| Year |
Citation |
Score |
| 2024 |
Zimnik AJ, Ames KC, An X, Driscoll L, Lara AH, Russo AA, Susoy V, Cunningham JP, Paninski L, Churchland MM, Glaser JI. Identifying Interpretable Latent Factors with Sparse Component Analysis. Biorxiv : the Preprint Server For Biology. PMID 38370650 DOI: 10.1101/2024.02.05.578988 |
0.52 |
|
| 2023 |
Zhang Y, He T, Boussard J, Windolf C, Winter O, Trautmann E, Roth N, Barrell H, Churchland M, Steinmetz NA, Varol E, Hurwitz C, Paninski L. Bypassing spike sorting: Density-based decoding using spike localization from dense multielectrode probes. Biorxiv : the Preprint Server For Biology. PMID 37790422 DOI: 10.1101/2023.09.21.558869 |
0.318 |
|
| 2023 |
Pasarkar A, Kinsella I, Zhou P, Wu M, Pan D, Fan JL, Wang Z, Abdeladim L, Peterka DS, Adesnik H, Ji N, Paninski L. maskNMF: A denoise-sparsen-detect approach for extracting neural signals from dense imaging data. Biorxiv : the Preprint Server For Biology. PMID 37745388 DOI: 10.1101/2023.09.14.557777 |
0.581 |
|
| 2023 |
Ye Z, Shelton AM, Shaker JR, Boussard J, Colonell J, Manavi S, Chen S, Windolf C, Hurwitz C, Namima T, Pedraja F, Weiss S, Raducanu B, Ness TV, Einevoll GT, ... ... Paninski L, et al. Ultra-high density electrodes improve detection, yield, and cell type specificity of brain recordings. Biorxiv : the Preprint Server For Biology. PMID 37662298 DOI: 10.1101/2023.08.23.554527 |
0.338 |
|
| 2023 |
Biderman D, Whiteway MR, Hurwitz C, Greenspan N, Lee RS, Vishnubhotla A, Warren R, Pedraja F, Noone D, Schartner M, Huntenburg JM, Khanal A, Meijer GT, Noel JP, Pan-Vazquez A, ... ... Paninski L, et al. Lightning Pose: improved animal pose estimation via semi-supervised learning, Bayesian ensembling, and cloud-native open-source tools. Biorxiv : the Preprint Server For Biology. PMID 37162966 DOI: 10.1101/2023.04.28.538703 |
0.52 |
|
| 2022 |
Abe T, Kinsella I, Saxena S, Buchanan EK, Couto J, Briggs J, Kitt SL, Glassman R, Zhou J, Paninski L, Cunningham JP. Neuroscience Cloud Analysis As a Service: An open-source platform for scalable, reproducible data analysis. Neuron. PMID 35870448 DOI: 10.1016/j.neuron.2022.06.018 |
0.439 |
|
| 2022 |
Chen S, Loper J, Zhou P, Paninski L. Blind demixing methods for recovering dense neuronal morphology from barcode imaging data. Plos Computational Biology. 18: e1009991. PMID 35395020 DOI: 10.1371/journal.pcbi.1009991 |
0.583 |
|
| 2022 |
Turner NL, Macrina T, Bae JA, Yang R, Wilson AM, Schneider-Mizell C, Lee K, Lu R, Wu J, Bodor AL, Bleckert AA, Brittain D, Froudarakis E, Dorkenwald S, Collman F, ... ... Paninski L, et al. Reconstruction of neocortex: Organelles, compartments, cells, circuits, and activity. Cell. PMID 35216674 DOI: 10.1016/j.cell.2022.01.023 |
0.695 |
|
| 2021 |
Whiteway MR, Biderman D, Friedman Y, Dipoppa M, Buchanan EK, Wu A, Zhou J, Bonacchi N, Miska NJ, Noel JP, Rodriguez E, Schartner M, Socha K, Urai AE, Salzman CD, ... ... Paninski L, et al. Partitioning variability in animal behavioral videos using semi-supervised variational autoencoders. Plos Computational Biology. 17: e1009439. PMID 34550974 DOI: 10.1371/journal.pcbi.1009439 |
0.607 |
|
| 2021 |
Kim YJ, Brackbill N, Batty E, Lee J, Mitelut C, Tong W, Chichilnisky EJ, Paninski L. Nonlinear Decoding of Natural Images From Large-Scale Primate Retinal Ganglion Recordings. Neural Computation. 33: 1719-1750. PMID 34411268 DOI: 10.1162/neco_a_01395 |
0.763 |
|
| 2021 |
Couto J, Musall S, Sun XR, Khanal A, Gluf S, Saxena S, Kinsella I, Abe T, Cunningham JP, Paninski L, Churchland AK. Chronic, cortex-wide imaging of specific cell populations during behavior. Nature Protocols. PMID 34075229 DOI: 10.1038/s41596-021-00527-z |
0.517 |
|
| 2020 |
Yemini E, Lin A, Nejatbakhsh A, Varol E, Sun R, Mena GE, Samuel ADT, Paninski L, Venkatachalam V, Hobert O. NeuroPAL: A Multicolor Atlas for Whole-Brain Neuronal Identification in C. elegans. Cell. PMID 33378642 DOI: 10.1016/j.cell.2020.12.012 |
0.799 |
|
| 2020 |
Saxena S, Kinsella I, Musall S, Kim SH, Meszaros J, Thibodeaux DN, Kim C, Cunningham J, Hillman EMC, Churchland A, Paninski L. Localized semi-nonnegative matrix factorization (LocaNMF) of widefield calcium imaging data. Plos Computational Biology. 16: e1007791. PMID 32282806 DOI: 10.1371/Journal.Pcbi.1007791 |
0.569 |
|
| 2020 |
Lu R, Liang Y, Meng G, Zhou P, Svoboda K, Paninski L, Ji N. Rapid mesoscale volumetric imaging of neural activity with synaptic resolution. Nature Methods. PMID 32123393 DOI: 10.1038/S41592-020-0760-9 |
0.607 |
|
| 2019 |
Abdelfattah AS, Kawashima T, Singh A, Novak O, Liu H, Shuai Y, Huang YC, Campagnola L, Seeman SC, Yu J, Zheng J, Grimm JB, Patel R, Friedrich J, Mensh BD, ... Paninski L, et al. Bright and photostable chemigenetic indicators for extended in vivo voltage imaging. Science (New York, N.Y.). PMID 31371562 DOI: 10.1126/Science.Aav6416 |
0.371 |
|
| 2019 |
Adam Y, Kim JJ, Lou S, Zhao Y, Xie ME, Brinks D, Wu H, Mostajo-Radji MA, Kheifets S, Parot V, Chettih S, Williams KJ, Gmeiner B, Farhi SL, Madisen L, ... ... Paninski L, et al. Voltage imaging and optogenetics reveal behaviour-dependent changes in hippocampal dynamics. Nature. PMID 31043747 DOI: 10.1038/S41586-019-1166-7 |
0.767 |
|
| 2019 |
Lacefield CO, Pnevmatikakis EA, Paninski L, Bruno RM. Reinforcement Learning Recruits Somata and Apical Dendrites across Layers of Primary Sensory Cortex. Cell Reports. 26: 2000-2008.e2. PMID 30784583 DOI: 10.1016/J.Celrep.2019.01.093 |
0.332 |
|
| 2018 |
Berens P, Freeman J, Deneux T, Chenkov N, McColgan T, Speiser A, Macke JH, Turaga SC, Mineault P, Rupprecht P, Gerhard S, Friedrich RW, Friedrich J, Paninski L, Pachitariu M, et al. Community-based benchmarking improves spike rate inference from two-photon calcium imaging data. Plos Computational Biology. 14: e1006157. PMID 29782491 DOI: 10.1371/Journal.Pcbi.1006157 |
0.78 |
|
| 2018 |
Paninski L, Cunningham JP. Neural data science: accelerating the experiment-analysis-theory cycle in large-scale neuroscience. Current Opinion in Neurobiology. 50: 232-241. PMID 29738986 DOI: 10.1016/J.Conb.2018.04.007 |
0.597 |
|
| 2018 |
Zhou P, Resendez SL, Rodriguez-Romaguera J, Jimenez JC, Neufeld SQ, Giovannucci A, Friedrich J, Pnevmatikakis EA, Stuber GD, Hen R, Kheirbek MA, Sabatini BL, Kass RE, Paninski L. Efficient and accurate extraction ofcalcium signals from microendoscopic video data. Elife. 7. PMID 29469809 DOI: 10.7554/Elife.28728 |
0.732 |
|
| 2018 |
Jimenez JC, Su K, Goldberg AR, Luna VM, Biane JS, Ordek G, Zhou P, Ong SK, Wright MA, Zweifel L, Paninski L, Hen R, Kheirbek MA. Anxiety Cells in a Hippocampal-Hypothalamic Circuit. Neuron. PMID 29397273 DOI: 10.1016/J.Neuron.2018.01.016 |
0.54 |
|
| 2018 |
Zhou P, Resendez SL, Rodriguez-Romaguera J, Jimenez JC, Neufeld SQ, Giovannucci A, Friedrich J, Pnevmatikakis EA, Stuber GD, Hen R, Kheirbek MA, Sabatini BL, Kass RE, Paninski L. Author response: Efficient and accurate extraction of in vivo calcium signals from microendoscopic video data Elife. DOI: 10.7554/Elife.28728.031 |
0.678 |
|
| 2017 |
Yu K, Ahrens S, Zhang X, Schiff H, Ramakrishnan C, Fenno L, Deisseroth K, Zhao F, Luo MH, Gong L, He M, Zhou P, Paninski L, Li B. The central amygdala controls learning in the lateral amygdala. Nature Neuroscience. 20: 1680-1685. PMID 29184202 DOI: 10.1038/S41593-017-0009-9 |
0.638 |
|
| 2017 |
Klaus A, Martins GJ, Paixao VB, Zhou P, Paninski L, Costa RM. The Spatiotemporal Organization of the Striatum Encodes Action Space. Neuron. 96: 949. PMID 29144975 DOI: 10.1016/J.Neuron.2017.10.031 |
0.567 |
|
| 2017 |
Mena GE, Grosberg LE, Madugula S, Hottowy P, Litke A, Cunningham J, Chichilnisky EJ, Paninski L. Electrical stimulus artifact cancellation and neural spike detection on large multi-electrode arrays. Plos Computational Biology. 13: e1005842. PMID 29131818 DOI: 10.1371/Journal.Pcbi.1005842 |
0.789 |
|
| 2017 |
Klaus A, Martins GJ, Paixao VB, Zhou P, Paninski L, Costa RM. The Spatiotemporal Organization of the Striatum Encodes Action Space. Neuron. 95: 1171-1180.e7. PMID 28858619 DOI: 10.1016/j.neuron.2017.08.015 |
0.517 |
|
| 2017 |
Friedrich J, Yang W, Soudry D, Mu Y, Ahrens MB, Yuste R, Peterka DS, Paninski L. Multi-scale approaches for high-speed imaging and analysis of large neural populations. Plos Computational Biology. 13: e1005685. PMID 28771570 DOI: 10.1371/Journal.Pcbi.1005685 |
0.398 |
|
| 2017 |
Giovannucci A, Badura A, Deverett B, Najafi F, Pereira TD, Gao Z, Ozden I, Kloth AD, Pnevmatikakis E, Paninski L, De Zeeuw CI, Medina JF, Wang SS. Cerebellar granule cells acquire a widespread predictive feedback signal during motor learning. Nature Neuroscience. PMID 28319608 DOI: 10.1038/Nn.4531 |
0.361 |
|
| 2017 |
Friedrich J, Zhou P, Paninski L. Fast online deconvolution of calcium imaging data. Plos Computational Biology. 13: e1005423. PMID 28291787 DOI: 10.1371/Journal.Pcbi.1005423 |
0.624 |
|
| 2017 |
Rahnama Rad K, Machado TA, Paninski L. Robust and scalable Bayesian analysis of spatial neural tuning function data The Annals of Applied Statistics. 11: 598-637. DOI: 10.1214/16-AOAS996 |
0.322 |
|
| 2016 |
Sharpee TO, Destexhe A, Kawato M, Sekulić V, Skinner FK, Wójcik DK, Chintaluri C, Cserpán D, Somogyvári Z, Kim JK, Kilpatrick ZP, Bennett MR, Josić K, Elices I, Arroyo D, ... ... Paninski L, et al. 25th Annual Computational Neuroscience Meeting: CNS-2016 Bmc Neuroscience. 17: 54. PMID 27534393 DOI: 10.1186/S12868-016-0283-6 |
0.675 |
|
| 2016 |
Merel J, Shababo B, Naka A, Adesnik H, Paninski L. Bayesian methods for event analysis of intracellular currents. Journal of Neuroscience Methods. PMID 27208694 DOI: 10.1016/j.jneumeth.2016.05.015 |
0.328 |
|
| 2016 |
Picardo MA, Merel J, Katlowitz KA, Vallentin D, Okobi DE, Benezra SE, Clary RC, Pnevmatikakis EA, Paninski L, Long MA. Population-Level Representation of a Temporal Sequence Underlying Song Production in the Zebra Finch. Neuron. 90: 866-76. PMID 27196976 DOI: 10.1016/J.Neuron.2016.02.016 |
0.388 |
|
| 2016 |
Merel J, Carlson D, Paninski L, Cunningham JP. Neuroprosthetic Decoder Training as Imitation Learning. Plos Computational Biology. 12: e1004948. PMID 27191387 DOI: 10.1371/Journal.Pcbi.1004948 |
0.53 |
|
| 2016 |
Gabitto MI, Pakman A, Bikoff JB, Abbott LF, Jessell TM, Paninski L. Bayesian Sparse Regression Analysis Documents the Diversity of Spinal Inhibitory Interneurons. Cell. PMID 26949187 DOI: 10.1016/J.Cell.2016.01.026 |
0.779 |
|
| 2016 |
Pnevmatikakis EA, Soudry D, Gao Y, Machado TA, Merel J, Pfau D, Reardon T, Mu Y, Lacefield C, Yang W, Ahrens M, Bruno R, Jessell TM, Peterka DS, Yuste R, ... Paninski L, et al. Simultaneous Denoising, Deconvolution, and Demixing of Calcium Imaging Data. Neuron. PMID 26774160 DOI: 10.1016/J.Neuron.2015.11.037 |
0.803 |
|
| 2016 |
Yang W, Miller JK, Carrillo-Reid L, Pnevmatikakis E, Paninski L, Yuste R, Peterka DS. Simultaneous Multi-plane Imaging of Neural Circuits. Neuron. PMID 26774159 DOI: 10.1016/J.Neuron.2015.12.012 |
0.426 |
|
| 2016 |
Yang W, Miller JK, Carrillo-Reid L, Pnevmatikakis E, Paninski L, Peterka DS, Yuste R. Two-photon multiplane imaging of neural circuits(Conference Presentation) Proceedings of Spie. 9690: 969016. DOI: 10.1117/12.2219843 |
0.364 |
|
| 2015 |
Freeman J, Field GD, Li PH, Greschner M, Gunning DE, Mathieson K, Sher A, Litke AM, Paninski L, Simoncelli EP, Chichilnisky EJ. Mapping nonlinear receptive field structure in primate retina at single cone resolution. Elife. 4. PMID 26517879 DOI: 10.7554/Elife.05241 |
0.803 |
|
| 2015 |
Soudry D, Keshri S, Stinson P, Oh MH, Iyengar G, Paninski L. Efficient "Shotgun" Inference of Neural Connectivity from Highly Sub-sampled Activity Data. Plos Computational Biology. 11: e1004464. PMID 26465147 DOI: 10.1371/Journal.Pcbi.1004464 |
0.409 |
|
| 2015 |
Machado TA, Pnevmatikakis E, Paninski L, Jessell TM, Miri A. Primacy of Flexor Locomotor Pattern Revealed by Ancestral Reversion of Motor Neuron Identity. Cell. 162: 338-50. PMID 26186188 DOI: 10.1016/J.Cell.2015.06.036 |
0.767 |
|
| 2015 |
Merel J, Pianto DM, Cunningham JP, Paninski L. Encoder-decoder optimization for brain-computer interfaces. Plos Computational Biology. 11: e1004288. PMID 26029919 DOI: 10.1371/Journal.Pcbi.1004288 |
0.527 |
|
| 2015 |
Freeman J, Field GD, Li PH, Greschner M, Gunning DE, Mathieson K, Sher A, Litke AM, Paninski L, Simoncelli EP, Chichilnisky E. Author response: Mapping nonlinear receptive field structure in primate retina at single cone resolution Elife. DOI: 10.7554/Elife.05241.012 |
0.764 |
|
| 2014 |
Mena G, Paninski L. On quadrature methods for refractory point process likelihoods. Neural Computation. 26: 2790-7. PMID 25248082 DOI: 10.1162/Neco_A_00676 |
0.806 |
|
| 2014 |
Ramirez A, Pnevmatikakis EA, Merel J, Paninski L, Miller KD, Bruno RM. Spatiotemporal receptive fields of barrel cortex revealed by reverse correlation of synaptic input. Nature Neuroscience. 17: 866-75. PMID 24836076 DOI: 10.1038/Nn.3720 |
0.582 |
|
| 2014 |
Pakman A, Huggins J, Smith C, Paninski L. Fast state-space methods for inferring dendritic synaptic connectivity. Journal of Computational Neuroscience. 36: 415-43. PMID 24077932 DOI: 10.1007/S10827-013-0478-0 |
0.805 |
|
| 2014 |
Ramirez AD, Paninski L. Fast inference in generalized linear models via expected log-likelihoods. Journal of Computational Neuroscience. 36: 215-34. PMID 23832289 DOI: 10.1007/S10827-013-0466-4 |
0.732 |
|
| 2014 |
Pakman A, Paninski L. Exact Hamiltonian Monte Carlo for truncated multivariate gaussians Journal of Computational and Graphical Statistics. 23: 518-542. DOI: 10.1080/10618600.2013.788448 |
0.728 |
|
| 2014 |
Buesing L, Machado TA, Cunningham JP, Paninski L. Clustered factor analysis of multineuronal spike data Advances in Neural Information Processing Systems. 4: 3500-3508. |
0.726 |
|
| 2013 |
Smith C, Paninski L. Computing loss of efficiency in optimal Bayesian decoders given noisy or incomplete spike trains. Network (Bristol, England). 24: 75-98. PMID 23742213 DOI: 10.3109/0954898X.2013.789568 |
0.677 |
|
| 2013 |
Sadeghi K, Gauthier JL, Field GD, Greschner M, Agne M, Chichilnisky EJ, Paninski L. Monte Carlo methods for localization of cones given multielectrode retinal ganglion cell recordings. Network (Bristol, England). 24: 27-51. PMID 23194406 DOI: 10.3109/0954898X.2012.740140 |
0.771 |
|
| 2013 |
Pnevmatikakis EA, Merel J, Pakman A, Paninski L. Bayesian spike inference from calcium imaging data Conference Record - Asilomar Conference On Signals, Systems and Computers. 349-353. DOI: 10.1109/ACSSC.2013.6810293 |
0.72 |
|
| 2012 |
Wong YT, Vigeral M, Putrino D, Pfau D, Merel J, Paninski L, Pesaran B. Decoding arm and hand movements across layers of the macaque frontal cortices. Conference Proceedings : ... Annual International Conference of the Ieee Engineering in Medicine and Biology Society. Ieee Engineering in Medicine and Biology Society. Annual Conference. 2012: 1757-60. PMID 23366250 DOI: 10.1109/EMBC.2012.6346289 |
0.685 |
|
| 2012 |
Doi E, Gauthier JL, Field GD, Shlens J, Sher A, Greschner M, Machado TA, Jepson LH, Mathieson K, Gunning DE, Litke AM, Paninski L, Chichilnisky EJ, Simoncelli EP. Efficient coding of spatial information in the primate retina. The Journal of Neuroscience : the Official Journal of the Society For Neuroscience. 32: 16256-64. PMID 23152609 DOI: 10.1523/Jneurosci.4036-12.2012 |
0.778 |
|
| 2012 |
Pnevmatikakis EA, Kelleher K, Chen R, Saggau P, Josi? K, Paninski L. Fast spatiotemporal smoothing of calcium measurements in dendritic trees. Plos Computational Biology. 8: e1002569. PMID 22787437 DOI: 10.1371/Journal.Pcbi.1002569 |
0.36 |
|
| 2012 |
Mishchenko Y, Paninski L. A Bayesian compressed-sensing approach for reconstructing neural connectivity from subsampled anatomical data. Journal of Computational Neuroscience. 33: 371-88. PMID 22437567 DOI: 10.1007/S10827-012-0390-Z |
0.349 |
|
| 2012 |
Vidne M, Ahmadian Y, Shlens J, Pillow JW, Kulkarni J, Litke AM, Chichilnisky EJ, Simoncelli E, Paninski L. Modeling the impact of common noise inputs on the network activity of retinal ganglion cells. Journal of Computational Neuroscience. 33: 97-121. PMID 22203465 DOI: 10.1007/S10827-011-0376-2 |
0.793 |
|
| 2012 |
Paninski L, Vidne M, DePasquale B, Ferreira DG. Inferring synaptic inputs given a noisy voltage trace via sequential Monte Carlo methods. Journal of Computational Neuroscience. 33: 1-19. PMID 22089473 DOI: 10.1007/S10827-011-0371-7 |
0.393 |
|
| 2012 |
Huggins JH, Paninski L. Optimal experimental design for sampling voltage on dendritic trees in the low-SNR regime. Journal of Computational Neuroscience. 32: 347-66. PMID 21861199 DOI: 10.1007/s10827-011-0357-5 |
0.344 |
|
| 2012 |
Nazarpour K, Ethier C, Paninski L, Rebesco JM, Miall RC, Miller LE. EMG prediction from motor cortical recordings via a nonnegative point-process filter. Ieee Transactions On Bio-Medical Engineering. 59: 1829-38. PMID 21659018 DOI: 10.1109/Tbme.2011.2159115 |
0.63 |
|
| 2011 |
Butts DA, Weng C, Jin J, Alonso JM, Paninski L. Temporal precision in the visual pathway through the interplay of excitation and stimulus-driven suppression. The Journal of Neuroscience : the Official Journal of the Society For Neuroscience. 31: 11313-27. PMID 21813691 DOI: 10.1523/JNEUROSCI.0434-11.2011 |
0.423 |
|
| 2011 |
Yuste R, MacLean J, Vogelstein J, Paninski L. Imaging action potentials with calcium indicators. Cold Spring Harbor Protocols. 2011: 985-9. PMID 21807854 DOI: 10.1101/Pdb.Prot5650 |
0.396 |
|
| 2011 |
Ahmadian Y, Packer AM, Yuste R, Paninski L. Designing optimal stimuli to control neuronal spike timing. Journal of Neurophysiology. 106: 1038-53. PMID 21511704 DOI: 10.1152/Jn.00427.2010 |
0.771 |
|
| 2011 |
Ramirez AD, Ahmadian Y, Schumacher J, Schneider D, Woolley SM, Paninski L. Incorporating naturalistic correlation structure improves spectrogram reconstruction from neuronal activity in the songbird auditory midbrain. The Journal of Neuroscience : the Official Journal of the Society For Neuroscience. 31: 3828-42. PMID 21389238 DOI: 10.1523/Jneurosci.3256-10.2011 |
0.812 |
|
| 2011 |
Escola S, Fontanini A, Katz D, Paninski L. Hidden Markov models for the stimulus-response relationships of multistate neural systems. Neural Computation. 23: 1071-132. PMID 21299424 DOI: 10.1162/Neco_A_00118 |
0.81 |
|
| 2011 |
Calabrese A, Schumacher JW, Schneider DM, Paninski L, Woolley SM. A generalized linear model for estimating spectrotemporal receptive fields from responses to natural sounds. Plos One. 6: e16104. PMID 21264310 DOI: 10.1371/Journal.Pone.0016104 |
0.47 |
|
| 2011 |
Calabrese A, Paninski L. Kalman filter mixture model for spike sorting of non-stationary data. Journal of Neuroscience Methods. 196: 159-69. PMID 21182868 DOI: 10.1016/J.Jneumeth.2010.12.002 |
0.382 |
|
| 2011 |
Ahmadian Y, Pillow JW, Paninski L. Efficient Markov chain Monte Carlo methods for decoding neural spike trains. Neural Computation. 23: 46-96. PMID 20964539 DOI: 10.1162/Neco_A_00059 |
0.815 |
|
| 2011 |
Pillow JW, Ahmadian Y, Paninski L. Model-based decoding, information estimation, and change-point detection techniques for multineuron spike trains. Neural Computation. 23: 1-45. PMID 20964538 DOI: 10.1162/Neco_A_00058 |
0.821 |
|
| 2011 |
Lewi J, Schneider DM, Woolley SM, Paninski L. Automating the design of informative sequences of sensory stimuli. Journal of Computational Neuroscience. 30: 181-200. PMID 20556641 DOI: 10.1007/S10827-010-0248-1 |
0.386 |
|
| 2011 |
Mishchenko Y, Paninski L. Efficient methods for sampling spike trains in networks of coupled neurons Annals of Applied Statistics. 5: 1893-1919. DOI: 10.1214/11-Aoas467 |
0.436 |
|
| 2011 |
Mishchenko Y, Vogelstein JT, Paninski L. A bayesian approach for inferring neuronal connectivity from calcium fluorescent imaging data Annals of Applied Statistics. 5: 1229-1261. DOI: 10.1214/09-Aoas303 |
0.427 |
|
| 2010 |
Rad KR, Paninski L. Efficient, adaptive estimation of two-dimensional firing rate surfaces via Gaussian process methods. Network (Bristol, England). 21: 142-68. PMID 21138363 DOI: 10.3109/0954898X.2010.532288 |
0.357 |
|
| 2010 |
Field GD, Gauthier JL, Sher A, Greschner M, Machado TA, Jepson LH, Shlens J, Gunning DE, Mathieson K, Dabrowski W, Paninski L, Litke AM, Chichilnisky EJ. Functional connectivity in the retina at the resolution of photoreceptors. Nature. 467: 673-7. PMID 20930838 DOI: 10.1038/Nature09424 |
0.792 |
|
| 2010 |
Babadi B, Casti A, Xiao Y, Kaplan E, Paninski L. A generalized linear model of the impact of direct and indirect inputs to the lateral geniculate nucleus. Journal of Vision. 10: 22. PMID 20884487 DOI: 10.1167/10.10.22 |
0.449 |
|
| 2010 |
Vogelstein JT, Packer AM, Machado TA, Sippy T, Babadi B, Yuste R, Paninski L. Fast nonnegative deconvolution for spike train inference from population calcium imaging. Journal of Neurophysiology. 104: 3691-704. PMID 20554834 DOI: 10.1152/Jn.01073.2009 |
0.759 |
|
| 2010 |
Lawhern V, Wu W, Hatsopoulos N, Paninski L. Population decoding of motor cortical activity using a generalized linear model with hidden states. Journal of Neuroscience Methods. 189: 267-80. PMID 20359500 DOI: 10.1016/j.jneumeth.2010.03.024 |
0.395 |
|
| 2010 |
Paninski L. Fast Kalman filtering on quasilinear dendritic trees. Journal of Computational Neuroscience. 28: 211-28. PMID 19943188 DOI: 10.1007/s10827-009-0200-4 |
0.305 |
|
| 2010 |
Paninski L, Ahmadian Y, Ferreira DG, Koyama S, Rahnama Rad K, Vidne M, Vogelstein J, Wu W. A new look at state-space models for neural data. Journal of Computational Neuroscience. 29: 107-26. PMID 19649698 DOI: 10.1007/S10827-009-0179-X |
0.745 |
|
| 2010 |
Koyama S, Paninski L. Efficient computation of the maximum a posteriori path and parameter estimation in integrate-and-fire and more general state-space models. Journal of Computational Neuroscience. 29: 89-105. PMID 19399603 DOI: 10.1007/s10827-009-0150-x |
0.376 |
|
| 2010 |
Simoncelli EP, Pillow JW, Shlens J, Paninski L, Chichilnisky EJ. Toward characterizion of the complete visual signal in a patch of retina Journal of Vision. 6: 2-2. DOI: 10.1167/6.13.2 |
0.767 |
|
| 2010 |
Paninski L, Brown EN, Iyengar S, Kass RE. Statistical Models of Spike Trains Stochastic Methods in Neuroscience. DOI: 10.1093/acprof:oso/9780199235070.003.0010 |
0.55 |
|
| 2009 |
Lalor EC, Ahmadian Y, Paninski L. The relationship between optimal and biologically plausible decoding of stimulus velocity in the retina. Journal of the Optical Society of America. a, Optics, Image Science, and Vision. 26: B25-42. PMID 19884914 DOI: 10.1364/Josaa.26.000B25 |
0.767 |
|
| 2009 |
Toyoizumi T, Rad KR, Paninski L. Mean-field approximations for coupled populations of generalized linear model spiking neurons with Markov refractoriness. Neural Computation. 21: 1203-43. PMID 19718814 DOI: 10.1162/Neco.2008.04-08-757 |
0.447 |
|
| 2009 |
Vogelstein JT, Watson BO, Packer AM, Yuste R, Jedynak B, Paninski L. Spike inference from calcium imaging using sequential Monte Carlo methods. Biophysical Journal. 97: 636-55. PMID 19619479 DOI: 10.1016/J.Bpj.2008.08.005 |
0.419 |
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| 2009 |
Wu W, Kulkarni JE, Hatsopoulos NG, Paninski L. Neural decoding of hand motion using a linear state-space model with hidden states. Ieee Transactions On Neural Systems and Rehabilitation Engineering : a Publication of the Ieee Engineering in Medicine and Biology Society. 17: 370-8. PMID 19497822 DOI: 10.1109/TNSRE.2009.2023307 |
0.365 |
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| 2009 |
Huys QJ, Paninski L. Smoothing of, and parameter estimation from, noisy biophysical recordings. Plos Computational Biology. 5: e1000379. PMID 19424506 DOI: 10.1371/journal.pcbi.1000379 |
0.652 |
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| 2009 |
Escola S, Eisele M, Miller K, Paninski L. Maximally reliable Markov chains under energy constraints. Neural Computation. 21: 1863-912. PMID 19292647 DOI: 10.1162/Neco.2009.08-08-843 |
0.756 |
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| 2009 |
Lewi J, Butera R, Paninski L. Sequential optimal design of neurophysiology experiments. Neural Computation. 21: 619-87. PMID 18928364 DOI: 10.1162/Neco.2008.08-07-594 |
0.416 |
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| 2008 |
Pillow JW, Shlens J, Paninski L, Sher A, Litke AM, Chichilnisky EJ, Simoncelli EP. Spatio-temporal correlations and visual signalling in a complete neuronal population. Nature. 454: 995-9. PMID 18650810 DOI: 10.1038/Nature07140 |
0.808 |
|
| 2008 |
Ahrens MB, Paninski L, Sahani M. Inferring input nonlinearities in neural encoding models. Network (Bristol, England). 19: 35-67. PMID 18300178 DOI: 10.1080/09548980701813936 |
0.434 |
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| 2008 |
Paninski L, Haith A, Szirtes G. Integral equation methods for computing likelihoods and their derivatives in the stochastic integrate-and-fire model. Journal of Computational Neuroscience. 24: 69-79. PMID 17492371 DOI: 10.1007/S10827-007-0042-X |
0.345 |
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| 2007 |
Kulkarni JE, Paninski L. Common-input models for multiple neural spike-train data. Network (Bristol, England). 18: 375-407. PMID 17943613 DOI: 10.1080/09548980701625173 |
0.481 |
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| 2007 |
Paninski L, Pillow J, Lewi J. Statistical models for neural encoding, decoding, and optimal stimulus design. Progress in Brain Research. 165: 493-507. PMID 17925266 DOI: 10.1016/S0079-6123(06)65031-0 |
0.693 |
|
| 2006 |
Paninski L. The spike-triggered average of the integrate-and-fire cell driven by gaussian white noise. Neural Computation. 18: 2592-616. PMID 16999572 DOI: 10.1162/neco.2006.18.11.2592 |
0.335 |
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| 2006 |
Townsend BR, Paninski L, Lemon RN. Linear encoding of muscle activity in primary motor cortex and cerebellum. Journal of Neurophysiology. 96: 2578-92. PMID 16790591 DOI: 10.1152/Jn.01086.2005 |
0.363 |
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| 2006 |
Paninski L. The most likely voltage path and large deviations approximations for integrate-and-fire neurons. Journal of Computational Neuroscience. 21: 71-87. PMID 16633936 DOI: 10.1007/s10827-006-7200-4 |
0.389 |
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| 2006 |
Huys QJ, Ahrens MB, Paninski L. Efficient estimation of detailed single-neuron models. Journal of Neurophysiology. 96: 872-90. PMID 16624998 DOI: 10.1152/Jn.00079.2006 |
0.669 |
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| 2005 |
Pillow JW, Paninski L, Uzzell VJ, Simoncelli EP, Chichilnisky EJ. Prediction and decoding of retinal ganglion cell responses with a probabilistic spiking model. The Journal of Neuroscience : the Official Journal of the Society For Neuroscience. 25: 11003-13. PMID 16306413 DOI: 10.1523/Jneurosci.3305-05.2005 |
0.816 |
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| 2005 |
Shoham S, Paninski LM, Fellows MR, Hatsopoulos NG, Donoghue JP, Normann RA. Statistical encoding model for a primary motor cortical brain-machine interface. Ieee Transactions On Bio-Medical Engineering. 52: 1312-22. PMID 16041995 DOI: 10.1109/TBME.2005.847542 |
0.59 |
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| 2005 |
Paninski L. Asymptotic theory of information-theoretic experimental design. Neural Computation. 17: 1480-507. PMID 15901405 DOI: 10.1162/0899766053723032 |
0.327 |
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| 2005 |
Paninski L, Pillow J, Simoncelli E. Comparing integrate-and-fire models estimated using intracellular and extracellular data Neurocomputing. 65: 379-385. DOI: 10.1016/j.neucom.2004.10.032 |
0.809 |
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| 2005 |
Ahrens MB, Huys QJM, Paninski L. Large-scale biophysical parameter estimation in single neurons via constrained linear regression Advances in Neural Information Processing Systems. 25-32. |
0.596 |
|
| 2004 |
Paninski L. Maximum likelihood estimation of cascade point-process neural encoding models. Network (Bristol, England). 15: 243-62. PMID 15600233 DOI: 10.1088/0954-898X/15/4/002 |
0.381 |
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| 2004 |
Paninski L, Pillow JW, Simoncelli EP. Maximum likelihood estimation of a stochastic integrate-and-fire neural encoding model. Neural Computation. 16: 2533-61. PMID 15516273 DOI: 10.1162/0899766042321797 |
0.817 |
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| 2004 |
Paninski L, Shoham S, Fellows MR, Hatsopoulos NG, Donoghue JP. Superlinear population encoding of dynamic hand trajectory in primary motor cortex. The Journal of Neuroscience : the Official Journal of the Society For Neuroscience. 24: 8551-61. PMID 15456829 DOI: 10.1523/JNEUROSCI.0919-04.2004 |
0.606 |
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| 2004 |
Paninski L, Fellows MR, Hatsopoulos NG, Donoghue JP. Spatiotemporal tuning of motor cortical neurons for hand position and velocity. Journal of Neurophysiology. 91: 515-32. PMID 13679402 DOI: 10.1152/jn.00587.2002 |
0.628 |
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| 2004 |
Pillow JW, Paninski L, Simoncelli EP. Maximum likelihood estimation of a stochastic integrate-and-fire neural model Advances in Neural Information Processing Systems. |
0.787 |
|
| 2003 |
Paninski L. Convergence properties of three spike-triggered analysis techniques. Network (Bristol, England). 14: 437-64. PMID 12938766 DOI: 10.1088/0954-898X/14/3/304 |
0.427 |
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| 2003 |
Hatsopoulos NG, Paninski L, Donoghue JP. Sequential movement representations based on correlated neuronal activity. Experimental Brain Research. 149: 478-86. PMID 12677328 DOI: 10.1007/s00221-003-1385-9 |
0.613 |
|
| 2003 |
Serruya M, Hatsopoulos N, Fellows M, Paninski L, Donoghue J. Robustness of neuroprosthetic decoding algorithms. Biological Cybernetics. 88: 219-28. PMID 12647229 DOI: 10.1007/S00422-002-0374-6 |
0.637 |
|
| 2003 |
Paninski L. Estimation of entropy and mutual information Neural Computation. 15: 1191-1253. DOI: 10.1162/089976603321780272 |
0.315 |
|
| 2003 |
Paninski L, Lau B, Reyes A. Noise-driven adaptation: In vitro and mathematical analysis Neurocomputing. 52: 877-883. DOI: 10.1016/S0925-2312(02)00819-6 |
0.402 |
|
| 2002 |
Serruya MD, Hatsopoulos NG, Paninski L, Fellows MR, Donoghue JP. Instant neural control of a movement signal. Nature. 416: 141-2. PMID 11894084 DOI: 10.1038/416141A |
0.599 |
|
| 1998 |
Hatsopoulos NG, Ojakangas CL, Paninski L, Donoghue JP. Information about movement direction obtained from synchronous activity of motor cortical neurons. Proceedings of the National Academy of Sciences of the United States of America. 95: 15706-11. PMID 9861034 DOI: 10.1073/pnas.95.26.15706 |
0.619 |
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| Low-probability matches (unlikely to be authored by this person) |
| 2019 |
Naka A, Veit J, Shababo B, Chance RK, Risso D, Stafford D, Snyder B, Egladyous A, Chu D, Sridharan S, Mossing DP, Paninski L, Ngai J, Adesnik H. Complementary networks of cortical somatostatin interneurons enforce layer specific control. Elife. 8. PMID 30883329 DOI: 10.7554/Elife.43696 |
0.296 |
|
| 2019 |
Wei X, Zhu R, Paninski L. A new method to analyze the variations of neural tuning and its application to primate V1 Journal of Vision. 19: 271b. DOI: 10.1167/19.10.271B |
0.289 |
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| 2001 |
Paninski L, Hawken MJ. Stochastic optimal control and the human oculomotor system Neurocomputing. 38: 1511-1517. DOI: 10.1016/S0925-2312(01)00541-0 |
0.281 |
|
| 2008 |
Kulkarni JE, Paninski L. State-space decoding of goal-directed movements Ieee Signal Processing Magazine. 25: 78-86. DOI: 10.1109/MSP.2008.4408444 |
0.275 |
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| 2014 |
Pnevmatikakis EA, Rad KR, Huggins J, Paninski L. Fast kalman filtering and forward-backward smoothing via a low- rank perturbative approach Journal of Computational and Graphical Statistics. 23: 316-339. DOI: 10.1080/10618600.2012.760461 |
0.272 |
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| 2008 |
Paninski L, Yajima M. Undersmoothed kernel entropy estimators Ieee Transactions On Information Theory. 54: 4384-4388. DOI: 10.1109/Tit.2008.928251 |
0.269 |
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| 2023 |
Schaffer ES, Mishra N, Whiteway MR, Li W, Vancura MB, Freedman J, Patel KB, Voleti V, Paninski L, Hillman EMC, Abbott LF, Axel R. The spatial and temporal structure of neural activity across the fly brain. Nature Communications. 14: 5572. PMID 37696814 DOI: 10.1038/s41467-023-41261-2 |
0.263 |
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| 2006 |
Lewi J, Butera R, Paninski L. Efficient model-based design of neurophysiological experiments. Conference Proceedings : ... Annual International Conference of the Ieee Engineering in Medicine and Biology Society. Ieee Engineering in Medicine and Biology Society. Annual Conference. 1: 599-602. PMID 17945990 DOI: 10.1109/IEMBS.2006.260690 |
0.252 |
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| 2019 |
Naka A, Veit J, Shababo B, Chance RK, Risso D, Stafford D, Snyder B, Egladyous A, Chu D, Sridharan S, Mossing DP, Paninski L, Ngai J, Adesnik H. Author response: Complementary networks of cortical somatostatin interneurons enforce layer specific control Elife. DOI: 10.7554/Elife.43696.035 |
0.249 |
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| 2023 |
Nejatbakhsh A, Dey N, Venkatachalam V, Yemini E, Paninski L, Varol E. Learning Probabilistic Piecewise Rigid Atlases of Model Organisms via Generative Deep Networks. Information Processing in Medical Imaging : Proceedings of the ... Conference. 13939: 332-343. PMID 37476079 DOI: 10.1007/978-3-031-34048-2_26 |
0.248 |
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| 2009 |
Fudenberg G, Paninski L. Bayesian image recovery for dendritic structures under low signal-to-noise conditions. Ieee Transactions On Image Processing : a Publication of the Ieee Signal Processing Society. 18: 471-82. PMID 19211329 DOI: 10.1109/TIP.2008.2010212 |
0.244 |
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| 2008 |
Paninski L. A coincidence-based test for uniformity given very sparsely sampled discrete data Ieee Transactions On Information Theory. 54: 4750-4755. DOI: 10.1109/TIT.2008.928987 |
0.238 |
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| 2021 |
Xie ME, Adam Y, Fan LZ, Böhm UL, Kinsella I, Zhou D, Rozsa M, Singh A, Svoboda K, Paninski L, Cohen AE. High-fidelity estimates of spikes and subthreshold waveforms from 1-photon voltage imaging in vivo. Cell Reports. 35: 108954. PMID 33826882 DOI: 10.1016/j.celrep.2021.108954 |
0.236 |
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| 2023 |
Triplett MA, Gajowa M, Adesnik H, Paninski L. Bayesian target optimisation for high-precision holographic optogenetics. Biorxiv : the Preprint Server For Biology. PMID 37292661 DOI: 10.1101/2023.05.25.542307 |
0.227 |
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| 2014 |
Gerstner W, Kistler WM, Naud R, Paninski L. Neuronal dynamics: From single neurons to networks and models of cognition Neuronal Dynamics: From Single Neurons to Networks and Models of Cognition. 1-577. DOI: 10.1017/CBO9781107447615 |
0.222 |
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| 2004 |
Paninski L. Estimating entropy on m bins given fewer than m samples Ieee Transactions On Information Theory. 50: 2200-2203. DOI: 10.1109/TIT.2004.833360 |
0.219 |
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| 2021 |
Chen S, Loper J, Chen X, Vaughan A, Zador AM, Paninski L. BARcode DEmixing through Non-negative Spatial Regression (BarDensr). Plos Computational Biology. 17: e1008256. PMID 33684106 DOI: 10.1371/journal.pcbi.1008256 |
0.217 |
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| 2023 |
Windolf C, Paulk AC, Kfir Y, Trautmann E, Meszéna D, Muñoz W, Caprara I, Jamali M, Boussard J, Williams ZM, Cash SS, Paninski L, Varol E. ROBUST ONLINE MULTIBAND DRIFT ESTIMATION IN ELECTROPHYSIOLOGY DATA. Proceedings of the ... Ieee International Conference On Acoustics, Speech, and Signal Processing. Icassp (Conference). 2023. PMID 37388234 DOI: 10.1109/icassp49357.2023.10095487 |
0.215 |
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| 2021 |
Tekieli T, Yemini E, Nejatbakhsh A, Wang C, Varol E, Fernandez RW, Masoudi N, Paninski L, Hobert O. Visualizing the organization and differentiation of the male-specific nervous system of C. elegans. Development (Cambridge, England). PMID 34415309 DOI: 10.1242/dev.199687 |
0.206 |
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| 2023 |
Chen S, Rao BY, Herrlinger S, Losonczy A, Paninski L, Varol E. MULTIMODAL MICROSCOPY IMAGE ALIGNMENT USING SPATIAL AND SHAPE INFORMATION AND A BRANCH-AND-BOUND ALGORITHM. Proceedings of the ... Ieee International Conference On Acoustics, Speech, and Signal Processing. Icassp (Conference). 2023. PMID 37388235 DOI: 10.1109/icassp49357.2023.10096185 |
0.205 |
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| 2023 |
Windolf C, Yu H, Paulk AC, Meszéna D, Muñoz W, Boussard J, Hardstone R, Caprara I, Jamali M, Kfir Y, Xu D, Chung JE, Sellers KK, Ye Z, Shaker J, ... ... Paninski L, et al. DREDge: robust motion correction for high-density extracellular recordings across species. Biorxiv : the Preprint Server For Biology. PMID 37961359 DOI: 10.1101/2023.10.24.563768 |
0.188 |
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| 2021 |
Rao BY, Peterson AM, Kandror EK, Herrlinger S, Losonczy A, Paninski L, Rizvi AH, Varol E. Non-parametric Vignetting Correction for Sparse Spatial Transcriptomics Images. Medical Image Computing and Computer-Assisted Intervention : Miccai ... International Conference On Medical Image Computing and Computer-Assisted Intervention. 12908: 466-475. PMID 35274110 DOI: 10.1007/978-3-030-87237-3_45 |
0.187 |
|
| 2011 |
Rad KR, Paninski L. Information rates and optimal decoding in large neural populations Advances in Neural Information Processing Systems 24: 25th Annual Conference On Neural Information Processing Systems 2011, Nips 2011. |
0.161 |
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| 2003 |
Paninski L. Convergence properties of some spike-triggered analysis techniques Advances in Neural Information Processing Systems. |
0.144 |
|
| 2007 |
Lewi J, Butera R, Paninski L. Real-time adaptive information-theoretic optimization of neurophysiology experiments Advances in Neural Information Processing Systems. 857-864. |
0.128 |
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| 2009 |
Lewi J, Butera R, Schneider DM, Woolley SMN, Paninski L. Designing neurophysiology experiments to optimally constrain receptive field models along parametric submanifolds Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference. 945-952. |
0.125 |
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| 2007 |
Lewi J, Butera R, Paninski L. Efficient active learning with generalized linear models Journal of Machine Learning Research. 2: 267-274. |
0.125 |
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| 2013 |
Shababo B, Paige B, Pakman A, Paninski L. Bayesian inference and online experimental design for mapping neural microcircuits Advances in Neural Information Processing Systems. |
0.124 |
|
| 2012 |
Pnevmatikakis EA, Paninski L. Fast interior-point inference in high-dimensional sparse, penalized state-space models Journal of Machine Learning Research. 22: 895-904. |
0.123 |
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| 2013 |
Pfau D, Pnevmatikakis EA, Paninski L. Robust learning of low-dimensional dynamics from large neural ensembles Advances in Neural Information Processing Systems. |
0.119 |
|
| 2010 |
Field RM, Lary J, Cohn J, Paninski L, Shepard KL. A low-noise, single-photon avalanche diode in standard 0.13 μm complementary metal-oxide-semiconductor process Applied Physics Letters. 97. DOI: 10.1063/1.3518473 |
0.106 |
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| 2012 |
Smith C, Wood F, Paninski L. Low rank continuous-space graphical models Journal of Machine Learning Research. 22: 1064-1072. |
0.105 |
|
| 2013 |
Pnevmatikakis EA, Paninski L. Sparse nonnegative deconvolution for compressive calcium imaging: Algorithms and phase transitions Advances in Neural Information Processing Systems. |
0.092 |
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| 2005 |
Paninski L. Nonparametric inference of prior probabilities from Bayes-optimal behavior Advances in Neural Information Processing Systems. 1067-1074. |
0.09 |
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| 2005 |
Paninski L. Variational minimax estimation of discrete distributions under KL loss Advances in Neural Information Processing Systems. |
0.087 |
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| 2005 |
Paninski L. Log-concavity results on Gaussian process methods for supervised and unsupervised learning Advances in Neural Information Processing Systems. |
0.071 |
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| 2013 |
Merel J, Fox R, Jebara T, Paninski L. A multi-agent control framework for co-adaptation in brain-computer interfaces Advances in Neural Information Processing Systems. |
0.061 |
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| 2013 |
Pakman A, Paninski L. Auxiliary-variable exact Hamiltonian Monte Carlo samplers for binary distributions Advances in Neural Information Processing Systems. |
0.056 |
|
| 2012 |
Paninski L, Rad KR, Vidne M. Robust particle filters via sequential pairwise reparameterized Gibbs sampling 2012 46th Annual Conference On Information Sciences and Systems, Ciss 2012. DOI: 10.1109/CISS.2012.6310772 |
0.053 |
|
| 2004 |
Paninski L. Design of experiments via information theory Advances in Neural Information Processing Systems. |
0.045 |
|
| Hide low-probability matches. |