Neural Differential Equations
  Kim TD, Luo TZ, Pillow JW, and Brody CD. (2021).
  Proceedings of the 38th International Conference on Machine Learning (ICML), PMLR 139:5551-5561. [abs | link | pdf | bibtex]
  


• Neural Anatomy and Optical Microscopy (NAOMi) Simulation for evaluating calcium imaging methods
  Song A, Gauthier JL, Pillow JW, Tank DW, & Charles AS. (2021).
  J. Neurosci. Methods 358. 109173 [link | pdf | code | bibtex]
  


• Extracting the dynamics of behavior in sensory decision-making experiments
  Roy NA, Bak JH, The International Brain Laboratory, Akrami A, Brody CD, & Pillow JW (2021).
  Neuron 109:1-14. [abs | link | pdf | code | colab-notebook | bibtex]
  


2020


• Modeling statistical dependencies in multi-region spike train data
  Keeley SL, Zoltowski DM, Aoi MC, & Pillow JW (2020).
  Current Opinion in Neurobiology 65: 194-202. [abs | pdf | link | bibtex]
  


• Identifying signal and noise structure in neural population activity with Gaussian process factor models
  Keeley SL, Aoi MC, Yu Y, Smith SL, BR & Pillow JW (2020).
  Advances in Neural Information Processing Systems (NeurIPS) 33: 13795-13805. [abs | pdf | link | bibtex]
  


• High-contrast “gaudy” images improve the training of deep neural network models of visual cortex
  Cowley BR & Pillow JW (2020).
  Advances in Neural Information Processing Systems (NeurIPS) 33: 21591-21603. [abs | pdf | link | bibtex]
  


• Inferring learning rules from animal decision-making
  Ashwood Z*, Roy NA*, Bak JH, The International Brain Laboratory, & Pillow JW (2020).
  Advances in Neural Information Processing Systems (NeurIPS) 33: 3442-3453. [abs | pdf | link | bibtex]
  


• Prefrontal cortex exhibits multi-dimensional dynamic encoding during decision-making
  Aoi MC, Mante V, & Pillow JW. (2020).
  Nat Neurosci 23, 1410–1420. [abs | pdf | link | code | bibtex]
  


• Poisson balanced spiking networks
  Rullán Buxó CE & Pillow JW. (2020).
  PLoS Computational Biology 16:1-27. [ abs | link | code | bibtex]
  


• Efficient non-conjugate Gaussian process factor models for spike count data using polynomial approximations
  Keeley SL, Zoltowski DM, Yu Y, Yates JL, Smith SL, & Pillow JW. (2020).
  Proceedings of the 37th International Conference on Machine Learning (ICML) 119:5177-5186. [abs | pdf | link | bibtex]


• A general recurrent state space framework for modeling neural dynamics during decision-making
  Zoltowski DM, Pillow JW, & Linderman SW. (2020).
  Proceedings of the 37th International Conference on Machine Learning (ICML) 119:11680-11691. [abs | pdf | link | code | bibtex]


• A simple linear readout of MT supports motion direction-discrimination performance
  Yates JA, Katz LN, Levi AJ, Pillow JW, and Huk AC (2020).
  J. Neurophysiol. 123(2):682–694. [link | pdf | bibtex]


2019


• Inferring synaptic inputs from spikes with a conductance-based neural encoding model
  Latimer KW, Rieke F, & Pillow JW (2019).
  eLife 8:e47012 [abs | link | pdf | code | bibtex]


• Unsupervised identification of the internal states that shape natural behavior
  Calhoun AJ, Pillow JW, & Murthy M. (2019).
  Nature Neuroscience 22:2040-20149. [abs | link | pdf | code | bibtex]


• Error-correcting dynamics in visual working memory
  Panichello MF, DePasquale B, Pillow JW, & Buschman TJ (2019).
  Nature Communications 10 (1): 3366. [link | bibtex]


• Dependent relevance determination for smooth and structured sparse regression
  Wu Anqi, Koyejo O, & Pillow JW (2019).
  Journal of Machine learning Research 20 (89): 1-43. [abs | pdf | bibtex | code]
  


• Discrete stepping and nonlinear ramping dynamics underlie spiking responses of LIP neurons during decision-making
  Zoltowski D, Latimer KW, Yates JL, Huk AC, & Pillow JW (2019).
  Neuron 102(6):1249-1258. [abs | pdf | link | code | bibtex]


• Representational structure or task structure? Bias in neural representational similarity analysis and a Bayesian method for reducing bias
  Cai MB, Schuck NW, Pillow JW, & Niv Y. (2019).
  PLoS computational biology, 15 (5), e1006299 [link | bibtex]


• Editorial overview: Machine learning, big data, and neuroscience
  Pillow JW & Sahani M (2019).
  Current Opinion in Neurobiology 55:iii-iv. [link | pdf | bibtex]


2018


• Adaptive stimulus selection for multi-alternative psychometric functions with lapses
  Bak Ji Hyun & Pillow JW (2018).
  Journal of Vision 18(12):4, 1-25 [abs | pdf | link | bibtex | code]
  


• Knöll J, Pillow JW, & Huk AC (2018).
  Lawful tracking of visual motion in humans, macaques, and marmosets in a naturalistic, continuous, and untrained behavioral context
  Proc. Nat. Acad. Sci. 201807192; doi: 10.1073/pnas.1807192115. [link | bibtex]


• Scaling the Poisson GLM to massive neural datasets through polynomial approximations
  Zoltowski D & Pillow JW (2018).
  Advances in Neural Information Processing Systems 31, 3521-3531. [abs | pdf | link | bibtex | code]
  


• Power-law efficient neural codes provide general link between perceptual bias and discriminability
  Morais MJ & Pillow JW (2018).
  Advances in Neural Information Processing Systems 31, 5071-5080. [abs | pdf | link | bibtex ]
  


• Learning a latent manifold of odor representations from neural responses in piriform cortex
  Wu Anqi, Pashkovski S, Datta RS & Pillow JW (2018).
  Advances in Neural Information Processing Systems 31, 5379-5389. [abs | pdf | link | code | bibtex ]
  


• Efficient inference for time-varying behavior during learning
  Roy NA, Bak JH, Akrami A, Brody CD, & Pillow JW (2018).
  Advances in Neural Information Processing Systems 31, 5696-5706. [abs | pdf | link | bibtex | code ]
  


• Model-based targeted dimensionality reduction for neuronal population data
  Aoi M & Pillow JW (2018).
  Advances in Neural Information Processing Systems 31, 6689-6698. [abs | pdf | link | bibtex]
  


• Dethroning the Fano Factor: a flexible, model-based approach to partitioning neural variability
  Charles AS, Park Mijung, Weller JP, Horwitz GD, & Pillow JW (2018).
  Neural Computation 30(4):1012-1045. [abs | pdf | link | bibtex | code]
  


• Systematic misperceptions of 3D motion explained by Bayesian inference
  Rokers B, Fulvio JM, Pillow JW, and Cooper EA (2018).
  Journal of Vision 18(23):1-23. [link | bibtex | code]
  


• Shared representational geometry across neural networks
  Lu Qihong, Chen Po-Hsuan, Pillow JW, Ramadge PJ, Norman KA, & Hasson U (2018).
  arXiv:1811.11684 [cs.LG]. [link]
  


2017


• Combined social and spatial coding in a descending projection from the prefrontal cortex
  Murugan M, Jang HJ, Park M, Miller EM, Cox J, Taliaferro JP, Parker NF, Bhave V, Nectow AR, Pillow JW, & Witten IB (2017).
  Cell. 171(7):1663-1677. [pdf | link | bibtex]
  


• Gaussian process based nonlinear latent structure discovery in multivariate spike train data Wu Anqi, Roy NA, Keeley S, & Pillow JW (2017).
  Advances in Neural Information Processing Systems 30, 3496-3505 [abs | pdf | link | code | bibtex]
  


• Capturing the dynamical repertoire of single neurons with generalized linear models
  Weber AI & Pillow JW (2017).
  Neural Computation 29(12): 3260-3289 [abs | pdf | link | code | bibtex]


• Is population activity more than the sum of its parts?
  Pillow JW & Aoi MC (2017).
  Nat. Neurosci. 20, 1196-1198. (News & Views on Elsayed & Cunningham 2017).
  [link | pdf]
  


• Functional dissection of signal and noise in MT and LIP during decision-making
  Yates JL, Park Il Memming, Katz LN, Pillow JW, & Huk AC (2017).
  Nat. Neurosci. 20, 1285-1292. [abs | pdf | link | bibtex ]
  


• No cause for pause: new analyses of ramping and stepping dynamics in LIP (Rebuttal to Response to Reply to Comment on Latimer et al. 2015)
  Latimer KW, Huk AC, & Pillow JW (2017).
  bioRxiv. [link | pdf]
  


• Discovering event structure in continuous narrative perception and memory
  Baldassano C, Chen J, Zadbood A, Pillow JW, Hasson U, & Norman KA (2016).
  Neuron 95(3): 709-721. [pdf | link | bibtex]
  


• Volumetric Two-photon Imaging of Neurons Using Stereoscopy (vTwINS)
  Song A*, Charles AS*, Koay SA, Gauthier JL, Thiberge SY, Pillow JW, & Tank DW (2017).
  Nature Methods 14(4): 420-460. [abs | pdf | link | bibtex]
  


• Computational approaches to fMRI analysis
  Cohen JD, Daw N, Engelhardt B, Hasson U, Li K, Niv Y, Norman KA, Pillow JW, Ramadge PJ, Turk-Brown NB, & Willke TL (2017).
  Nature Neuroscience 20: 304-313. [link]
  


• Stochastic filtering of two-photon imaging using reweighted l1 Charles AS, Song A, Koay SA, Tank DW, & Pillow JW (2017). IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 1038-1042. [link | pdf]
  


• An International Laboratory for Systems and Computational Neuroscience LF Abbott, DE Angelaki, M Carandini, AK Churchland, Y Dan, P Dayan, S Deneve, I Fiete, S Ganguli, KD Harris, M Häusser, S Hofer, PE Latham, ZF Mainen, T Mrsic-Flogel, L Paninski, JW Pillow, A Pouget, K Svoboda, IB Witten, & AM Zador (2017).
  Neuron 96 (6), 1213-1218. [link]


• Exploiting gradients and Hessians in Bayesian optimization and Bayesian quadrature
  Wu A, Aoi MC, & Pillow JW (2017).
  arXiv:1704.00060
  


• Scalable Bayesian inference for high-dimensional neural receptive fields
  Aoi MC & Pillow JW (2017).
  bioRxiv 212217; doi: https://doi.org/10.1101/212217 [code]
  


2016


• Adaptive optimal training of animal behavior
  Bak JH, Choi JY, Akrami A, Witten IB, & Pillow JW (2016).
  eds D. D. Lee and M. Sugiyama and U. V. Luxburg and I. Guyon and R. Garnett. Advances in Neural Information Processing Systems 29, 1947-1955. [abs | pdf | bibtex | code]
  


• Bayesian latent structure discovery from multi-neuron recordings
  Linderman S, Adams R, & Pillow JW (2016).
  eds D. D. Lee and M. Sugiyama and U. V. Luxburg and I. Guyon and R. Garnett. Advances in Neural Information Processing Systems 29, 2002-2010. [abs | pdf | bibtex | code]
  


• A Bayesian method for reducing bias in neural representational similarity analysis
  Cai MB, Schuck N, Pillow JW, & Niv Y (2016).
  eds D. D. Lee and M. Sugiyama and U. V. Luxburg and I. Guyon and R. Garnett. Advances in Neural Information Processing Systems 29, 4951-4959. [link | bibtex]
  


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


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


• Adaptive Bayesian methods for closed-loop neurophysiology
  Pillow JW & Park Mijung (2016).
  In Closed Loop Neuroscience, ed. A. El Hady, Elsevier: 3-18. [abs | pdf | link | bibtex | code]


2015


• Convolutional spike-triggered covariance analysis for neural subunit models
  Wu A , Park Il Memming, & Pillow JW (2015).
  Advances in Neural Information Processing Systems 28, 793-801. [abs | pdf | bibtex | code]


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


• Single-trial spike trains in parietal cortex reveal discrete steps during decision-making
  Latimer KW, Yates JL, Meister MLR, Huk AC, & Pillow JW (2015).
  Science 349(6244): 184-187. [abs | pdf | link | SI | code]


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


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


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


2014


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


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


• Bayesian active learning of neural firing rate maps with transformed Gaussian process priors
  Park Mijung, Weller JP, Horwitz GD, & Pillow JW (2014).
  Neural Computation 26(8):1519-1541. [abs | pdf | SI | link | code]


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


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


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


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


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


2013


• Bayesian entropy estimation for binary spike train data using parametric prior knowledge
  Archer E, Park Il Memming, & Pillow JW (2013).
  Advances in Neural Information Processing Systems 26, 1700-1708. [abs | pdf | code ]


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


• Spectral methods for neural characterization using generalized quadratic models
  Park Il Memming, Archer E, Priebe NJ, & Pillow JW (2013).
  Advances in Neural Information Processing Systems 26, 2454-2462. [abs | pdf ]


• Universal models for binary spike patterns using centered Dirichlet processes
  Park Il Memming, Archer E, Latimer K, & Pillow JW (2013).
  Advances in Neural Information Processing Systems 26, 2463-2471. [abs | pdf ]


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


• Bayesian and quasi-Bayesian estimators for mutual information from discrete data
  Archer E, Park Il Memming, & Pillow JW (2013).
  Entropy 15(5), 1738-1755. Special Issue: Estimating Information-Theoretic Quantities from Data. [abs | pdf | link]


• Bayesian structure learning for functional neuroimaging
  Park Mijung, Koyejo O, Ghosh J, Poldrack RA, & Pillow JW. (2013).
  Proceedings of the 16th International Conference on Artificial Intelligence and Statistics (AISTATS), 1-9. [abs | pdf]


• A model-based spike sorting algorithm for removing correlation artifacts in multi-neuron recordings
  Pillow JW, Shlens J, Chichilnisky EJ, Simoncelli EP. (2013).
  PLOS ONE. 8(5).
  1-14. [abs | pdf | link ]


2012


• Bayesian estimation of discrete entropy with mixtures of stick-breaking priors
  Archer E, Park Il Memming, & Pillow JW (2012).
  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]


• Bayesian active learning with localized priors for fast receptive field characterization
  Park Mijung & Pillow JW (2012).
  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]


• Fully Bayesian inference for neural models with negative-binomial spiking
  Pillow JW & Scott JG (2012) 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]


• Modeling the impact of common noise inputs on the network activity of retinal ganglion cells
  Vidne M, Ahmadian Y, Shlens J, Pillow JW, Kulkarni J, Litke AM, Chichilnisky EJ, Simoncelli EP, & Paninski L (2012).
  J Comput Neurosci, 33(1): 97-121 [abs | pdf | link]


2011


• Bayesian spike-triggered covariance
  Park Il Memming & Pillow JW (2009).
  Advances in Neural Information Processing Systems (NIPS) 24, eds. Shawe-Taylor, J.; Zemel, R.; Bartlett, P.; Pereira, F. & Weinberger, K., 1692-1700 [abs | pdf]


• Active learning of neural response functions with Gaussian processes.
  Park Mijung, Horwitz GD, & Pillow JW (2011).
  Advances in Neural Information Processing Systems (NIPS) 24, eds. Shawe-Taylor, J.; Zemel, R.; Bartlett, P.; Pereira, F. & Weinberger, K., 2043-2051 [abs | pdf]


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


• The 8th annual computational and systems neuroscience (Cosyne) meeting Histed MH & Pillow JW (2011).
  Neural Systems & Circuits 1:8, 1-3 (Invited meeting review) [ link]
  


• Model-based decoding, information estimation, and change-point detection techniques for multi-neuron spike trains
  Pillow JW, Ahmadian Y, & Paninski L (2011).
  Neural Computation 23:1-45. [abs | pdf]


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


2010


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


2009


• Time-rescaling methods for the estimation and assessment of non-Poisson neural encoding models
  Pillow JW. (2009).
  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]


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


2008 and earlier


• Spatio-temporal correlations and visual signaling in a complete neuronal population
  Pillow JW, Shlens J, Paninski L, Sher A, Litke AM, Chichilnisky EJ, Simoncelli EP (2008).
  Nature 454: 995-999. [abs | pdf | SI | code ]


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


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


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


• Dimensionality reduction in neural models: an information-theoretic generalization of spike-triggered average and covariance analysis
  Pillow JW and Simoncelli EP (2006).
  Journal of Vision, 6(4):414-428. [abs | pdf | code]


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


• Prediction and Decoding of Retinal Ganglion Cell Responses with a Probabilistic Spiking Model
  Pillow JW, Paninski L, Uzzell VJ, Simoncelli EP, Chichilnisky EJ. (2005).
  Journal of Neuroscience 25:11003-11013. [abs | pdf | code]


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


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


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


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


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


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


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


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