Liam Paninski

Publication Activity (10 Years)

Years Active: 2001-2023
Publications (10 Years): 55

Top Venues

PLoS Comput. Biol.

NeurIPS

CoRR

NIPS

Publications

Marcus Triplett, Marta Gajowa, Hillel Adesnik, Liam Paninski
Bayesian target optimisation for high-precision holographic optogenetics. NeurIPS (2023)
Shuonan Chen, Bovey Y. Rao, Stephanie Herrlinger, Attila Losonczy, Liam Paninski, Erdem Varol
Multimodal Microscopy Image Alignment Using Spatial and Shape Information and a Branch-and-Bound Algorithm. ICASSP (2023)
Amin Nejatbakhsh, Neel Dey, Vivek Venkatachalam, Eviatar Yemini, Liam Paninski, Erdem Varol
Learning Probabilistic Piecewise Rigid Atlases of Model Organisms via Generative Deep Networks. IPMI (2023)
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
Robust Online Multiband Drift Estimation in Electrophysiology Data. ICASSP (2023)
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
Bypassing spike sorting: Density-based decoding using spike localization from dense multielectrode probes. NeurIPS (2023)
Ankit Vishnubhotla, Charlotte Loh, Akash Srivastava, Liam Paninski, Cole L. Hurwitz
Towards robust and generalizable representations of extracellular data using contrastive learning. NeurIPS (2023)
Shuonan Chen, Jackson Loper, Pengcheng Zhou, Liam Paninski
Blind demixing methods for recovering dense neuronal morphology from barcode imaging data. PLoS Comput. Biol. 18 (4) (2022)
Ari Blau, Christoph Gebhardt, Andrés Bendesky, Liam Paninski, Anqi Wu
SemiMultiPose: A Semi-supervised Multi-animal Pose Estimation Framework. CoRR (2022)
Julien Boussard, Erdem Varol, Hyun Dong Lee, Nishchal Dethe, Liam Paninski
Three-dimensional spike localization and improved motion correction for Neuropixels recordings. NeurIPS (2021)
Jackson Loper, David M. Blei, John P. Cunningham, Liam Paninski
A general linear-time inference method for Gaussian Processes on one dimension. J. Mach. Learn. Res. 22 (2021)
Erdem Varol, Julien Boussard, Nishchal Dethe, Olivier Winter, Anne Urai, International Brain Laboratory, Anne Churchland, Nick Steinmetz, Liam Paninski
Decentralized Motion Inference and Registration of Neuropixel Data. ICASSP (2021)
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
Partitioning variability in animal behavioral videos using semi-supervised variational autoencoders. PLoS Comput. Biol. 17 (9) (2021)
Bovey Y. Rao, Alexis M. Peterson, Elena K. Kandror, Stephanie Herrlinger, Attila Losonczy, Liam Paninski, Abbas H. Rizvi, Erdem Varol
Non-parametric Vignetting Correction for Sparse Spatial Transcriptomics Images. MICCAI (8) (2021)
Shuonan Chen, Jackson Loper, Xiaoyin Chen, Alex Vaughan, Anthony M. Zador, Liam Paninski
BARcode DEmixing through Non-negative Spatial Regression (BarDensr). PLoS Comput. Biol. 17 (3) (2021)
Young Joon Kim, Nora Brackbill, Eleanor Batty, Jin Hyung Lee, Catalin Mitelut, William Tong, E. J. Chichilnisky, Liam Paninski
Nonlinear Decoding of Natural Images From Large-Scale Primate Retinal Ganglion Recordings. Neural Comput. 33 (7) (2021)
Jackson Loper, David M. Blei, John P. Cunningham, Liam Paninski
General linear-time inference for Gaussian Processes on one dimension. CoRR (2020)
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
Localized semi-nonnegative matrix factorization (LocaNMF) of widefield calcium imaging data. PLoS Comput. Biol. 16 (4) (2020)
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 Angelaki, Andrés Bendesky, International Brain Laboratory, John P. Cunningham, Liam Paninski
Deep Graph Pose: a semi-supervised deep graphical model for improved animal pose tracking. NeurIPS (2020)
Amin Nejatbakhsh, Erdem Varol, Eviatar Yemini, Vivek Venkatachalam, Albert Lin, Aravinthan D. T. Samuel, Oliver Hobert, Liam Paninski
Demixing Calcium Imaging Data in C. elegans via Deformable Non-negative Matrix Factorization. MICCAI (5) (2020)
Ding Zhou, Yuanjun Gao, Liam Paninski
Disentangled Sticky Hierarchical Dirichlet Process Hidden Markov Model. ECML/PKDD (1) (2020)
Erdem Varol, Amin Nejatbakhsh, Ruoxi Sun, Gonzalo E. Mena, Eviatar Yemini, Oliver Hobert, Liam Paninski
Statistical Atlas of C. elegans Neurons. MICCAI (5) (2020)
Amin Nejatbakhsh, Erdem Varol, Eviatar Yemini, Oliver Hobert, Liam Paninski
Probabilistic Joint Segmentation and Labeling of C. elegans Neurons. MICCAI (5) (2020)
Ari Pakman, Yueqi Wang, Catalin Mitelut, Jin Hyung Lee, Liam Paninski
Neural Clustering Processes. ICML (2020)
Joshua I. Glaser, Matthew R. Whiteway, John P. Cunningham, Liam Paninski, Scott W. Linderman
Recurrent Switching Dynamical Systems Models for Multiple Interacting Neural Populations. NeurIPS (2020)
Ding Zhou, Yuanjun Gao, Liam Paninski
Disentangled sticky hierarchical Dirichlet process hidden Markov model. CoRR (2020)
Ari Pakman, Yueqi Wang, Yoonho Lee, Pallab Basu, Juho Lee, Yee Whye Teh, Liam Paninski
Attentive Clustering Processes. CoRR (2020)
Gonzalo E. Mena, Erdem Varol, Amin Nejatbakhsh, Eviatar Yemini, Liam Paninski
Sinkhorn Permutation Variational Marginal Inference. AABI (2019)
Ari Pakman, Yueqi Wang, Liam Paninski
Neural Permutation Processes. AABI (2019)
Ari Pakman, Liam Paninski
Discrete Neural Processes. CoRR (2019)
Nishal P. Shah, Sasidhar Madugula, Pawel Hottowy, Alexander Sher, Alan M. Litke, Liam Paninski, E. J. Chichilnisky
Efficient characterization of electrically evoked responses for neural interfaces. NeurIPS (2019)
Eleanor Batty, Matthew R. Whiteway, Shreya Saxena, Dan Biderman, Taiga Abe, Simon Musall, Winthrop Gillis, Jeffrey Markowitz, Anne Churchland, John P. Cunningham, Sandeep R. Datta, Scott W. Linderman, Liam Paninski
BehaveNet: nonlinear embedding and Bayesian neural decoding of behavioral videos. NeurIPS (2019)
Ruoxi Sun, Scott W. Linderman, Ian Kinsella, Liam Paninski
Scalable Bayesian inference of dendritic voltage via spatiotemporal recurrent state space models. NeurIPS (2019)
Ruoxi Sun, Liam Paninski
Scalable Approximate Bayesian Inference for Particle Tracking Data. ICML (2018)
Ari Pakman, Liam Paninski
Amortized Bayesian inference for clustering models. CoRR (2018)
Philipp Berens, Jeremy Freeman, Thomas Deneux, Nicolay Chenkov, Thomas McColgan, Artur Speiser, Jakob H. Macke, Srinivas C. Turaga, Patrick 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
Community-based benchmarking improves spike rate inference from two-photon calcium imaging data. PLoS Comput. Biol. 14 (5) (2018)
Scott W. Linderman, Gonzalo E. Mena, Hal James Cooper, Liam Paninski, John P. Cunningham
Reparameterizing the Birkhoff Polytope for Variational Permutation Inference. AISTATS (2018)
Daniel Hernández, Antonio Khalil Moretti, Ziqiang Wei, Shreya Saxena, John P. Cunningham, Liam Paninski
A Novel Variational Family for Hidden Nonlinear Markov Models. CoRR (2018)
Scott W. Linderman, Matthew J. Johnson, Andrew C. Miller, Ryan P. Adams, David M. Blei, Liam Paninski
Bayesian Learning and Inference in Recurrent Switching Linear Dynamical Systems. AISTATS (2017)
Andrea Giovannucci, Johannes Friedrich, Matt Kaufman, Anne Churchland, Dmitri B. Chklovskii, Liam Paninski, Eftychios A. Pnevmatikakis
OnACID: Online Analysis of Calcium Imaging Data in Real Time. NIPS (2017)
Ari Pakman, Dar Gilboa, David E. Carlson, Liam Paninski
Stochastic Bouncy Particle Sampler. ICML (2017)
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
YASS: Yet Another Spike Sorter. NIPS (2017)
Nikhil Parthasarathy, Eleanor Batty, William Falcon, Thomas Rutten, Mohit Rajpal, E. J. Chichilnisky, Liam Paninski
Neural Networks for Efficient Bayesian Decoding of Natural Images from Retinal Neurons. NIPS (2017)
Gonzalo E. Mena, Lauren E. Grosberg, Sasidhar Madugula, Pawel Hottowy, Alan M. Litke, John P. Cunningham, E. J. Chichilnisky, Liam Paninski
Electrical stimulus artifact cancellation and neural spike detection on large multi-electrode arrays. PLoS Comput. Biol. 13 (11) (2017)
Johannes Friedrich, Pengcheng Zhou, Liam Paninski
Fast online deconvolution of calcium imaging data. PLoS Comput. Biol. 13 (3) (2017)
Eleanor Batty, Josh Merel, Nora Brackbill, Alexander Heitman, Alexander Sher, Alan M. Litke, E. J. Chichilnisky, Liam Paninski
Multilayer Recurrent Network Models of Primate Retinal Ganglion Cell Responses. ICLR (Poster) (2017)
Ruoxi Sun, Evan Archer, Liam Paninski
Scalable Variational Inference for Super Resolution Microscopy. AISTATS (2017)
Johannes Friedrich, Weijian Yang, Daniel Soudry, Yu Mu, Misha Ahrens, Rafael Yuste, Darcy S. Peterka, Liam Paninski
Multi-scale approaches for high-speed imaging and analysis of large neural populations. PLoS Comput. Biol. 13 (8) (2017)
Yuanjun Gao, Evan W. Archer, Liam Paninski, John P. Cunningham
Linear dynamical neural population models through nonlinear embeddings. NIPS (2016)
Josh Merel, David E. Carlson, Liam Paninski, John P. Cunningham
Neuroprosthetic Decoder Training as Imitation Learning. PLoS Comput. Biol. 12 (5) (2016)
Uygar Sümbül, Douglas H. Roossien, Dawen Cai, Fei Chen, Nicholas Barry, John P. Cunningham, Edward S. Boyden, Liam Paninski
Automated scalable segmentation of neurons from multispectral images. NIPS (2016)
David E. Carlson, Patrick Stinson, Ari Pakman, Liam Paninski
Partition Functions from Rao-Blackwellized Tempered Sampling. ICML (2016)
Johannes Friedrich, Liam Paninski
Fast Active Set Methods for Online Spike Inference from Calcium Imaging. NIPS (2016)
Daniel Soudry, Suraj Keshri, Patrick Stinson, Min-hwan Oh, Garud Iyengar, Liam Paninski
Efficient "Shotgun" Inference of Neural Connectivity from Highly Sub-sampled Activity Data. PLoS Comput. Biol. 11 (10) (2015)
Josh Merel, David E. Carlson, Liam Paninski, John P. Cunningham
Neuroprosthetic decoder training as imitation learning. CoRR (2015)
Josh Merel, Donald M. Pianto, John P. Cunningham, Liam Paninski
Encoder-Decoder Optimization for Brain-Computer Interfaces. PLoS Comput. Biol. 11 (6) (2015)
Alexandro D. Ramirez, Liam Paninski
Fast inference in generalized linear models via expected log-likelihoods. J. Comput. Neurosci. 36 (2) (2014)
Ari Pakman, Jonathan Hunter Huggins, Carl Smith, Liam Paninski
Fast state-space methods for inferring dendritic synaptic connectivity. J. Comput. Neurosci. 36 (3) (2014)
Gonzalo E. Mena, Liam Paninski
On Quadrature Methods for Refractory Point Process Likelihoods. Neural Comput. 26 (12) (2014)
Lars Buesing, Timothy A. Machado, John P. Cunningham, Liam Paninski
Clustered factor analysis of multineuronal spike data. NIPS (2014)
Ari Pakman, Liam Paninski
Auxiliary-variable Exact Hamiltonian Monte Carlo Samplers for Binary Distributions. NIPS (2013)
Benjamin Shababo, Brooks Paige, Ari Pakman, Liam Paninski
Bayesian Inference and Online Experimental Design for Mapping Neural Microcircuits. NIPS (2013)
Eftychios A. Pnevmatikakis, Josh Merel, Ari Pakman, Liam Paninski
Bayesian spike inference from calcium imaging data. ACSSC (2013)
Eftychios A. Pnevmatikakis, Liam Paninski
Sparse nonnegative deconvolution for compressive calcium imaging: algorithms and phase transitions. NIPS (2013)
Josh Merel, Roy Fox, Tony Jebara, Liam Paninski
A multi-agent control framework for co-adaptation in brain-computer interfaces. NIPS (2013)
David Pfau, Eftychios A. Pnevmatikakis, Liam Paninski
Robust learning of low-dimensional dynamics from large neural ensembles. NIPS (2013)
Carl Smith, Frank D. Wood, Liam Paninski
Low rank continuous-space graphical models. AISTATS (2012)
Liam Paninski, Michael Vidne, Brian DePasquale, Daniel Gil Ferreira
Inferring synaptic inputs given a noisy voltage trace via sequential Monte Carlo methods. J. Comput. Neurosci. 33 (1) (2012)
Jonathan Hunter Huggins, Liam Paninski
Optimal experimental design for sampling voltage on dendritic trees in the low-SNR regime. J. Comput. Neurosci. 32 (2) (2012)
Eftychios A. Pnevmatikakis, Liam Paninski
Fast interior-point inference in high-dimensional sparse, penalized state-space models. AISTATS (2012)
Kianoush Nazarpour, Christian Ethier, Liam Paninski, James M. Rebesco, R. Chris Miall, Lee E. Miller
EMG Prediction From Motor Cortical Recordings via a Nonnegative Point-Process Filter. IEEE Trans. Biomed. Eng. 59 (7) (2012)
Liam Paninski, Kamiar Rahnama Rad, Michael Vidne
Robust particle filters via sequential pairwise reparameterized Gibbs sampling. CISS (2012)
Yuriy Mishchenko, Liam Paninski
A Bayesian compressed-sensing approach for reconstructing neural connectivity from subsampled anatomical data. J. Comput. Neurosci. 33 (2) (2012)
Yan Tat Wong, Mariana Vigeral, David Putrino, David Pfau, Josh Merel, Liam Paninski, Bijan Pesaran
Decoding arm and hand movements across layers of the macaque frontal cortices. EMBC (2012)
Michael Vidne, Yashar Ahmadian, Jonathon Shlens, Jonathan W. Pillow, Jayant Kulkarni, Alan M. Litke, E. J. Chichilnisky, Eero P. Simoncelli, Liam Paninski
Modeling the impact of common noise inputs on the network activity of retinal ganglion cells. J. Comput. Neurosci. 33 (1) (2012)
Eftychios A. Pnevmatikakis, Keith J. Kelleher, Rebecca Chen, Petter Saggau, Kresimir Josic, Liam Paninski
Fast Spatiotemporal Smoothing of Calcium Measurements in Dendritic Trees. PLoS Comput. Biol. 8 (6) (2012)
Kamiar Rahnama Rad, Liam Paninski
Information Rates and Optimal Decoding in Large Neural Populations. NIPS (2011)
Sean Escola, Alfredo Fontanini, Don Katz, Liam Paninski
Hidden Markov Models for the Stimulus-Response Relationships of Multistate Neural Systems. Neural Comput. 23 (5) (2011)
Jeremy Lewi, David M. Schneider, Sarah M. N. Woolley, Liam Paninski
Automating the design of informative sequences of sensory stimuli. J. Comput. Neurosci. 30 (1) (2011)
Yashar Ahmadian, Jonathan W. Pillow, Liam Paninski
Efficient Markov Chain Monte Carlo Methods for Decoding Neural Spike Trains. Neural Comput. 23 (1) (2011)
Jonathan W. Pillow, Yashar Ahmadian, Liam Paninski
Model-Based Decoding, Information Estimation, and Change-Point Detection Techniques for Multineuron Spike Trains. Neural Comput. 23 (1) (2011)
Liam Paninski, Yashar Ahmadian, Daniel Gil Ferreira, Shinsuke Koyama, Kamiar Rahnama Rad, Michael Vidne, Joshua T. Vogelstein, Wei Wu
A new look at state-space models for neural data. J. Comput. Neurosci. 29 (1-2) (2010)
Liam Paninski
Fast Kalman filtering on quasilinear dendritic trees. J. Comput. Neurosci. 28 (2) (2010)
Shinsuke Koyama, Liam Paninski
Efficient computation of the maximum a posteriori path and parameter estimation in integrate-and-fire and more general state-space models. J. Comput. Neurosci. 29 (1-2) (2010)
Sean Escola, Michael Eisele, Kenneth D. Miller, Liam Paninski
Maximally Reliable Markov Chains Under Energy Constraints. Neural Comput. 21 (7) (2009)
Geoffrey Fudenberg, Liam Paninski
Bayesian Image Recovery for Dendritic Structures Under Low Signal-to-Noise Conditions. IEEE Trans. Image Process. 18 (3) (2009)
Taro Toyoizumi, Kamiar Rahnama Rad, Liam Paninski
Mean-Field Approximations for Coupled Populations of Generalized Linear Model Spiking Neurons with Markov Refractoriness. Neural Comput. 21 (5) (2009)
Jeremy Lewi, Robert J. Butera, Liam Paninski
Sequential Optimal Design of Neurophysiology Experiments. Neural Comput. 21 (3) (2009)
Quentin J. M. Huys, Liam Paninski
Smoothing of, and Parameter Estimation from, Noisy Biophysical Recordings. PLoS Comput. Biol. 5 (5) (2009)
Jeremy Lewi, Robert J. Butera, David M. Schneider, Sarah M. N. Woolley, Liam Paninski
Designing neurophysiology experiments to optimally constrain receptive field models along parametric submanifolds. NIPS (2008)
Liam Paninski, Adrian Haith, Gábor Szirtes
Integral equation methods for computing likelihoods and their derivatives in the stochastic integrate-and-fire model. J. Comput. Neurosci. 24 (1) (2008)
Liam Paninski
A Coincidence-Based Test for Uniformity Given Very Sparsely Sampled Discrete Data. IEEE Trans. Inf. Theory 54 (10) (2008)
Liam Paninski, Masanao Yajima
Undersmoothed Kernel Entropy Estimators. IEEE Trans. Inf. Theory 54 (9) (2008)
Jayant E. Kulkarni, Liam Paninski
State-Space Decoding of Goal-Directed Movements. IEEE Signal Process. Mag. 25 (1) (2008)
Jeremy Lewi, Robert J. Butera, Liam Paninski
Efficient active learning with generalized linear models. AISTATS (2007)
Jeremy Lewi, Robert J. Butera, Liam Paninski
Real-time adaptive information-theoretic optimization of neurophysiology experiments. NIPS (2006)
Jeremy Lewi, Robert J. Butera, Liam Paninski
Efficient model-based design of neurophysiological experiments. EMBC (2006)
Liam Paninski
The Spike-Triggered Average of the Integrate-and-Fire Cell Driven by Gaussian White Noise. Neural Comput. 18 (11) (2006)
Liam Paninski
The most likely voltage path and large deviations approximations for integrate-and-fire neurons. J. Comput. Neurosci. 21 (1) (2006)
Misha Ahrens, Quentin J. M. Huys, Liam Paninski
Large-scale biophysical parameter estimation in single neurons via constrained linear regression. NIPS (2005)
Liam Paninski
Asymptotic Theory of Information-Theoretic Experimental Design. Neural Comput. 17 (7) (2005)

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Publication Activity (10 Years)

Top Topics

Top Venues

Liam Paninski