Biblio

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Author Title Type [ Year(Asc)]
Filters: Author is Paninski, Liam  [Clear All Filters]
2025
Park P, Wong-Campos D, Itkis DG, Lee BHun, Qi Y, Davis H, Antin B, Pasarkar A, Grimm JB, Plutkis SE et al..  2025.  Dendritic excitations govern back-propagation via a spike-rate accelerometer. Nature Communications. 16(1)
Ye Z, Shelton AM, Shaker JR, Boussard J, Colonell J, Birman D, Manavi S, Chen S, Windolf C, Hurwitz C et al..  2025.  Ultra-high-density Neuropixels probes improve detection and identification in neuronal recordings.. Neuron. 113(23)
2023
Ye Z, Shelton AM, Shaker JR, Boussard J, Colonell J, Manavi S, Chen S, Windolf C, Hurwitz C, Namima T et al..  2023.  Ultra-high density electrodes improve detection, yield, and cell type specificity of brain recordings.. bioRxiv.
2021
Xie ME, Adam Y, Fan LZ, Böhm UL, Kinsella I, Zhou D, Rozsa M, Singh A, Svoboda K, Paninski L et al..  2021.  High-fidelity estimates of spikes and subthreshold waveforms from 1-photon voltage imaging in vivo.. Cell Reports. 35(1):108954.
2020
Lu R, Liang Y, Meng G, Zhou P, Svoboda K, Paninski L, Ji N.  2020.  Rapid mesoscale volumetric imaging of neural activity with synaptic resolution.. Nature Methods. 17(3):291-4.
2019
Abdelfattah AS, Kawashima T, Singh A, Novak O, Liu H, Shuai Y, Huang Y-C, Campagnola L, Seeman SC, Yu J et al..  2019.  Bright and photostable chemigenetic indicators for extended in vivo voltage imaging.. Science. 365(6454):699-704.
2018
Berens P, Freeman J, Deneux T, Chenkov N, McColgan T, Speiser A, Macke JH, Turaga SC, Mineault P, Rupprecht P et al..  2018.  Community-based benchmarking improves spike rate inference from two-photon calcium imaging data. PLOS Computational Biology. 14(5):e1006157.
2017
Berens P, Freeman J, Deneux T, Chenkov N, McColgan T, Speiser A, Macke JH, Turaga SC, Mineault P, Rupprecht P et al..  2017.  Community-based benchmarking improves spike inference from two-photon calcium imaging data.. PLoS Computational Biology. 14(5):e1006157.
Friedrich J, Yang W, Soudry D, Mu Y, Ahrens MB, Yuste R, Peterka DS, Paninski L.  2017.  Multi-scale approaches for high-speed imaging and analysis of large neural populations.. PLoS Computational Biology. 13(8):e1005685.
2016
Pnevmatikakis EA, Soudry D, Gao Y, Machado TA, Merel J, Pfau D, Reardon T, Mu Y, Lacefield C, Yang W et al..  2016.  Simultaneous denoising, deconvolution, and demixing of calcium imaging data.. Neuron. 89(2):285-99.
2015
Freeman J, Field GD, Li PH, Greschner M, Gunning DE, Mathieson K, Sher A, Litke AM, Paninski L, Simoncelli EP et al..  2015.  Mapping nonlinear receptive field structure in primate retina at single cone resolution.. eLife. 4
2008
Ahrens MB, Paninski L, Sahani M.  2008.  Inferring input nonlinearities in neural encoding models.. Network. 19(1):35-67.
2006
Huys QJM, Ahrens MB, Paninski L.  2006.  Efficient estimation of detailed single-neuron models.. Journal of Neurophysiology. 96(2):872-90.