Liam Paninski - RankMe

125 papers A* 16A 6Misc 5Journal 65Unranked 33

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	An uncertainty-aware framework for data-efficient multi-view animal pose estimation. CoRR Lenny Aharon, Keemin Lee, Karan Sikka, Selmaan Chettih, Cole L. Hurwitz, Liam Paninski, Matthew R. Whiteway
2025	J	`jnl`	Decoding inner speech with an end-to-end brain-to-text neural interface. CoRR Yizi Zhang, Linyang He, Chaofei Fan, Tingkai Liu, Han Yu, Trung Le, Jingyuan Li, Scott W. Linderman, Lea Duncker, Francis R. Willett, Nima Mesgarani, Liam Paninski
2025	A*	`conf`	In vivo cell-type and brain region classification via multimodal contrastive learning. ICLR Han Yu, Hanrui Lyu, YiXun Xu, Charlie Windolf, Eric Kenji Lee, Fan Yang, Andrew M. Shelton, Olivier Winter, International Brain Laboratory, Eva L. Dyer, Chandramouli Chandrasekaran, Nicholas A. Steinmetz, Liam Paninski, Cole Lincoln Hurwitz
2025	A*	`conf`	Neural Encoding and Decoding at Scale. ICML Yizi Zhang, Yanchen Wang, Mehdi Azabou, Alexandre Andre, Zixuan Wang, Hanrui Lyu, International Brain Laboratory, Eva L. Dyer, Liam Paninski, Cole Lincoln Hurwitz
2025	J	`jnl`	Neural Encoding and Decoding at Scale. CoRR Yizi Zhang, Yanchen Wang, Mehdi Azabou, Alexandre Andre, Zixuan Wang, Hanrui Lyu, International Brain Laboratory, Eva L. Dyer, Liam Paninski, Cole L. Hurwitz
2025	J	`jnl`	Self-supervised pretraining of vision transformers for animal behavioral analysis and neural encoding. CoRR Yanchen Wang, Han Yu, Ari Blau, Yizi Zhang, International Brain Laboratory, Liam Paninski, Cole L. Hurwitz, Matthew R. Whiteway
2024	J	`jnl`	A study of animal action segmentation algorithms across supervised, unsupervised, and semi-supervised learning paradigms. CoRR Ari Blau, Evan S. Schaffer, Neeli Mishra, Nathaniel J. Miska, International Brain Laboratory, Liam Paninski, Matthew R. Whiteway
2024	J	`jnl`	Removing direct photocurrent artifacts in optogenetic connectivity mapping data via constrained matrix factorization. PLoS Comput. Biol. Benjamin Antin, Masato Sadahiro, Marta Gajowa, Marcus A. Triplett, Hillel Adesnik, Liam Paninski
2024	A*	`conf`	Towards a "Universal Translator" for Neural Dynamics at Single-Cell, Single-Spike Resolution. NeurIPS Yizi Zhang, Yanchen Wang, Donato Jiménez-Benetó, Zixuan Wang, Mehdi Azabou, Blake A. Richards, Renee Tung, Olivier Winter, International Brain Laboratory, Eva L. Dyer, Liam Paninski, Cole L. Hurwitz
2024	J	`jnl`	Towards a "universal translator" for neural dynamics at single-cell, single-spike resolution. CoRR Yizi Zhang, Yanchen Wang, Donato Jiménez-Benetó, Zixuan Wang, Mehdi Azabou, Blake A. Richards, Olivier Winter, International Brain Laboratory, Eva L. Dyer, Liam Paninski, Cole L. Hurwitz
2024	J	`jnl`	Versatile multiple object tracking in sparse 2D/3D videos via deformable image registration. PLoS Comput. Biol. James Ryu, Amin Nejatbakhsh, Mahdi Torkashvand, Sahana Gangadharan, Maedeh Seyedolmohadesin, Jinmahn Kim, Liam Paninski, Vivek Venkatachalam
2023	A*	`conf`	Bayesian target optimisation for high-precision holographic optogenetics. NeurIPS Marcus A. Triplett, Marta Gajowa, Hillel Adesnik, Liam Paninski
2023	A*	`conf`	Bypassing spike sorting: Density-based decoding using spike localization from dense multielectrode probes. NeurIPS Yizi Zhang, Tianxiao He, Julien Boussard, Charles Windolf, Olivier Winter, Eric Trautmann, Noam Roth, Hailey Barrell, Mark Churchland, Nicholas A. Steinmetz, Erdem Varol, Cole L. Hurwitz, Liam Paninski
2023	—	`conf`	Learning Probabilistic Piecewise Rigid Atlases of Model Organisms via Generative Deep Networks. IPMI Amin Nejatbakhsh, Neel Dey, Vivek Venkatachalam, Eviatar Yemini, Liam Paninski, Erdem Varol
2023	Misc	`conf`	Multimodal Microscopy Image Alignment Using Spatial and Shape Information and a Branch-and-Bound Algorithm. ICASSP Shuonan Chen, Bovey Y. Rao, Stephanie Herrlinger, Attila Losonczy, Liam Paninski, Erdem Varol
2023	Misc	`conf`	Robust Online Multiband Drift Estimation in Electrophysiology Data. ICASSP Charlie Windolf, Angelique C. Paulk, Yoav Kfir, Eric Trautmann, Domokos Meszéna, William Muñoz, Irene Caprara, Mohsen Jamali, Julien Boussard, Ziv M. Williams, Sydney S. Cash, Liam Paninski, Erdem Varol
2023	A*	`conf`	Towards robust and generalizable representations of extracellular data using contrastive learning. NeurIPS Ankit Vishnubhotla, Charlotte Loh, Akash Srivastava, Liam Paninski, Cole L. Hurwitz
2022	J	`jnl`	Blind demixing methods for recovering dense neuronal morphology from barcode imaging data. PLoS Comput. Biol. Shuonan Chen, Jackson Loper, Pengcheng Zhou, Liam Paninski
2022	J	`jnl`	SemiMultiPose: A Semi-supervised Multi-animal Pose Estimation Framework. CoRR Ari Blau, Christoph Gebhardt, Andrés Bendesky, Liam Paninski, Anqi Wu
2021	J	`jnl`	A general linear-time inference method for Gaussian Processes on one dimension. J. Mach. Learn. Res. Jackson Loper, David M. Blei, John P. Cunningham, Liam Paninski
2021	J	`jnl`	BARcode DEmixing through Non-negative Spatial Regression (BarDensr). PLoS Comput. Biol. Shuonan Chen, Jackson Loper, Xiaoyin Chen, Alex Vaughan, Anthony M. Zador, Liam Paninski
2021	Misc	`conf`	Decentralized Motion Inference and Registration of Neuropixel Data. ICASSP Erdem Varol, Julien Boussard, Nishchal Dethe, Olivier Winter, Anne E. Urai, International Brain Laboratory, Anne Churchland, Nick Steinmetz, Liam Paninski
2021	—	`conf`	Non-parametric Vignetting Correction for Sparse Spatial Transcriptomics Images. MICCAI (8) Bovey Y. Rao, Alexis M. Peterson, Elena K. Kandror, Stephanie Herrlinger, Attila Losonczy, Liam Paninski, Abbas H. Rizvi, Erdem Varol
2021	J	`jnl`	Nonlinear Decoding of Natural Images From Large-Scale Primate Retinal Ganglion Recordings. Neural Comput. Young Joon Kim, Nora Brackbill, Eleanor Batty, Jin Hyung Lee, Catalin Mitelut, William Tong, E. J. Chichilnisky, Liam Paninski
2021	J	`jnl`	Partitioning variability in animal behavioral videos using semi-supervised variational autoencoders. PLoS Comput. Biol. Matthew R. Whiteway, Dan Biderman, Yoni Friedman, Mario Dipoppa, Estefany Kelly Buchanan, Anqi Wu, John Zhou, Niccolò Bonacchi, Nathaniel J. Miska, Jean-Paul Noel, Erica Rodriguez, Michael Schartner, Karolina Socha, Anne E. Urai, C. Daniel Salzman, International Brain Laboratory, John P. Cunningham, Liam Paninski
2021	A*	`conf`	Three-dimensional spike localization and improved motion correction for Neuropixels recordings. NeurIPS Julien Boussard, Erdem Varol, Hyun Dong Lee, Nishchal Dethe, Liam Paninski
2020	J	`jnl`	Attentive Clustering Processes. CoRR Ari Pakman, Yueqi Wang, Yoonho Lee, Pallab Basu, Juho Lee, Yee Whye Teh, Liam Paninski
2020	A*	`conf`	Deep Graph Pose: a semi-supervised deep graphical model for improved animal pose tracking. NeurIPS Anqi Wu, Estefany Kelly Buchanan, Matthew R. Whiteway, Michael Schartner, Guido Meijer, Jean-Paul Noel, Erica Rodriguez, Claire Everett, Amy Norovich, Evan Schaffer, Neeli Mishra, C. Daniel Salzman, Dora E. Angelaki, Andrés Bendesky, International Brain Laboratory, John P. Cunningham, Liam Paninski
2020	—	`conf`	Demixing Calcium Imaging Data in C. elegans via Deformable Non-negative Matrix Factorization. MICCAI (5) Amin Nejatbakhsh, Erdem Varol, Eviatar Yemini, Vivek Venkatachalam, Albert Lin, Aravinthan D. T. Samuel, Oliver Hobert, Liam Paninski
2020	—	`conf`	Disentangled Sticky Hierarchical Dirichlet Process Hidden Markov Model. ECML/PKDD (1) Ding Zhou, Yuanjun Gao, Liam Paninski
2020	J	`jnl`	Disentangled sticky hierarchical Dirichlet process hidden Markov model. CoRR Ding Zhou, Yuanjun Gao, Liam Paninski
2020	J	`jnl`	General linear-time inference for Gaussian Processes on one dimension. CoRR Jackson Loper, David M. Blei, John P. Cunningham, Liam Paninski
2020	J	`jnl`	Localized semi-nonnegative matrix factorization (LocaNMF) of widefield calcium imaging data. PLoS Comput. Biol. Shreya Saxena, Ian Kinsella, Simon Musall, Sharon H. Kim, Jozsef Meszaros, David N. Thibodeaux, Carla Kim, John J. Cunningham, Elizabeth M. C. Hillman, Anne Churchland, Liam Paninski
2020	A*	`conf`	Neural Clustering Processes. ICML Ari Pakman, Yueqi Wang, Catalin Mitelut, Jin Hyung Lee, Liam Paninski
2020	—	`conf`	Probabilistic Joint Segmentation and Labeling of C. elegans Neurons. MICCAI (5) Amin Nejatbakhsh, Erdem Varol, Eviatar Yemini, Oliver Hobert, Liam Paninski
2020	A*	`conf`	Recurrent Switching Dynamical Systems Models for Multiple Interacting Neural Populations. NeurIPS Joshua I. Glaser, Matthew R. Whiteway, John P. Cunningham, Liam Paninski, Scott W. Linderman
2020	—	`conf`	Statistical Atlas of C. elegans Neurons. MICCAI (5) Erdem Varol, Amin Nejatbakhsh, Ruoxi Sun, Gonzalo E. Mena, Eviatar Yemini, Oliver Hobert, Liam Paninski
2019	A*	`conf`	BehaveNet: nonlinear embedding and Bayesian neural decoding of behavioral videos. NeurIPS Eleanor Batty, Matthew R. Whiteway, Shreya Saxena, Dan Biderman, Taiga Abe, Simon Musall, Winthrop Gillis, Jeffrey E. Markowitz, Anne Churchland, John P. Cunningham, Sandeep R. Datta, Scott W. Linderman, Liam Paninski
2019	J	`jnl`	Discrete Neural Processes. CoRR Ari Pakman, Liam Paninski
2019	A*	`conf`	Efficient characterization of electrically evoked responses for neural interfaces. NeurIPS Nishal P. Shah, Sasidhar Madugula, Pawel Hottowy, Alexander Sher, Alan M. Litke, Liam Paninski, E. J. Chichilnisky
2019	—	`conf`	Neural Permutation Processes. AABI Ari Pakman, Yueqi Wang, Liam Paninski
2019	A*	`conf`	Scalable Bayesian inference of dendritic voltage via spatiotemporal recurrent state space models. NeurIPS Ruoxi Sun, Scott W. Linderman, Ian Kinsella, Liam Paninski
2019	—	`conf`	Sinkhorn Permutation Variational Marginal Inference. AABI Gonzalo E. Mena, Erdem Varol, Amin Nejatbakhsh, Eviatar Yemini, Liam Paninski
2018	J	`jnl`	A Novel Variational Family for Hidden Nonlinear Markov Models. CoRR Daniel Hernández, Antonio Khalil Moretti, Ziqiang Wei, Shreya Saxena, John P. Cunningham, Liam Paninski
2018	J	`jnl`	Amortized Bayesian inference for clustering models. CoRR Ari Pakman, Liam Paninski
2018	J	`jnl`	Community-based benchmarking improves spike rate inference from two-photon calcium imaging data. PLoS Comput. Biol. Philipp Berens, Jeremy Freeman, Thomas Deneux, Nicolay Chenkov, Thomas McColgan, Artur Speiser, Jakob H. Macke, Srinivas C. Turaga, Patrick J. Mineault, Peter Rupprecht, Stephan Gerhard, Rainer W. Friedrich, Johannes Friedrich, Liam Paninski, Marius Pachitariu, Kenneth D. Harris, Ben Bolte, Timothy A. Machado, Dario Ringach, Jasmine Stone, Luke E. Rogerson, Nicolas J. Sofroniew, Jacob Reimer, Emmanouil Froudarakis, Thomas Euler, Miroslav Román Rosón, Lucas Theis, Andreas S. Tolias, Matthias Bethge
2018	A	`conf`	Reparameterizing the Birkhoff Polytope for Variational Permutation Inference. AISTATS Scott W. Linderman, Gonzalo E. Mena, Hal James Cooper, Liam Paninski, John P. Cunningham
2018	A*	`conf`	Scalable Approximate Bayesian Inference for Particle Tracking Data. ICML Ruoxi Sun, Liam Paninski
2017	A	`conf`	Bayesian Learning and Inference in Recurrent Switching Linear Dynamical Systems. AISTATS Scott W. Linderman, Matthew J. Johnson, Andrew C. Miller, Ryan P. Adams, David M. Blei, Liam Paninski
2017	J	`jnl`	Electrical stimulus artifact cancellation and neural spike detection on large multi-electrode arrays. PLoS Comput. Biol. Gonzalo E. Mena, Lauren E. Grosberg, Sasidhar Madugula, Pawel Hottowy, Alan M. Litke, John P. Cunningham, E. J. Chichilnisky, Liam Paninski
2017	J	`jnl`	Fast online deconvolution of calcium imaging data. PLoS Comput. Biol. Johannes Friedrich, Pengcheng Zhou, Liam Paninski
2017	J	`jnl`	Multi-scale approaches for high-speed imaging and analysis of large neural populations. PLoS Comput. Biol. Johannes Friedrich, WeiJian Yang, Daniel Soudry, Yu Mu, Misha B. Ahrens, Rafael Yuste, Darcy S. Peterka, Liam Paninski
2017	—	`conf`	Multilayer Recurrent Network Models of Primate Retinal Ganglion Cell Responses. ICLR (Poster) Eleanor Batty, Josh Merel, Nora Brackbill, Alexander Heitman, Alexander Sher, Alan M. Litke, E. J. Chichilnisky, Liam Paninski
2017	—	`conf`	Neural Networks for Efficient Bayesian Decoding of Natural Images from Retinal Neurons. NIPS Nikhil Parthasarathy, Eleanor Batty, William Falcon, Thomas Rutten, Mohit Rajpal, E. J. Chichilnisky, Liam Paninski
2017	—	`conf`	OnACID: Online Analysis of Calcium Imaging Data in Real Time. NIPS Andrea Giovannucci, Johannes Friedrich, Matthew T. Kaufman, Anne Churchland, Dmitri B. Chklovskii, Liam Paninski, Eftychios A. Pnevmatikakis
2017	A	`conf`	Scalable Variational Inference for Super Resolution Microscopy. AISTATS Ruoxi Sun, Evan Archer, Liam Paninski
2017	A*	`conf`	Stochastic Bouncy Particle Sampler. ICML Ari Pakman, Dar Gilboa, David E. Carlson, Liam Paninski
2017	—	`conf`	YASS: Yet Another Spike Sorter. NIPS Jin Hyung Lee, David E. Carlson, Hooshmand Shokri Razaghi, Weichi Yao, Georges A. Goetz, Espen Hagen, Eleanor Batty, E. J. Chichilnisky, Gaute T. Einevoll, Liam Paninski
2016	—	`conf`	Automated scalable segmentation of neurons from multispectral images. NIPS Uygar Sümbül, Douglas H. Roossien, Dawen Cai, Fei Chen, Nicholas Barry, John P. Cunningham, Edward S. Boyden, Liam Paninski
2016	—	`conf`	Fast Active Set Methods for Online Spike Inference from Calcium Imaging. NIPS Johannes Friedrich, Liam Paninski
2016	—	`conf`	Linear dynamical neural population models through nonlinear embeddings. NIPS Yuanjun Gao, Evan W. Archer, Liam Paninski, John P. Cunningham
2016	J	`jnl`	Neuroprosthetic Decoder Training as Imitation Learning. PLoS Comput. Biol. Josh Merel, David E. Carlson, Liam Paninski, John P. Cunningham
2016	A*	`conf`	Partition Functions from Rao-Blackwellized Tempered Sampling. ICML David E. Carlson, Patrick Stinson, Ari Pakman, Liam Paninski
2015	J	`jnl`	Efficient "Shotgun" Inference of Neural Connectivity from Highly Sub-sampled Activity Data. PLoS Comput. Biol. Daniel Soudry, Suraj Keshri, Patrick Stinson, Min-hwan Oh, Garud Iyengar, Liam Paninski
2015	J	`jnl`	Encoder-Decoder Optimization for Brain-Computer Interfaces. PLoS Comput. Biol. Josh Merel, Donald M. Pianto, John P. Cunningham, Liam Paninski
2015	J	`jnl`	Neuroprosthetic decoder training as imitation learning. CoRR Josh Merel, David E. Carlson, Liam Paninski, John P. Cunningham
2014	—	`conf`	Clustered factor analysis of multineuronal spike data. NIPS Lars Buesing, Timothy A. Machado, John P. Cunningham, Liam Paninski
2014	J	`jnl`	Fast inference in generalized linear models via expected log-likelihoods. J. Comput. Neurosci. Alexandro D. Ramirez, Liam Paninski
2014	J	`jnl`	Fast state-space methods for inferring dendritic synaptic connectivity. J. Comput. Neurosci. Ari Pakman, Jonathan Hunter Huggins, Carl Smith, Liam Paninski
2014	J	`jnl`	On Quadrature Methods for Refractory Point Process Likelihoods. Neural Comput. Gonzalo E. Mena, Liam Paninski
2013	—	`conf`	A multi-agent control framework for co-adaptation in brain-computer interfaces. NIPS Josh Merel, Roy Fox, Tony Jebara, Liam Paninski
2013	—	`conf`	Auxiliary-variable Exact Hamiltonian Monte Carlo Samplers for Binary Distributions. NIPS Ari Pakman, Liam Paninski
2013	—	`conf`	Bayesian Inference and Online Experimental Design for Mapping Neural Microcircuits. NIPS Benjamin Shababo, Brooks Paige, Ari Pakman, Liam Paninski
2013	Misc	`conf`	Bayesian spike inference from calcium imaging data. ACSSC Eftychios A. Pnevmatikakis, Josh Merel, Ari Pakman, Liam Paninski
2013	—	`conf`	Robust learning of low-dimensional dynamics from large neural ensembles. NIPS David Pfau, Eftychios A. Pnevmatikakis, Liam Paninski
2013	—	`conf`	Sparse nonnegative deconvolution for compressive calcium imaging: algorithms and phase transitions. NIPS Eftychios A. Pnevmatikakis, Liam Paninski
2012	J	`jnl`	A Bayesian compressed-sensing approach for reconstructing neural connectivity from subsampled anatomical data. J. Comput. Neurosci. Yuriy Mishchenko, Liam Paninski
2012	—	`conf`	Decoding arm and hand movements across layers of the macaque frontal cortices. EMBC Yan Tat Wong, Mariana Vigeral, David Putrino, David Pfau, Josh Merel, Liam Paninski, Bijan Pesaran
2012	J	`jnl`	EMG Prediction From Motor Cortical Recordings via a Nonnegative Point-Process Filter. IEEE Trans. Biomed. Eng. Kianoush Nazarpour, Christian Ethier, Liam Paninski, James M. Rebesco, R. Chris Miall, Lee E. Miller
2012	J	`jnl`	Fast Spatiotemporal Smoothing of Calcium Measurements in Dendritic Trees. PLoS Comput. Biol. Eftychios A. Pnevmatikakis, Keith J. Kelleher, Rebecca Chen, Petter Saggau, Kresimir Josic, Liam Paninski
2012	A	`conf`	Fast interior-point inference in high-dimensional sparse, penalized state-space models. AISTATS Eftychios A. Pnevmatikakis, Liam Paninski
2012	J	`jnl`	Inferring synaptic inputs given a noisy voltage trace via sequential Monte Carlo methods. J. Comput. Neurosci. Liam Paninski, Michael Vidne, Brian DePasquale, Daniel Gil Ferreira
2012	A	`conf`	Low rank continuous-space graphical models. AISTATS Carl Smith, Frank D. Wood, Liam Paninski
2012	J	`jnl`	Modeling the impact of common noise inputs on the network activity of retinal ganglion cells. J. Comput. Neurosci. Michael Vidne, Yashar Ahmadian, Jonathon Shlens, Jonathan W. Pillow, Jayant Kulkarni, Alan M. Litke, E. J. Chichilnisky, Eero P. Simoncelli, Liam Paninski
2012	J	`jnl`	Optimal experimental design for sampling voltage on dendritic trees in the low-SNR regime. J. Comput. Neurosci. Jonathan Hunter Huggins, Liam Paninski
2012	Misc	`conf`	Robust particle filters via sequential pairwise reparameterized Gibbs sampling. CISS Liam Paninski, Kamiar Rahnama Rad, Michael Vidne
2011	J	`jnl`	Automating the design of informative sequences of sensory stimuli. J. Comput. Neurosci. Jeremy Lewi, David M. Schneider, Sarah M. N. Woolley, Liam Paninski
2011	J	`jnl`	Efficient Markov Chain Monte Carlo Methods for Decoding Neural Spike Trains. Neural Comput. Yashar Ahmadian, Jonathan W. Pillow, Liam Paninski
2011	J	`jnl`	Hidden Markov Models for the Stimulus-Response Relationships of Multistate Neural Systems. Neural Comput. G. Sean Escola, Alfredo Fontanini, Donald B. Katz, Liam Paninski
2011	—	`conf`	Information Rates and Optimal Decoding in Large Neural Populations. NIPS Kamiar Rahnama Rad, Liam Paninski
2011	J	`jnl`	Model-Based Decoding, Information Estimation, and Change-Point Detection Techniques for Multineuron Spike Trains. Neural Comput. Jonathan W. Pillow, Yashar Ahmadian, Liam Paninski
2010	J	`jnl`	A new look at state-space models for neural data. J. Comput. Neurosci. Liam Paninski, Yashar Ahmadian, Daniel Gil Ferreira, Shinsuke Koyama, Kamiar Rahnama Rad, Michael Vidne, Joshua T. Vogelstein, Wei Wu
2010	J	`jnl`	Efficient computation of the maximum a posteriori path and parameter estimation in integrate-and-fire and more general state-space models. J. Comput. Neurosci. Shinsuke Koyama, Liam Paninski
2010	J	`jnl`	Fast Kalman filtering on quasilinear dendritic trees. J. Comput. Neurosci. Liam Paninski
2009	J	`jnl`	Bayesian Image Recovery for Dendritic Structures Under Low Signal-to-Noise Conditions. IEEE Trans. Image Process. Geoffrey Fudenberg, Liam Paninski
2009	J	`jnl`	Maximally Reliable Markov Chains Under Energy Constraints. Neural Comput. G. Sean Escola, Michael Eisele, Kenneth D. Miller, Liam Paninski
2009	J	`jnl`	Mean-Field Approximations for Coupled Populations of Generalized Linear Model Spiking Neurons with Markov Refractoriness. Neural Comput. Taro Toyoizumi, Kamiar Rahnama Rad, Liam Paninski
2009	J	`jnl`	Sequential Optimal Design of Neurophysiology Experiments. Neural Comput. Jeremy Lewi, Robert J. Butera, Liam Paninski
2009	J	`jnl`	Smoothing of, and Parameter Estimation from, Noisy Biophysical Recordings. PLoS Comput. Biol. Quentin J. M. Huys, Liam Paninski
2008	J	`jnl`	A Coincidence-Based Test for Uniformity Given Very Sparsely Sampled Discrete Data. IEEE Trans. Inf. Theory Liam Paninski
2008	—	`conf`	Designing neurophysiology experiments to optimally constrain receptive field models along parametric submanifolds. NIPS Jeremy Lewi, Robert J. Butera, David M. Schneider, Sarah M. N. Woolley, Liam Paninski
2008	J	`jnl`	Integral equation methods for computing likelihoods and their derivatives in the stochastic integrate-and-fire model. J. Comput. Neurosci. Liam Paninski, Adrian Haith, Gábor Szirtes
2008	J	`jnl`	State-Space Decoding of Goal-Directed Movements. IEEE Signal Process. Mag. Jayant E. Kulkarni, Liam Paninski
2008	J	`jnl`	Undersmoothed Kernel Entropy Estimators. IEEE Trans. Inf. Theory Liam Paninski, Masanao Yajima
2007	A	`conf`	Efficient active learning with generalized linear models. AISTATS Jeremy Lewi, Robert J. Butera, Liam Paninski
2006	—	`conf`	Efficient model-based design of neurophysiological experiments. EMBC Jeremy Lewi, Robert J. Butera, Liam Paninski
2006	—	`conf`	Real-time adaptive information-theoretic optimization of neurophysiology experiments. NIPS Jeremy Lewi, Robert J. Butera, Liam Paninski
2006	J	`jnl`	The Spike-Triggered Average of the Integrate-and-Fire Cell Driven by Gaussian White Noise. Neural Comput. Liam Paninski
2006	J	`jnl`	The most likely voltage path and large deviations approximations for integrate-and-fire neurons. J. Comput. Neurosci. Liam Paninski
2005	J	`jnl`	Asymptotic Theory of Information-Theoretic Experimental Design. Neural Comput. Liam Paninski
2005	J	`jnl`	Comparing integrate-and-fire models estimated using intracellular and extracellular data. Neurocomputing Liam Paninski, Jonathan W. Pillow, Eero P. Simoncelli
2005	—	`conf`	Large-scale biophysical parameter estimation in single neurons via constrained linear regression. NIPS Misha B. Ahrens, Quentin J. M. Huys, Liam Paninski
2005	—	`conf`	Nonparametric inference of prior probabilities from Bayes-optimal behavior. NIPS Liam Paninski
2005	J	`jnl`	Statistical encoding model for a primary motor cortical brain-machine interface. IEEE Trans. Biomed. Eng. Shy Shoham, Liam Paninski, Matthew Fellows, Nicholas G. Hatsopoulos, John P. Donoghue, Richard A. Normann
2004	J	`jnl`	Estimating Entropy on m Bins Given Fewer than m Samples. IEEE Trans. Inf. Theory Liam Paninski
2004	—	`conf`	Log-concavity Results on Gaussian Process Methods for Supervised and Unsupervised Learning. NIPS Liam Paninski
2004	J	`jnl`	Maximum Likelihood Estimation of a Stochastic Integrate-and-Fire Neural Encoding Model. Neural Comput. Liam Paninski, Jonathan W. Pillow, Eero P. Simoncelli
2004	—	`conf`	Variational Minimax Estimation of Discrete Distributions under KL Loss. NIPS Liam Paninski
2003	—	`conf`	Design of Experiments via Information Theory. NIPS Liam Paninski
2003	J	`jnl`	Estimation of Entropy and Mutual Information. Neural Comput. Liam Paninski
2003	—	`conf`	Maximum Likelihood Estimation of a Stochastic Integrate-and-Fire Neural Model. NIPS Jonathan W. Pillow, Liam Paninski, Eero P. Simoncelli
2003	J	`jnl`	Noise-driven adaptation: in vitro and mathematical analysis. Neurocomputing Liam Paninski, Brian Lau, Alex D. Reyes
2003	J	`jnl`	Robustness of neuroprosthetic decoding algorithms. Biol. Cybern. Mijail Serruya, Nicholas G. Hatsopoulos, Matthew Fellows, Liam Paninski, John P. Donoghue
2002	—	`conf`	Convergence Properties of Some Spike-Triggered Analysis Techniques. NIPS Liam Paninski
2001	J	`jnl`	Stochastic optimal control and the human oculomotor system. Neurocomputing Liam Paninski, Michael J. Hawken