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2020 – today
- 2024
[i10]Yizi Zhang, Yanchen Wang, Donato Jimenez-Beneto, Zixuan Wang, Mehdi Azabou, Blake A. Richards, Olivier Winter, International Brain Laboratory, Eva L. Dyer, Liam Paninski, Cole L. Hurwitz:
Towards a "universal translator" for neural dynamics at single-cell, single-spike resolution. CoRR abs/2407.14668 (2024)- [i9]Ari Blau, Evan S. Schaffer
, Neeli Mishra, Nathaniel J. Miska, International Brain Laboratory, Liam Paninski, Matthew R. Whiteway:
A study of animal action segmentation algorithms across supervised, unsupervised, and semi-supervised learning paradigms. CoRR abs/2407.16727 (2024) - 2023
- [c57]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: 1-5 - [c56]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: 1-5 - [c55]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: 332-343 - [c54]Marcus Triplett, Marta Gajowa, Hillel Adesnik, Liam Paninski:
Bayesian target optimisation for high-precision holographic optogenetics. NeurIPS 2023 - [c53]Ankit Vishnubhotla, Charlotte Loh, Akash Srivastava, Liam Paninski, Cole L. Hurwitz:
Towards robust and generalizable representations of extracellular data using contrastive learning. NeurIPS 2023 - [c52]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 - 2022
- [j49]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) - [i8]Ari Blau, Christoph Gebhardt, Andrés Bendesky, Liam Paninski, Anqi Wu:
SemiMultiPose: A Semi-supervised Multi-animal Pose Estimation Framework. CoRR abs/2204.07072 (2022) - 2021
- [j48]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: 234:1-234:36 (2021) - [j47]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): 1719-1750 (2021) - [j46]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) - [j45]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) - [c51]Erdem Varol, Julien Boussard, Nishchal Dethe, Olivier Winter, Anne E. Urai, International Brain Laboratory, Anne Churchland, Nick Steinmetz, Liam Paninski:
Decentralized Motion Inference and Registration of Neuropixel Data. ICASSP 2021: 1085-1089 - [c50]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: 466-475 - [c49]Julien Boussard, Erdem Varol, Hyun Dong Lee, Nishchal Dethe, Liam Paninski:
Three-dimensional spike localization and improved motion correction for Neuropixels recordings. NeurIPS 2021: 22095-22105 - 2020
- [j44]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) - [c48]Ari Pakman, Yueqi Wang, Catalin Mitelut, Jin Hyung Lee, Liam Paninski:
Neural Clustering Processes. ICML 2020: 7455-7465 - [c47]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: 14-24 - [c46]Erdem Varol, Amin Nejatbakhsh, Ruoxi Sun, Gonzalo E. Mena, Eviatar Yemini, Oliver Hobert, Liam Paninski:
Statistical Atlas of C. elegans Neurons. MICCAI (5) 2020: 119-129 - [c45]Amin Nejatbakhsh, Erdem Varol, Eviatar Yemini, Oliver Hobert, Liam Paninski:
Probabilistic Joint Segmentation and Labeling of C. elegans Neurons. MICCAI (5) 2020: 130-140 - [c44]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 - [c43]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:
Deep Graph Pose: a semi-supervised deep graphical model for improved animal pose tracking. NeurIPS 2020 - [c42]Ding Zhou, Yuanjun Gao, Liam Paninski:
Disentangled Sticky Hierarchical Dirichlet Process Hidden Markov Model. ECML/PKDD (1) 2020: 612-627 - [i7]Jackson Loper, David M. Blei, John P. Cunningham, Liam Paninski:
General linear-time inference for Gaussian Processes on one dimension. CoRR abs/2003.05554 (2020) - [i6]Ding Zhou, Yuanjun Gao, Liam Paninski:
Disentangled sticky hierarchical Dirichlet process hidden Markov model. CoRR abs/2004.03019 (2020) - [i5]Ari Pakman, Yueqi Wang, Yoonho Lee, Pallab Basu, Juho Lee, Yee Whye Teh, Liam Paninski:
Attentive Clustering Processes. CoRR abs/2010.15727 (2020)
2010 – 2019
- 2019
- [c41]Gonzalo E. Mena, Erdem Varol, Amin Nejatbakhsh, Eviatar Yemini, Liam Paninski:
Sinkhorn Permutation Variational Marginal Inference. AABI 2019: 1-9 - [c40]Ari Pakman, Yueqi Wang, Liam Paninski:
Neural Permutation Processes. AABI 2019: 1-7 - [c39]Ruoxi Sun, Scott W. Linderman, Ian Kinsella, Liam Paninski:
Scalable Bayesian inference of dendritic voltage via spatiotemporal recurrent state space models. NeurIPS 2019: 10165-10174 - [c38]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: 14421-14431 - [c37]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:
BehaveNet: nonlinear embedding and Bayesian neural decoding of behavioral videos. NeurIPS 2019: 15680-15691 - [i4]Ari Pakman, Liam Paninski:
Discrete Neural Processes. CoRR abs/1901.00409 (2019) - 2018
- [j43]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:
Community-based benchmarking improves spike rate inference from two-photon calcium imaging data. PLoS Comput. Biol. 14(5) (2018) - [c36]Scott W. Linderman, Gonzalo E. Mena, Hal James Cooper, Liam Paninski, John P. Cunningham:
Reparameterizing the Birkhoff Polytope for Variational Permutation Inference. AISTATS 2018: 1618-1627 - [c35]Ruoxi Sun, Liam Paninski:
Scalable Approximate Bayesian Inference for Particle Tracking Data. ICML 2018: 4807-4816 - [i3]Daniel Hernández, Antonio Khalil Moretti, Ziqiang Wei, Shreya Saxena, John P. Cunningham, Liam Paninski:
A Novel Variational Family for Hidden Nonlinear Markov Models. CoRR abs/1811.02459 (2018) - [i2]Ari Pakman, Liam Paninski:
Amortized Bayesian inference for clustering models. CoRR abs/1811.09747 (2018) - 2017
- [j42]Johannes Friedrich, Pengcheng Zhou, Liam Paninski:
Fast online deconvolution of calcium imaging data. PLoS Comput. Biol. 13(3) (2017) - [j41]Johannes Friedrich, Weijian Yang, Daniel Soudry, Yu Mu, Misha B. 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) - [j40]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) - [c34]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: 914-922 - [c33]Ruoxi Sun, Evan Archer, Liam Paninski:
Scalable Variational Inference for Super Resolution Microscopy. AISTATS 2017: 1057-1065 - [c32]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 - [c31]Ari Pakman, Dar Gilboa, David E. Carlson, Liam Paninski:
Stochastic Bouncy Particle Sampler. ICML 2017: 2741-2750 - [c30]Andrea Giovannucci, Johannes Friedrich, Matthew T. Kaufman, Anne Churchland, Dmitri B. Chklovskii, Liam Paninski, Eftychios A. Pnevmatikakis:
OnACID: Online Analysis of Calcium Imaging Data in Real Time. NIPS 2017: 2381-2391 - [c29]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: 4002-4012 - [c28]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: 6434-6445 - 2016
- [j39]Josh Merel, David E. Carlson, Liam Paninski, John P. Cunningham:
Neuroprosthetic Decoder Training as Imitation Learning. PLoS Comput. Biol. 12(5) (2016) - [c27]David E. Carlson, Patrick Stinson, Ari Pakman, Liam Paninski:
Partition Functions from Rao-Blackwellized Tempered Sampling. ICML 2016: 2896-2905 - [c26]Yuanjun Gao, Evan W. Archer, Liam Paninski, John P. Cunningham:
Linear dynamical neural population models through nonlinear embeddings. NIPS 2016: 163-171 - [c25]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: 1912-1920 - [c24]Johannes Friedrich, Liam Paninski:
Fast Active Set Methods for Online Spike Inference from Calcium Imaging. NIPS 2016: 1984-1992 - 2015
- [j38]Josh Merel, Donald M. Pianto, John P. Cunningham, Liam Paninski:
Encoder-Decoder Optimization for Brain-Computer Interfaces. PLoS Comput. Biol. 11(6) (2015) - [j37]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) - [i1]Josh Merel, David E. Carlson, Liam Paninski, John P. Cunningham:
Neuroprosthetic decoder training as imitation learning. CoRR abs/1511.04156 (2015) - 2014
- [j36]Alexandro D. Ramirez, Liam Paninski:
Fast inference in generalized linear models via expected log-likelihoods. J. Comput. Neurosci. 36(2): 215-234 (2014) - [j35]Ari Pakman, Jonathan Hunter Huggins, Carl Smith, Liam Paninski:
Fast state-space methods for inferring dendritic synaptic connectivity. J. Comput. Neurosci. 36(3): 415-443 (2014) - [j34]Gonzalo E. Mena, Liam Paninski:
On Quadrature Methods for Refractory Point Process Likelihoods. Neural Comput. 26(12): 2790-2797 (2014) - [c23]Lars Buesing, Timothy A. Machado, John P. Cunningham, Liam Paninski:
Clustered factor analysis of multineuronal spike data. NIPS 2014: 3500-3508 - 2013
- [c22]Eftychios A. Pnevmatikakis, Josh Merel, Ari Pakman, Liam Paninski:
Bayesian spike inference from calcium imaging data. ACSSC 2013: 349-353 - [c21]Eftychios A. Pnevmatikakis, Liam Paninski:
Sparse nonnegative deconvolution for compressive calcium imaging: algorithms and phase transitions. NIPS 2013: 1250-1258 - [c20]Benjamin Shababo, Brooks Paige, Ari Pakman, Liam Paninski:
Bayesian Inference and Online Experimental Design for Mapping Neural Microcircuits. NIPS 2013: 1304-1312 - [c19]David Pfau, Eftychios A. Pnevmatikakis, Liam Paninski:
Robust learning of low-dimensional dynamics from large neural ensembles. NIPS 2013: 2391-2399 - [c18]Ari Pakman, Liam Paninski:
Auxiliary-variable Exact Hamiltonian Monte Carlo Samplers for Binary Distributions. NIPS 2013: 2490-2498 - [c17]Josh Merel, Roy Fox, Tony Jebara, Liam Paninski:
A multi-agent control framework for co-adaptation in brain-computer interfaces. NIPS 2013: 2841-2849 - 2012
- [j33]Jonathan Hunter Huggins, Liam Paninski:
Optimal experimental design for sampling voltage on dendritic trees in the low-SNR regime. J. Comput. Neurosci. 32(2): 347-366 (2012) - [j32]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): 1-19 (2012) - [j31]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): 97-121 (2012) - [j30]Yuriy Mishchenko, Liam Paninski:
A Bayesian compressed-sensing approach for reconstructing neural connectivity from subsampled anatomical data. J. Comput. Neurosci. 33(2): 371-388 (2012) - [j29]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) - [j28]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): 1829-1838 (2012) - [c16]Liam Paninski, Kamiar Rahnama Rad, Michael Vidne:
Robust particle filters via sequential pairwise reparameterized Gibbs sampling. CISS 2012: 1-6 - [c15]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: 1757-1760 - [c14]Eftychios A. Pnevmatikakis, Liam Paninski:
Fast interior-point inference in high-dimensional sparse, penalized state-space models. AISTATS 2012: 895-904 - [c13]Carl Smith, Frank D. Wood, Liam Paninski:
Low rank continuous-space graphical models. AISTATS 2012: 1064-1072 - 2011
- [j27]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): 181-200 (2011) - [j26]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): 1-45 (2011) - [j25]Yashar Ahmadian, Jonathan W. Pillow, Liam Paninski:
Efficient Markov Chain Monte Carlo Methods for Decoding Neural Spike Trains. Neural Comput. 23(1): 46-96 (2011) - [j24]G. Sean Escola, Alfredo Fontanini, Donald B. Katz, Liam Paninski:
Hidden Markov Models for the Stimulus-Response Relationships of Multistate Neural Systems. Neural Comput. 23(5): 1071-1132 (2011) - [c12]Kamiar Rahnama Rad, Liam Paninski:
Information Rates and Optimal Decoding in Large Neural Populations. NIPS 2011: 846-854 - 2010
- [j23]Liam Paninski:
Fast Kalman filtering on quasilinear dendritic trees. J. Comput. Neurosci. 28(2): 211-228 (2010) - [j22]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): 89-105 (2010) - [j21]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): 107-126 (2010)
2000 – 2009
- 2009
- [j20]Jeremy Lewi, Robert J. Butera, Liam Paninski:
Sequential Optimal Design of Neurophysiology Experiments. Neural Comput. 21(3): 619-687 (2009) - [j19]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): 1203-1243 (2009) - [j18]G. Sean Escola, Michael Eisele, Kenneth D. Miller, Liam Paninski:
Maximally Reliable Markov Chains Under Energy Constraints. Neural Comput. 21(7): 1863-1912 (2009) - [j17]Quentin J. M. Huys, Liam Paninski:
Smoothing of, and Parameter Estimation from, Noisy Biophysical Recordings. PLoS Comput. Biol. 5(5) (2009) - [j16]Geoffrey Fudenberg, Liam Paninski:
Bayesian Image Recovery for Dendritic Structures Under Low Signal-to-Noise Conditions. IEEE Trans. Image Process. 18(3): 471-482 (2009) - 2008
- [j15]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): 69-79 (2008) - [j14]Jayant E. Kulkarni, Liam Paninski:
State-Space Decoding of Goal-Directed Movements. IEEE Signal Process. Mag. 25(1): 78-86 (2008) - [j13]Liam Paninski, Masanao Yajima:
Undersmoothed Kernel Entropy Estimators. IEEE Trans. Inf. Theory 54(9): 4384-4388 (2008) - [j12]Liam Paninski:
A Coincidence-Based Test for Uniformity Given Very Sparsely Sampled Discrete Data. IEEE Trans. Inf. Theory 54(10): 4750-4755 (2008) - [c11]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: 945-952 - 2007
- [c10]Jeremy Lewi, Robert J. Butera, Liam Paninski:
Efficient active learning with generalized linear models. AISTATS 2007: 267-274 - 2006
- [j11]Liam Paninski:
The most likely voltage path and large deviations approximations for integrate-and-fire neurons. J. Comput. Neurosci. 21(1): 71-87 (2006) - [j10]Liam Paninski:
The Spike-Triggered Average of the Integrate-and-Fire Cell Driven by Gaussian White Noise. Neural Comput. 18(11): 2592-2616 (2006) - [c9]Jeremy Lewi, Robert J. Butera, Liam Paninski:
Efficient model-based design of neurophysiological experiments. EMBC 2006: 599-602 - [c8]Jeremy Lewi, Robert J. Butera, Liam Paninski:
Real-time adaptive information-theoretic optimization of neurophysiology experiments. NIPS 2006: 857-864 - 2005
- [j9]Liam Paninski, Jonathan W. Pillow, Eero P. Simoncelli:
Comparing integrate-and-fire models estimated using intracellular and extracellular data. Neurocomputing 65-66: 379-385 (2005) - [j8]Liam Paninski:
Asymptotic Theory of Information-Theoretic Experimental Design. Neural Comput. 17(7): 1480-1507 (2005) - [j7]Shy Shoham, Liam Paninski, Matthew Fellows, Nicholas G. Hatsopoulos, John P. Donoghue, Richard A. Normann:
Statistical encoding model for a primary motor cortical brain-machine interface. IEEE Trans. Biomed. Eng. 52(7): 1312-1322 (2005) - [c7]Misha B. Ahrens, Quentin J. M. Huys, Liam Paninski:
Large-scale biophysical parameter estimation in single neurons via constrained linear regression. NIPS 2005: 25-32 - [c6]Liam Paninski:
Nonparametric inference of prior probabilities from Bayes-optimal behavior. NIPS 2005: 1067-1074 - 2004
- [j6]Liam Paninski, Jonathan W. Pillow, Eero P. Simoncelli:
Maximum Likelihood Estimation of a Stochastic Integrate-and-Fire Neural Encoding Model. Neural Comput. 16(12): 2533-2561 (2004) - [j5]Liam Paninski:
Estimating Entropy on m Bins Given Fewer than m Samples. IEEE Trans. Inf. Theory 50(9): 2200-2203 (2004) - [c5]Liam Paninski:
Log-concavity Results on Gaussian Process Methods for Supervised and Unsupervised Learning. NIPS 2004: 1025-1032 - [c4]Liam Paninski:
Variational Minimax Estimation of Discrete Distributions under KL Loss. NIPS 2004: 1033-1040 - 2003
- [j4]Mijail Serruya, Nicholas G. Hatsopoulos, Matthew Fellows, Liam Paninski, John P. Donoghue:
Robustness of neuroprosthetic decoding algorithms. Biol. Cybern. 88(3): 219-228 (2003) - [j3]Liam Paninski, Brian Lau, Alex D. Reyes:
Noise-driven adaptation: in vitro and mathematical analysis. Neurocomputing 52-54: 877-883 (2003) - [j2]Liam Paninski:
Estimation of Entropy and Mutual Information. Neural Comput. 15(6): 1191-1253 (2003) - [c3]Jonathan W. Pillow, Liam Paninski, Eero P. Simoncelli:
Maximum Likelihood Estimation of a Stochastic Integrate-and-Fire Neural Model. NIPS 2003: 1311-1318 - [c2]Liam Paninski:
Design of Experiments via Information Theory. NIPS 2003: 1319-1326 - 2002
- [c1]Liam Paninski:
Convergence Properties of Some Spike-Triggered Analysis Techniques. NIPS 2002: 173-180 - 2001
- [j1]Liam Paninski, Michael J. Hawken:
Stochastic optimal control and the human oculomotor system. Neurocomputing 38-40: 1511-1517 (2001)
Coauthor Index
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