Stochastic optimal control and the human oculomotor system. - Liam Paninski; Michael J. Hawken (Neurocomputing, 2001)

Convergence Properties of Some Spike-Triggered Analysis Techniques. - Liam Paninski (NIPS, 2002)

Design of Experiments via Information Theory. - Liam Paninski (NIPS, 2003)

Maximum Likelihood Estimation of a Stochastic Integrate-and-Fire Neural Model. - Jonathan W. Pillow; Liam Paninski; Eero P. Simoncelli (NIPS, 2003)

Robustness of neuroprosthetic decoding algorithms. - Mijail Serruya; Nicholas G. Hatsopoulos; Matthew Fellows; Liam Paninski; John P. Donoghue (Biol. Cybern., 2003)

Noise-driven adaptation: in vitro and mathematical analysis. - Liam Paninski; Brian Lau; Alex D. Reyes (Neurocomputing, 2003)

Estimation of Entropy and Mutual Information. - Liam Paninski (Neural Comput., 2003)

Log-concavity Results on Gaussian Process Methods for Supervised and Unsupervised Learning. - Liam Paninski (NIPS, 2004)

Variational Minimax Estimation of Discrete Distributions under KL Loss. - Liam Paninski (NIPS, 2004)

Maximum Likelihood Estimation of a Stochastic Integrate-and-Fire Neural Encoding Model. - Liam Paninski; Jonathan W. Pillow; Eero P. Simoncelli (Neural Comput., 2004)

Estimating Entropy on m Bins Given Fewer than m Samples. - Liam Paninski (IEEE Trans. Inf. Theory, 2004)

Large-scale biophysical parameter estimation in single neurons via constrained linear regression. - Misha B. Ahrens; Quentin J. M. Huys; Liam Paninski (NIPS, 2005)

Nonparametric inference of prior probabilities from Bayes-optimal behavior. - Liam Paninski (NIPS, 2005)

Comparing integrate-and-fire models estimated using intracellular and extracellular data. - Liam Paninski; Jonathan W. Pillow; Eero P. Simoncelli (Neurocomputing, 2005)

Asymptotic Theory of Information-Theoretic Experimental Design. - Liam Paninski (Neural Comput., 2005)

Statistical encoding model for a primary motor cortical brain-machine interface. - Shy Shoham; Liam Paninski; Matthew Fellows; Nicholas G. Hatsopoulos; John P. Donoghue; Richard A. Normann (IEEE Trans. Biomed. Eng., 2005)

Efficient model-based design of neurophysiological experiments. - Jeremy Lewi; Robert J. Butera; Liam Paninski (EMBC, 2006)

Real-time adaptive information-theoretic optimization of neurophysiology experiments. - Jeremy Lewi; Robert J. Butera; Liam Paninski (NIPS, 2006)

The most likely voltage path and large deviations approximations for integrate-and-fire neurons. - Liam Paninski (J. Comput. Neurosci., 2006)

The Spike-Triggered Average of the Integrate-and-Fire Cell Driven by Gaussian White Noise. - Liam Paninski (Neural Comput., 2006)

Efficient active learning with generalized linear models. - Jeremy Lewi; Robert J. Butera; Liam Paninski (AISTATS, 2007)

Designing neurophysiology experiments to optimally constrain receptive field models along parametric submanifolds. - Jeremy Lewi; Robert J. Butera; David M. Schneider; Sarah M. N. Woolley; Liam Paninski (NIPS, 2008)

Integral equation methods for computing likelihoods and their derivatives in the stochastic integrate-and-fire model. - Liam Paninski; Adrian Haith; Gábor Szirtes (J. Comput. Neurosci., 2008)

State-Space Decoding of Goal-Directed Movements. - Jayant E. Kulkarni; Liam Paninski (IEEE Signal Process. Mag., 2008)

A Coincidence-Based Test for Uniformity Given Very Sparsely Sampled Discrete Data. - Liam Paninski (IEEE Trans. Inf. Theory, 2008)

Undersmoothed Kernel Entropy Estimators. - Liam Paninski; Masanao Yajima (IEEE Trans. Inf. Theory, 2008)

Maximally Reliable Markov Chains Under Energy Constraints. - G. Sean Escola; Michael Eisele; Kenneth D. Miller; Liam Paninski (Neural Comput., 2009)

Sequential Optimal Design of Neurophysiology Experiments. - Jeremy Lewi; Robert J. Butera; Liam Paninski (Neural Comput., 2009)

Mean-Field Approximations for Coupled Populations of Generalized Linear Model Spiking Neurons with Markov Refractoriness. - Taro Toyoizumi; Kamiar Rahnama Rad; Liam Paninski (Neural Comput., 2009)

Smoothing of, and Parameter Estimation from, Noisy Biophysical Recordings. - Quentin J. M. Huys; Liam Paninski (PLoS Comput. Biol., 2009)

Bayesian Image Recovery for Dendritic Structures Under Low Signal-to-Noise Conditions. - Geoffrey Fudenberg; Liam Paninski (IEEE Trans. Image Process., 2009)

Efficient computation of the maximum a posteriori path and parameter estimation in integrate-and-fire and more general state-space models. - Shinsuke Koyama; Liam Paninski (J. Comput. Neurosci., 2010)

Fast Kalman filtering on quasilinear dendritic trees. - Liam Paninski (J. Comput. Neurosci., 2010)

A new look at state-space models for neural data. - Liam Paninski; Yashar Ahmadian; Daniel Gil Ferreira; Shinsuke Koyama; Kamiar Rahnama Rad; Michael Vidne; Joshua T. Vogelstein; Wei Wu 006 (J. Comput. Neurosci., 2010)

Information Rates and Optimal Decoding in Large Neural Populations. - Kamiar Rahnama Rad; Liam Paninski (NIPS, 2011)

Automating the design of informative sequences of sensory stimuli. - Jeremy Lewi; David M. Schneider; Sarah M. N. Woolley; Liam Paninski (J. Comput. Neurosci., 2011)

Efficient Markov Chain Monte Carlo Methods for Decoding Neural Spike Trains. - Yashar Ahmadian; Jonathan W. Pillow; Liam Paninski (Neural Comput., 2011)

Hidden Markov Models for the Stimulus-Response Relationships of Multistate Neural Systems. - G. Sean Escola; Alfredo Fontanini; Donald B. Katz; Liam Paninski (Neural Comput., 2011)

Model-Based Decoding, Information Estimation, and Change-Point Detection Techniques for Multineuron Spike Trains. - Jonathan W. Pillow; Yashar Ahmadian; Liam Paninski (Neural Comput., 2011)

Robust particle filters via sequential pairwise reparameterized Gibbs sampling. - Liam Paninski; Kamiar Rahnama Rad; Michael Vidne (CISS, 2012)

Decoding arm and hand movements across layers of the macaque frontal cortices. - Yan Tat Wong; Mariana Vigeral; David Putrino; David Pfau; Josh Merel; Liam Paninski; Bijan Pesaran (EMBC, 2012)

Optimal experimental design for sampling voltage on dendritic trees in the low-SNR regime. - Jonathan Hunter Huggins; Liam Paninski (J. Comput. Neurosci., 2012)

A Bayesian compressed-sensing approach for reconstructing neural connectivity from subsampled anatomical data. - Yuriy Mishchenko; Liam Paninski (J. Comput. Neurosci., 2012)

Inferring synaptic inputs given a noisy voltage trace via sequential Monte Carlo methods. - Liam Paninski; Michael Vidne; Brian DePasquale; Daniel Gil Ferreira (J. Comput. Neurosci., 2012)

Modeling the impact of common noise inputs on the network activity of retinal ganglion cells. - Michael Vidne; Yashar Ahmadian; Jonathon Shlens; Jonathan W. Pillow; Jayant Kulkarni; Alan M. Litke; E. J. Chichilnisky; Eero P. Simoncelli; Liam Paninski (J. Comput. Neurosci., 2012)

Fast interior-point inference in high-dimensional sparse, penalized state-space models. - Eftychios A. Pnevmatikakis; Liam Paninski (AISTATS, 2012)

Low rank continuous-space graphical models. - Carl Smith; Frank D. Wood; Liam Paninski (AISTATS, 2012)

Fast Spatiotemporal Smoothing of Calcium Measurements in Dendritic Trees. - Eftychios A. Pnevmatikakis; Keith J. Kelleher; Rebecca Chen; Petter Saggau; Kresimir Josic; Liam Paninski (PLoS Comput. Biol., 2012)

EMG Prediction From Motor Cortical Recordings via a Nonnegative Point-Process Filter. - Kianoush Nazarpour; Christian Ethier; Liam Paninski; James M. Rebesco; R. Chris Miall; Lee E. Miller (IEEE Trans. Biomed. Eng., 2012)

Bayesian spike inference from calcium imaging data. - Eftychios A. Pnevmatikakis; Josh Merel; Ari Pakman; Liam Paninski (ACSSC, 2013)

A multi-agent control framework for co-adaptation in brain-computer interfaces. - Josh Merel; Roy Fox; Tony Jebara; Liam Paninski (NIPS, 2013)

Auxiliary-variable Exact Hamiltonian Monte Carlo Samplers for Binary Distributions. - Ari Pakman; Liam Paninski (NIPS, 2013)

Robust learning of low-dimensional dynamics from large neural ensembles. - David Pfau; Eftychios A. Pnevmatikakis; Liam Paninski (NIPS, 2013)

Sparse nonnegative deconvolution for compressive calcium imaging: algorithms and phase transitions. - Eftychios A. Pnevmatikakis; Liam Paninski (NIPS, 2013)

Bayesian Inference and Online Experimental Design for Mapping Neural Microcircuits. - Benjamin Shababo; Brooks Paige; Ari Pakman; Liam Paninski (NIPS, 2013)

Clustered factor analysis of multineuronal spike data. - Lars Buesing; Timothy A. Machado; John P. Cunningham; Liam Paninski (NIPS, 2014)

Fast state-space methods for inferring dendritic synaptic connectivity. - Ari Pakman; Jonathan Hunter Huggins; Carl Smith; Liam Paninski (J. Comput. Neurosci., 2014)

Fast inference in generalized linear models via expected log-likelihoods. - Alexandro D. Ramirez; Liam Paninski (J. Comput. Neurosci., 2014)

On Quadrature Methods for Refractory Point Process Likelihoods. - Gonzalo E. Mena; Liam Paninski (Neural Comput., 2014)

Encoder-Decoder Optimization for Brain-Computer Interfaces. - Josh Merel; Donald M. Pianto; John P. Cunningham; Liam Paninski (PLoS Comput. Biol., 2015)

Efficient "Shotgun" Inference of Neural Connectivity from Highly Sub-sampled Activity Data. - Daniel Soudry; Suraj Keshri; Patrick Stinson; Min-hwan Oh; Garud Iyengar; Liam Paninski (PLoS Comput. Biol., 2015)

Partition Functions from Rao-Blackwellized Tempered Sampling. - David E. Carlson; Patrick Stinson; Ari Pakman; Liam Paninski (ICML, 2016)

Fast Active Set Methods for Online Spike Inference from Calcium Imaging. - Johannes Friedrich; Liam Paninski (NIPS, 2016)

Linear dynamical neural population models through nonlinear embeddings. - Yuanjun Gao; Evan W. Archer; Liam Paninski; John P. Cunningham (NIPS, 2016)

Automated scalable segmentation of neurons from multispectral images. - Uygar Sümbül; Douglas H. Roossien; Dawen Cai; Fei Chen; Nicholas Barry; John P. Cunningham; Edward S. Boyden; Liam Paninski (NIPS, 2016)

Neuroprosthetic Decoder Training as Imitation Learning. - Josh Merel; David E. Carlson; Liam Paninski; John P. Cunningham (PLoS Comput. Biol., 2016)

Bayesian Learning and Inference in Recurrent Switching Linear Dynamical Systems. - Scott W. Linderman; Matthew J. Johnson 002; Andrew C. Miller; Ryan P. Adams; David M. Blei; Liam Paninski (AISTATS, 2017)

Scalable Variational Inference for Super Resolution Microscopy. - Ruoxi Sun 006; Evan Archer; Liam Paninski (AISTATS, 2017)

Multilayer Recurrent Network Models of Primate Retinal Ganglion Cell Responses. - Eleanor Batty; Josh Merel; Nora Brackbill; Alexander Heitman; Alexander Sher; Alan M. Litke; E. J. Chichilnisky; Liam Paninski (ICLR (Poster), 2017)

Stochastic Bouncy Particle Sampler. - Ari Pakman; Dar Gilboa; David E. Carlson; Liam Paninski (ICML, 2017)

OnACID: Online Analysis of Calcium Imaging Data in Real Time. - Andrea Giovannucci; Johannes Friedrich; Matthew T. Kaufman; Anne Churchland; Dmitri B. Chklovskii; Liam Paninski; Eftychios A. Pnevmatikakis (NIPS, 2017)

YASS: Yet Another Spike Sorter. - 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 (NIPS, 2017)

Neural Networks for Efficient Bayesian Decoding of Natural Images from Retinal Neurons. - Nikhil Parthasarathy; Eleanor Batty; William Falcon; Thomas Rutten; Mohit Rajpal; E. J. Chichilnisky; Liam Paninski (NIPS, 2017)

Multi-scale approaches for high-speed imaging and analysis of large neural populations. - Johannes Friedrich; Weijian Yang; Daniel Soudry; Yu Mu; Misha B. Ahrens; Rafael Yuste; Darcy S. Peterka; Liam Paninski (PLoS Comput. Biol., 2017)

Fast online deconvolution of calcium imaging data. - Johannes Friedrich; Pengcheng Zhou; Liam Paninski (PLoS Comput. Biol., 2017)

Electrical stimulus artifact cancellation and neural spike detection on large multi-electrode arrays. - Gonzalo E. Mena; Lauren E. Grosberg; Sasidhar Madugula; Pawel Hottowy; Alan M. Litke; John P. Cunningham; E. J. Chichilnisky; Liam Paninski (PLoS Comput. Biol., 2017)

Reparameterizing the Birkhoff Polytope for Variational Permutation Inference. - Scott W. Linderman; Gonzalo E. Mena; Hal James Cooper; Liam Paninski; John P. Cunningham (AISTATS, 2018)

Scalable Approximate Bayesian Inference for Particle Tracking Data. - Ruoxi Sun 006; Liam Paninski (ICML, 2018)

A Novel Variational Family for Hidden Nonlinear Markov Models. - Daniel Hernández 010; Antonio Khalil Moretti; Ziqiang Wei; Shreya Saxena; John P. Cunningham; Liam Paninski (CoRR, 2018)

Amortized Bayesian inference for clustering models. - Ari Pakman; Liam Paninski (CoRR, 2018)

Community-based benchmarking improves spike rate inference from two-photon calcium imaging data. - 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; et al. (PLoS Comput. Biol., 2018)

Sinkhorn Permutation Variational Marginal Inference. - Gonzalo E. Mena; Erdem Varol; Amin Nejatbakhsh; Eviatar Yemini; Liam Paninski (AABI, 2019)

Neural Permutation Processes. - Ari Pakman; Yueqi Wang; Liam Paninski (AABI, 2019)

BehaveNet: nonlinear embedding and Bayesian neural decoding of behavioral videos. - 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; et al. (NeurIPS, 2019)

Efficient characterization of electrically evoked responses for neural interfaces. - Nishal P. Shah; Sasidhar Madugula; Pawel Hottowy; Alexander Sher; Alan M. Litke; Liam Paninski; E. J. Chichilnisky (NeurIPS, 2019)

Scalable Bayesian inference of dendritic voltage via spatiotemporal recurrent state space models. - Ruoxi Sun 006; Scott W. Linderman; Ian Kinsella; Liam Paninski (NeurIPS, 2019)

Discrete Neural Processes. - Ari Pakman; Liam Paninski (CoRR, 2019)

Neural Clustering Processes. - Ari Pakman; Yueqi Wang; Catalin Mitelut; Jin Hyung Lee; Liam Paninski (ICML, 2020)

Probabilistic Joint Segmentation and Labeling of C. elegans Neurons. - Amin Nejatbakhsh; Erdem Varol; Eviatar Yemini; Oliver Hobert; Liam Paninski (MICCAI (5), 2020)

Demixing Calcium Imaging Data in C. elegans via Deformable Non-negative Matrix Factorization. - Amin Nejatbakhsh; Erdem Varol; Eviatar Yemini; Vivek Venkatachalam; Albert Lin; Aravinthan D. T. Samuel; Oliver Hobert; Liam Paninski (MICCAI (5), 2020)

Statistical Atlas of C. elegans Neurons. - Erdem Varol; Amin Nejatbakhsh; Ruoxi Sun 006; Gonzalo E. Mena; Eviatar Yemini; Oliver Hobert; Liam Paninski (MICCAI (5), 2020)

Recurrent Switching Dynamical Systems Models for Multiple Interacting Neural Populations. - Joshua I. Glaser; Matthew R. Whiteway; John P. Cunningham; Liam Paninski; Scott W. Linderman (NeurIPS, 2020)

Deep Graph Pose: a semi-supervised deep graphical model for improved animal pose tracking. - 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; et al. (NeurIPS, 2020)

Disentangled Sticky Hierarchical Dirichlet Process Hidden Markov Model. - Ding Zhou; Yuanjun Gao; Liam Paninski (ECML/PKDD (1), 2020)

General linear-time inference for Gaussian Processes on one dimension. - Jackson Loper; David M. Blei; John P. Cunningham; Liam Paninski (CoRR, 2020)

Attentive Clustering Processes. - Ari Pakman; Yueqi Wang; Yoonho Lee 001; Pallab Basu; Juho Lee 001; Yee Whye Teh; Liam Paninski (CoRR, 2020)

Localized semi-nonnegative matrix factorization (LocaNMF) of widefield calcium imaging data. - 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 (PLoS Comput. Biol., 2020)

Decentralized Motion Inference and Registration of Neuropixel Data. - Erdem Varol; Julien Boussard; Nishchal Dethe; Olivier Winter; Anne E. Urai; International Brain Laboratory; Anne Churchland; Nick Steinmetz; Liam Paninski (ICASSP, 2021)

Non-parametric Vignetting Correction for Sparse Spatial Transcriptomics Images. - Bovey Y. Rao; Alexis M. Peterson; Elena K. Kandror; Stephanie Herrlinger; Attila Losonczy; Liam Paninski; Abbas H. Rizvi; Erdem Varol (MICCAI (8), 2021)

Three-dimensional spike localization and improved motion correction for Neuropixels recordings. - Julien Boussard; Erdem Varol; Hyun Dong Lee; Nishchal Dethe; Liam Paninski (NeurIPS, 2021)

A general linear-time inference method for Gaussian Processes on one dimension. - Jackson Loper; David M. Blei; John P. Cunningham; Liam Paninski (J. Mach. Learn. Res., 2021)

Nonlinear Decoding of Natural Images From Large-Scale Primate Retinal Ganglion Recordings. - Young Joon Kim; Nora Brackbill; Eleanor Batty; Jin Hyung Lee; Catalin Mitelut; William Tong; E. J. Chichilnisky; Liam Paninski (Neural Comput., 2021)

BARcode DEmixing through Non-negative Spatial Regression (BarDensr). - Shuonan Chen; Jackson Loper; Xiaoyin Chen; Alex Vaughan; Anthony M. Zador; Liam Paninski (PLoS Comput. Biol., 2021)

Partitioning variability in animal behavioral videos using semi-supervised variational autoencoders. - 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; et al. (PLoS Comput. Biol., 2021)

SemiMultiPose: A Semi-supervised Multi-animal Pose Estimation Framework. - Ari Blau; Christoph Gebhardt; Andrés Bendesky; Liam Paninski; Anqi Wu (CoRR, 2022)

Blind demixing methods for recovering dense neuronal morphology from barcode imaging data. - Shuonan Chen; Jackson Loper; Pengcheng Zhou; Liam Paninski (PLoS Comput. Biol., 2022)

Multimodal Microscopy Image Alignment Using Spatial and Shape Information and a Branch-and-Bound Algorithm. - Shuonan Chen; Bovey Y. Rao; Stephanie Herrlinger; Attila Losonczy; Liam Paninski; Erdem Varol (ICASSP, 2023)

Robust Online Multiband Drift Estimation in Electrophysiology Data. - 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; et al. (ICASSP, 2023)

Learning Probabilistic Piecewise Rigid Atlases of Model Organisms via Generative Deep Networks. - Amin Nejatbakhsh; Neel Dey; Vivek Venkatachalam; Eviatar Yemini; Liam Paninski; Erdem Varol (IPMI, 2023)

Bayesian target optimisation for high-precision holographic optogenetics. - Marcus A. Triplett; Marta Gajowa; Hillel Adesnik; Liam Paninski (NeurIPS, 2023)

Towards robust and generalizable representations of extracellular data using contrastive learning. - Ankit Vishnubhotla; Charlotte Loh; Akash Srivastava; Liam Paninski; Cole L. Hurwitz (NeurIPS, 2023)

Bypassing spike sorting: Density-based decoding using spike localization from dense multielectrode probes. - 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; et al. (NeurIPS, 2023)

Towards a "Universal Translator" for Neural Dynamics at Single-Cell, Single-Spike Resolution. - 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 (NeurIPS, 2024)

Towards a "universal translator" for neural dynamics at single-cell, single-spike resolution. - 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 (CoRR, 2024)

A study of animal action segmentation algorithms across supervised, unsupervised, and semi-supervised learning paradigms. - Ari Blau; Evan S. Schaffer; Neeli Mishra; Nathaniel J. Miska; International Brain Laboratory; Liam Paninski; Matthew R. Whiteway (CoRR, 2024)

Removing direct photocurrent artifacts in optogenetic connectivity mapping data via constrained matrix factorization. - Benjamin Antin; Masato Sadahiro; Marta Gajowa; Marcus A. Triplett; Hillel Adesnik; Liam Paninski (PLoS Comput. Biol., 2024)

Versatile multiple object tracking in sparse 2D/3D videos via deformable image registration. - James Ryu; Amin Nejatbakhsh; Mahdi Torkashvand; Sahana Gangadharan; Maedeh Seyedolmohadesin; Jinmahn Kim; Liam Paninski; Vivek Venkatachalam (PLoS Comput. Biol., 2024)

In vivo cell-type and brain region classification via multimodal contrastive learning. - 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; et al. (ICLR, 2025)

Neural Encoding and Decoding at Scale. - Yizi Zhang; Yanchen Wang; Mehdi Azabou; Alexandre Andre; Zixuan Wang; Hanrui Lyu; International Brain Laboratory; Eva L. Dyer; Liam Paninski; Cole L. Hurwitz (CoRR, 2025)

Self-supervised pretraining of vision transformers for animal behavioral analysis and neural encoding. - Yanchen Wang; Han Yu; Ari Blau; Yizi Zhang; International Brain Laboratory; Liam Paninski; Cole L. Hurwitz; Matthew R. Whiteway (CoRR, 2025)