Liam Paninski

Liam Paninski (born 1978) is an American computational neuroscientist who specializes in neural data science. He is a professor in the Departments of Statistics and Neuroscience at Columbia University, where he co-directs the Grossman Center for the Statistics of Mind. Paninski's research focuses on using statistics to decipher electrical signals from the brain. Provided by Wikipedia
Showing 1 - 16 results of 16 for search 'Liam Paninski', query time: 0.05s Refine Results
  1. 1
    Smoothing of, and parameter estimation from, noisy biophysical recordings.

    Smoothing of, and parameter estimation from, noisy biophysical recordings. by Quentin J M Huys, Liam Paninski

    Published 2009-05-01
    Get full text
    Article
  2. 2
    Blind demixing methods for recovering dense neuronal morphology from barcode imaging data.

    Blind demixing methods for recovering dense neuronal morphology from barcode imaging data. by Shuonan Chen, Jackson Loper, Pengcheng Zhou, Liam Paninski

    Published 2022-04-01
    Get full text
    Article
  3. 3
    Neuroprosthetic Decoder Training as Imitation Learning.

    Neuroprosthetic Decoder Training as Imitation Learning. by Josh Merel, David Carlson, Liam Paninski, John P Cunningham

    Published 2016-05-01
    Get full text
    Article
  4. 4
    Fast spatiotemporal smoothing of calcium measurements in dendritic trees.

    Fast spatiotemporal smoothing of calcium measurements in dendritic trees. by Eftychios A Pnevmatikakis, Keith Kelleher, Rebecca Chen, Petter Saggau, Krešimir Josić, Liam Paninski

    Published 2012-06-01
    Get full text
    Article
  5. 5
    Efficient "Shotgun" Inference of Neural Connectivity from Highly Sub-sampled Activity Data.

    Efficient "Shotgun" Inference of Neural Connectivity from Highly Sub-sampled Activity Data. by Daniel Soudry, Suraj Keshri, Patrick Stinson, Min-Hwan Oh, Garud Iyengar, Liam Paninski

    Published 2015-10-01
    Get full text
    Article
  6. 6
    BARcode DEmixing through Non-negative Spatial Regression (BarDensr).

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

    Published 2021-03-01
    Get full text
    Article
  7. 7
    Multi-scale approaches for high-speed imaging and analysis of large neural populations.

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

    Published 2017-08-01
    Get full text
    Article
  8. 8
    Electrical stimulus artifact cancellation and neural spike detection on large multi-electrode arrays.

    Electrical stimulus artifact cancellation and neural spike detection on large multi-electrode arrays. by Gonzalo E Mena, Lauren E Grosberg, Sasidhar Madugula, Paweł Hottowy, Alan Litke, John Cunningham, E J Chichilnisky, Liam Paninski

    Published 2017-11-01
    Get full text
    Article
  9. 9
    Localized semi-nonnegative matrix factorization (LocaNMF) of widefield calcium imaging data.

    Localized semi-nonnegative matrix factorization (LocaNMF) of widefield calcium imaging data. by Shreya Saxena, Ian Kinsella, Simon Musall, Sharon H Kim, Jozsef Meszaros, David N Thibodeaux, Carla Kim, John Cunningham, Elizabeth M C Hillman, Anne Churchland, Liam Paninski

    Published 2020-04-01
    Get full text
    Article
  10. 10
    High-fidelity estimates of spikes and subthreshold waveforms from 1-photon voltage imaging in vivo

    High-fidelity estimates of spikes and subthreshold waveforms from 1-photon voltage imaging in vivo by Michael E. Xie, Yoav Adam, Linlin Z. Fan, Urs L. Böhm, Ian Kinsella, Ding Zhou, Marton Rozsa, Amrita Singh, Karel Svoboda, Liam Paninski, Adam E. Cohen

    Published 2021-04-01
    Get full text
    Article
  11. 11
    Mapping nonlinear receptive field structure in primate retina at single cone resolution

    Mapping nonlinear receptive field structure in primate retina at single cone resolution by Jeremy Freeman, Greg D Field, Peter H Li, Martin Greschner, Deborah E Gunning, Keith Mathieson, Alexander Sher, Alan M Litke, Liam Paninski, Eero P Simoncelli, EJ Chichilnisky

    Published 2015-10-01
    Get full text
    Article
  12. 12
    Complementary networks of cortical somatostatin interneurons enforce layer specific control

    Complementary networks of cortical somatostatin interneurons enforce layer specific control by Alexander Naka, Julia Veit, Ben Shababo, Rebecca K Chance, Davide Risso, David Stafford, Benjamin Snyder, Andrew Egladyous, Desiree Chu, Savitha Sridharan, Daniel P Mossing, Liam Paninski, John Ngai, Hillel Adesnik

    Published 2019-03-01
    Get full text
    Article
  13. 13
    The spatial and temporal structure of neural activity across the fly brain

    The spatial and temporal structure of neural activity across the fly brain by Evan S. Schaffer, Neeli Mishra, Matthew R. Whiteway, Wenze Li, Michelle B. Vancura, Jason Freedman, Kripa B. Patel, Venkatakaushik Voleti, Liam Paninski, Elizabeth M. C. Hillman, L. F. Abbott, Richard Axel

    Published 2023-09-01
    Get full text
    Article
  14. 14
    Efficient and accurate extraction of in vivo calcium signals from microendoscopic video data

    Efficient and accurate extraction of in vivo calcium signals from microendoscopic video data by Pengcheng Zhou, Shanna L Resendez, Jose Rodriguez-Romaguera, Jessica C Jimenez, Shay Q Neufeld, Andrea Giovannucci, Johannes Friedrich, Eftychios A Pnevmatikakis, Garret D Stuber, Rene Hen, Mazen A Kheirbek, Bernardo L Sabatini, Robert E Kass, Liam Paninski

    Published 2018-02-01
    Get full text
    Article
  15. 15
    Partitioning variability in animal behavioral videos using semi-supervised variational autoencoders.

    Partitioning variability in animal behavioral videos using semi-supervised variational autoencoders. by Matthew R Whiteway, Dan Biderman, Yoni Friedman, Mario Dipoppa, E 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

    Published 2021-09-01
    Get full text
    Article
  16. 16
    Community-based benchmarking improves spike rate inference from two-photon calcium imaging data.

    Community-based benchmarking improves spike rate inference from two-photon calcium imaging data. by Philipp Berens, Jeremy Freeman, Thomas Deneux, Nikolay 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

    Published 2018-05-01
    Get full text
    Article

Search Tools: