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Items: 1 to 50 of 87

1.

Bright and photostable chemigenetic indicators for extended in vivo voltage imaging.

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, Macklin JJ, Murphy GJ, Podgorski K, Lin BJ, Chen TW, Turner GC, Liu Z, Koyama M, Svoboda K, Ahrens MB, Lavis LD, Schreiter ER.

Science. 2019 Aug 16;365(6454):699-704. doi: 10.1126/science.aav6416. Epub 2019 Aug 1.

PMID:
31371562
2.

Voltage imaging and optogenetics reveal behaviour-dependent changes in hippocampal dynamics.

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, Buchanan EK, Kinsella I, Zhou D, Paninski L, Harvey CD, Zeng H, Arlotta P, Campbell RE, Cohen AE.

Nature. 2019 May;569(7756):413-417. doi: 10.1038/s41586-019-1166-7. Epub 2019 May 1.

3.

Complementary networks of cortical somatostatin interneurons enforce layer specific control.

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.

Elife. 2019 Mar 18;8. pii: e43696. doi: 10.7554/eLife.43696.

4.

Reinforcement Learning Recruits Somata and Apical Dendrites across Layers of Primary Sensory Cortex.

Lacefield CO, Pnevmatikakis EA, Paninski L, Bruno RM.

Cell Rep. 2019 Feb 19;26(8):2000-2008.e2. doi: 10.1016/j.celrep.2019.01.093.

5.

Community-based benchmarking improves spike rate inference from two-photon calcium imaging data.

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, Harris KD, Bolte B, Machado TA, Ringach D, Stone J, Rogerson LE, Sofroniew NJ, Reimer J, Froudarakis E, Euler T, Román Rosón M, Theis L, Tolias AS, Bethge M.

PLoS Comput Biol. 2018 May 21;14(5):e1006157. doi: 10.1371/journal.pcbi.1006157. eCollection 2018 May.

6.

Neural data science: accelerating the experiment-analysis-theory cycle in large-scale neuroscience.

Paninski L, Cunningham JP.

Curr Opin Neurobiol. 2018 Jun;50:232-241. doi: 10.1016/j.conb.2018.04.007. Review.

PMID:
29738986
7.

Efficient and accurate extraction of in vivo calcium signals from microendoscopic video data.

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.

Elife. 2018 Feb 22;7. pii: e28728. doi: 10.7554/eLife.28728.

8.

Anxiety Cells in a Hippocampal-Hypothalamic Circuit.

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.

Neuron. 2018 Feb 7;97(3):670-683.e6. doi: 10.1016/j.neuron.2018.01.016. Epub 2018 Jan 31.

9.

The central amygdala controls learning in the lateral amygdala.

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.

Nat Neurosci. 2017 Dec;20(12):1680-1685. doi: 10.1038/s41593-017-0009-9. Epub 2017 Oct 23.

10.

The Spatiotemporal Organization of the Striatum Encodes Action Space.

Klaus A, Martins GJ, Paixao VB, Zhou P, Paninski L, Costa RM.

Neuron. 2017 Nov 15;96(4):949. doi: 10.1016/j.neuron.2017.10.031. No abstract available.

11.

Electrical stimulus artifact cancellation and neural spike detection on large multi-electrode arrays.

Mena GE, Grosberg LE, Madugula S, Hottowy P, Litke A, Cunningham J, Chichilnisky EJ, Paninski L.

PLoS Comput Biol. 2017 Nov 13;13(11):e1005842. doi: 10.1371/journal.pcbi.1005842. eCollection 2017 Nov.

12.

The Spatiotemporal Organization of the Striatum Encodes Action Space.

Klaus A, Martins GJ, Paixao VB, Zhou P, Paninski L, Costa RM.

Neuron. 2017 Aug 30;95(5):1171-1180.e7. doi: 10.1016/j.neuron.2017.08.015. Erratum in: Neuron. 2017 Nov 15;96(4):949.

13.

Multi-scale approaches for high-speed imaging and analysis of large neural populations.

Friedrich J, Yang W, Soudry D, Mu Y, Ahrens MB, Yuste R, Peterka DS, Paninski L.

PLoS Comput Biol. 2017 Aug 3;13(8):e1005685. doi: 10.1371/journal.pcbi.1005685. eCollection 2017 Aug.

14.

Cerebellar granule cells acquire a widespread predictive feedback signal during motor learning.

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.

Nat Neurosci. 2017 May;20(5):727-734. doi: 10.1038/nn.4531. Epub 2017 Mar 20.

15.

Fast online deconvolution of calcium imaging data.

Friedrich J, Zhou P, Paninski L.

PLoS Comput Biol. 2017 Mar 14;13(3):e1005423. doi: 10.1371/journal.pcbi.1005423. eCollection 2017 Mar.

16.

Bayesian methods for event analysis of intracellular currents.

Merel J, Shababo B, Naka A, Adesnik H, Paninski L.

J Neurosci Methods. 2016 Aug 30;269:21-32. doi: 10.1016/j.jneumeth.2016.05.015. Epub 2016 May 18.

PMID:
27208694
17.

Population-Level Representation of a Temporal Sequence Underlying Song Production in the Zebra Finch.

Picardo MA, Merel J, Katlowitz KA, Vallentin D, Okobi DE, Benezra SE, Clary RC, Pnevmatikakis EA, Paninski L, Long MA.

Neuron. 2016 May 18;90(4):866-76. doi: 10.1016/j.neuron.2016.02.016.

18.

Neuroprosthetic Decoder Training as Imitation Learning.

Merel J, Carlson D, Paninski L, Cunningham JP.

PLoS Comput Biol. 2016 May 18;12(5):e1004948. doi: 10.1371/journal.pcbi.1004948. eCollection 2016 May.

19.

Bayesian Sparse Regression Analysis Documents the Diversity of Spinal Inhibitory Interneurons.

Gabitto MI, Pakman A, Bikoff JB, Abbott LF, Jessell TM, Paninski L.

Cell. 2016 Mar 24;165(1):220-233. doi: 10.1016/j.cell.2016.01.026. Epub 2016 Mar 3.

20.

Simultaneous Denoising, Deconvolution, and Demixing of Calcium Imaging Data.

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.

Neuron. 2016 Jan 20;89(2):285-99. doi: 10.1016/j.neuron.2015.11.037. Epub 2016 Jan 7.

21.

Simultaneous Multi-plane Imaging of Neural Circuits.

Yang W, Miller JE, Carrillo-Reid L, Pnevmatikakis E, Paninski L, Yuste R, Peterka DS.

Neuron. 2016 Jan 20;89(2):269-84. doi: 10.1016/j.neuron.2015.12.012. Epub 2016 Jan 7.

22.

Mapping nonlinear receptive field structure in primate retina at single cone resolution.

Freeman J, Field GD, Li PH, Greschner M, Gunning DE, Mathieson K, Sher A, Litke AM, Paninski L, Simoncelli EP, Chichilnisky EJ.

Elife. 2015 Oct 30;4. pii: e05241. doi: 10.7554/eLife.05241.

23.

Efficient "Shotgun" Inference of Neural Connectivity from Highly Sub-sampled Activity Data.

Soudry D, Keshri S, Stinson P, Oh MH, Iyengar G, Paninski L.

PLoS Comput Biol. 2015 Oct 14;11(10):e1004464. doi: 10.1371/journal.pcbi.1004464. eCollection 2015 Oct. Erratum in: PLoS Comput Biol. 2015 Dec;11(12):e1004657.

24.

Primacy of Flexor Locomotor Pattern Revealed by Ancestral Reversion of Motor Neuron Identity.

Machado TA, Pnevmatikakis E, Paninski L, Jessell TM, Miri A.

Cell. 2015 Jul 16;162(2):338-350. doi: 10.1016/j.cell.2015.06.036.

25.

Encoder-decoder optimization for brain-computer interfaces.

Merel J, Pianto DM, Cunningham JP, Paninski L.

PLoS Comput Biol. 2015 Jun 1;11(6):e1004288. doi: 10.1371/journal.pcbi.1004288. eCollection 2015 Jun.

26.

On quadrature methods for refractory point process likelihoods.

Mena G, Paninski L.

Neural Comput. 2014 Dec;26(12):2790-7. doi: 10.1162/NECO_a_00676. Epub 2014 Sep 23.

PMID:
25248082
27.

Spatiotemporal receptive fields of barrel cortex revealed by reverse correlation of synaptic input.

Ramirez A, Pnevmatikakis EA, Merel J, Paninski L, Miller KD, Bruno RM.

Nat Neurosci. 2014 Jun;17(6):866-75. doi: 10.1038/nn.3720. Epub 2014 May 18.

28.

Fast state-space methods for inferring dendritic synaptic connectivity.

Pakman A, Huggins J, Smith C, Paninski L.

J Comput Neurosci. 2014 Jun;36(3):415-43.

PMID:
24077932
29.

Fast inference in generalized linear models via expected log-likelihoods.

Ramirez AD, Paninski L.

J Comput Neurosci. 2014 Apr;36(2):215-34. doi: 10.1007/s10827-013-0466-4. Epub 2013 Jul 6.

30.

Computing loss of efficiency in optimal Bayesian decoders given noisy or incomplete spike trains.

Smith C, Paninski L.

Network. 2013;24(2):75-98. doi: 10.3109/0954898X.2013.789568.

PMID:
23742213
31.

Decoding arm and hand movements across layers of the macaque frontal cortices.

Wong YT, Vigeral M, Putrino D, Pfau D, Merel J, Paninski L, Pesaran B.

Conf Proc IEEE Eng Med Biol Soc. 2012;2012:1757-60. doi: 10.1109/EMBC.2012.6346289.

32.

Monte Carlo methods for localization of cones given multielectrode retinal ganglion cell recordings.

Sadeghi K, Gauthier JL, Field GD, Greschner M, Agne M, Chichilnisky EJ, Paninski L.

Network. 2013;24(1):27-51. doi: 10.3109/0954898X.2012.740140. Epub 2012 Nov 29.

33.

Efficient coding of spatial information in the primate retina.

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.

J Neurosci. 2012 Nov 14;32(46):16256-64. doi: 10.1523/JNEUROSCI.4036-12.2012.

34.

Fast spatiotemporal smoothing of calcium measurements in dendritic trees.

Pnevmatikakis EA, Kelleher K, Chen R, Saggau P, Josić K, Paninski L.

PLoS Comput Biol. 2012 Jun;8(6):e1002569. doi: 10.1371/journal.pcbi.1002569. Epub 2012 Jun 28.

35.

A Bayesian compressed-sensing approach for reconstructing neural connectivity from subsampled anatomical data.

Mishchenko Y, Paninski L.

J Comput Neurosci. 2012 Oct;33(2):371-88. doi: 10.1007/s10827-012-0390-z. Epub 2012 Mar 22.

PMID:
22437567
36.

Modeling the impact of common noise inputs on the network activity of retinal ganglion cells.

Vidne M, Ahmadian Y, Shlens J, Pillow JW, Kulkarni J, Litke AM, Chichilnisky EJ, Simoncelli E, Paninski L.

J Comput Neurosci. 2012 Aug;33(1):97-121. doi: 10.1007/s10827-011-0376-2. Epub 2011 Dec 29.

37.

Inferring synaptic inputs given a noisy voltage trace via sequential Monte Carlo methods.

Paninski L, Vidne M, DePasquale B, Ferreira DG.

J Comput Neurosci. 2012 Aug;33(1):1-19. doi: 10.1007/s10827-011-0371-7. Epub 2011 Nov 17.

PMID:
22089473
38.

Optimal experimental design for sampling voltage on dendritic trees in the low-SNR regime.

Huggins JH, Paninski L.

J Comput Neurosci. 2012 Apr;32(2):347-66. doi: 10.1007/s10827-011-0357-5. Epub 2011 Aug 23.

PMID:
21861199
39.

Temporal precision in the visual pathway through the interplay of excitation and stimulus-driven suppression.

Butts DA, Weng C, Jin J, Alonso JM, Paninski L.

J Neurosci. 2011 Aug 3;31(31):11313-27. doi: 10.1523/JNEUROSCI.0434-11.2011.

40.

Imaging action potentials with calcium indicators.

Yuste R, MacLean J, Vogelstein J, Paninski L.

Cold Spring Harb Protoc. 2011 Aug 1;2011(8):985-9. doi: 10.1101/pdb.prot5650. No abstract available.

PMID:
21807854
41.

EMG prediction from motor cortical recordings via a nonnegative point-process filter.

Nazarpour K, Ethier C, Paninski L, Rebesco JM, Miall RC, Miller LE.

IEEE Trans Biomed Eng. 2012 Jul;59(7):1829-38. doi: 10.1109/TBME.2011.2159115. Epub 2011 Jun 9.

42.

Designing optimal stimuli to control neuronal spike timing.

Ahmadian Y, Packer AM, Yuste R, Paninski L.

J Neurophysiol. 2011 Aug;106(2):1038-53. doi: 10.1152/jn.00427.2010. Epub 2011 Apr 20.

43.

Incorporating naturalistic correlation structure improves spectrogram reconstruction from neuronal activity in the songbird auditory midbrain.

Ramirez AD, Ahmadian Y, Schumacher J, Schneider D, Woolley SM, Paninski L.

J Neurosci. 2011 Mar 9;31(10):3828-42. doi: 10.1523/JNEUROSCI.3256-10.2011.

44.

Hidden Markov models for the stimulus-response relationships of multistate neural systems.

Escola S, Fontanini A, Katz D, Paninski L.

Neural Comput. 2011 May;23(5):1071-132. doi: 10.1162/NECO_a_00118. Epub 2011 Feb 7.

PMID:
21299424
45.

A generalized linear model for estimating spectrotemporal receptive fields from responses to natural sounds.

Calabrese A, Schumacher JW, Schneider DM, Paninski L, Woolley SM.

PLoS One. 2011 Jan 11;6(1):e16104. doi: 10.1371/journal.pone.0016104.

46.

Kalman filter mixture model for spike sorting of non-stationary data.

Calabrese A, Paninski L.

J Neurosci Methods. 2011 Mar 15;196(1):159-69. doi: 10.1016/j.jneumeth.2010.12.002. Epub 2010 Dec 21.

PMID:
21182868
47.

Efficient, adaptive estimation of two-dimensional firing rate surfaces via Gaussian process methods.

Rad KR, Paninski L.

Network. 2010;21(3-4):142-68. doi: 10.3109/0954898X.2010.532288.

PMID:
21138363
48.

Efficient Markov chain Monte Carlo methods for decoding neural spike trains.

Ahmadian Y, Pillow JW, Paninski L.

Neural Comput. 2011 Jan;23(1):46-96. doi: 10.1162/NECO_a_00059. Epub 2010 Oct 21.

49.

Model-based decoding, information estimation, and change-point detection techniques for multineuron spike trains.

Pillow JW, Ahmadian Y, Paninski L.

Neural Comput. 2011 Jan;23(1):1-45. doi: 10.1162/NECO_a_00058. Epub 2010 Oct 21.

PMID:
20964538
50.

Functional connectivity in the retina at the resolution of photoreceptors.

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.

Nature. 2010 Oct 7;467(7316):673-7. doi: 10.1038/nature09424.

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