| Year |
Citation |
Score |
| 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 |
0.64 |
|
| 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 |
0.52 |
|
| 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 |
0.52 |
|
| 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 |
0.92 |
|
| 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 |
0.44 |
|
| 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 |
0.52 |
|
| 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 |
0.52 |
|
| 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 |
0.52 |
|
| 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 |
0.52 |
|
| 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 |
0.92 |
|
| 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 |
0.52 |
|
| 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 |
0.52 |
|
| 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 |
0.44 |
|
| 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 |
0.52 |
|
| 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 |
1 |
|
| 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 |
1 |
|
| 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 |
1 |
|
| 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 |
1 |
|
| 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 |
1 |
|
| 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 |
1 |
|
| 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 |
1 |
|
| 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 |
1 |
|
| 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 |
1 |
|
| 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 |
1 |
|
| 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 |
1 |
|
| 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 |
1 |
|
| 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 |
1 |
|
| 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 |
1 |
|
| 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 |
1 |
|
| 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 |
1 |
|
| 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 |
1 |
|
| 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. |
1 |
|
| 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 |
1 |
|
| 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 |
1 |
|
| 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 |
1 |
|
| 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. |
1 |
|
| 2013 |
Pakman A, Paninski L. Auxiliary-variable exact Hamiltonian Monte Carlo samplers for binary distributions Advances in Neural Information Processing Systems. |
1 |
|
| 2013 |
Pfau D, Pnevmatikakis EA, Paninski L. Robust learning of low-dimensional dynamics from large neural ensembles Advances in Neural Information Processing Systems. |
1 |
|
| 2013 |
Pnevmatikakis EA, Paninski L. Sparse nonnegative deconvolution for compressive calcium imaging: Algorithms and phase transitions Advances in Neural Information Processing Systems. |
1 |
|
| 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. |
1 |
|
| 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 |
1 |
|
| 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 |
1 |
|
| 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 |
1 |
|
| 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 |
1 |
|
| 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 |
1 |
|
| 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 |
1 |
|
| 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 |
1 |
|
| 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 |
1 |
|
| 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 |
1 |
|
| 2012 |
Smith C, Wood F, Paninski L. Low rank continuous-space graphical models Journal of Machine Learning Research. 22: 1064-1072. |
1 |
|
| 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. |
1 |
|
| 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 |
1 |
|
| 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 |
1 |
|
| 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 |
1 |
|
| 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 |
1 |
|
| 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 |
1 |
|
| 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 |
1 |
|
| 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 |
1 |
|
| 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 |
1 |
|
| 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 |
1 |
|
| 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 |
1 |
|
| 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 |
1 |
|
| 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 |
1 |
|
| 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. |
1 |
|
| 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 |
1 |
|
| 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 |
1 |
|
| 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 |
1 |
|
| 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 |
1 |
|
| 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 |
1 |
|
| 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 |
1 |
|
| 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 |
1 |
|
| 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 |
1 |
|
| 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 |
1 |
|
| 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 |
1 |
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| 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 |
1 |
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| 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 |
1 |
|
| 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 |
0.32 |
|
| 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 |
1 |
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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 |
1 |
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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 |
1 |
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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 |
1 |
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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 |
1 |
|
| 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. |
1 |
|
| 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 |
1 |
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| 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 |
1 |
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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 |
1 |
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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 |
1 |
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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 |
1 |
|
| 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 |
1 |
|
| 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 |
1 |
|
| 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 |
1 |
|
| 2007 |
Lewi J, Butera R, Paninski L. Real-time adaptive information-theoretic optimization of neurophysiology experiments Advances in Neural Information Processing Systems. 857-864. |
1 |
|
| 2007 |
Lewi J, Butera R, Paninski L. Efficient active learning with generalized linear models Journal of Machine Learning Research. 2: 267-274. |
1 |
|
| 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 |
1 |
|
| 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 |
1 |
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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 |
1 |
|
| 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 |
1 |
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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 |
1 |
|
| 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 |
1 |
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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 |
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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 |
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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. |
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| 2005 |
Paninski L. Nonparametric inference of prior probabilities from Bayes-optimal behavior Advances in Neural Information Processing Systems. 1067-1074. |
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| 2005 |
Paninski L. Variational minimax estimation of discrete distributions under KL loss Advances in Neural Information Processing Systems. |
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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. |
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| 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 |
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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 |
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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 |
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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 |
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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 |
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| 2004 |
Paninski L. Design of experiments via information theory Advances in Neural Information Processing Systems. |
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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. |
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| 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 |
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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 |
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| 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 |
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| 2003 |
Paninski L. Estimation of entropy and mutual information Neural Computation. 15: 1191-1253. DOI: 10.1162/089976603321780272 |
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| 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 |
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| 2003 |
Paninski L. Convergence properties of some spike-triggered analysis techniques Advances in Neural Information Processing Systems. |
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| 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 |
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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 |
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| 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 |
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