Current position
Professor, Departments of Statistics and Neuroscience, Center for Theoretical Neuroscience, Doctoral Program in Neurobiology and Behavior, Zuckerman Mind Brain Behavior Institute, and Kavli Institute for Brain Science, Columbia University.
Co-director, Grossman Center for the Statistics of Mind.
Co-director, Columbia NeuroTechnology Center.
Education
New York University;
Ph.D., Neural Science (2003).
Brown
University; B.S., Neuroscience (1999).
Previous experience
Assistant (2005-8) and Associate (2008-13) Professor, Department of
Statistics,
Columbia University.
Senior research fellow,
Gatsby Computational Neuroscience
Unit, University College London
(2004-5).
Postdoctoral fellow, Center for
Neural Science, HHMI, NYU (2003).
Papers
Whiteway, M. et al (2021).
Semi-supervised sequence modeling for improved behavioral segmentation.
Submitted.
Whiteway, M. et al (2021).
Partitioning variability in animal behavioral videos using semi-supervised variational autoencoders.
Biorxiv 2021.02.22.432309.
Varol, E. et al (2021).
Decentralized motion inference and registration of Neuropixels data.
ICASSP.
Kim, Y. et al (2021).
Nonlinear decoding of natural images from large-scale primate retinal ganglion recordings.
Biorxiv 2020.09.07.285742; in press, Neural Computation.
Couto, J. et al (2021). Chronic, cortex-wide imaging of specific cell populations during behavior.
arXiv 2010.15191; in press, Nature Protocols.
Chen, S. et al (2021). BARcode DEmixing through Non-negative Spatial Regression (BarDensr).
PLoS Comput. Bio. 1008256.
Xie, M. et al (2021). High fidelity estimates of spikes and subthreshold waveforms from 1-photon voltage imaging in vivo. Biorxiv 920256; in press, Cell Reports.
Turner, N. et al (2020). Multiscale and multimodal reconstruction of cortical structure and function.
Biorxiv 2020.10.14.338681.
Abe, T. et al (2020). Neuroscience cloud analysis as a service.
Biorxiv 146746.
Pakman, A. et al (2020). Attentive clustering processes.
arXiv 2010.15191.
Glaser, J. et al (2020). Recurrent Switching Dynamical Systems Models for Multiple Interacting Neural Populations.
Neurips.
Nejatbaksh, A. et al (2020). Probabilistic Joint Segmentation and Labeling of C. elegans Neurons.
MICCAI
Varol, E. et al (2020). Statistical Atlas of C. elegans Neurons.
MICCAI
Nejatbaksh, A. et al (2020). Extracting neural signals from semi-immobilized animals with deformable non-negative matrix factorization.
Biorxiv 192120
Wu, A., Buchanan, E.K. et al (2020). Deep Graph Pose: a semi-supervised deep graphical model for improved animal pose tracking.
Neurips.
Zhou, D. et al. Disentangled sticky hierarchical Dirichlet
process hidden Markov model. ArXiv 2004.03019; ECML-PKDD.
Lee, P. et al (2020). YASS: Yet Another Spike Sorter applied to large-scale multi-electrode array
recordings in primate retina. Biorxiv 997924.
Loper, J. et al (2020). General linear-time inference for Gaussian Processes on one dimension. ArXiv 2003.05554.
Zhou, P. et al (2020). EASE: EM-assisted Source Extraction from calcium imaging data. Biorxiv 007468.
Saxena, S. et al (2020). Localized semi-nonnegative matrix factorization (LocaNMF) of widefield calcium imaging data. PLoS Comput. Bio.
Lu, R., Liang, Y. et al (2020). Rapid mesoscale volumetric imaging of neural activity with synaptic resolution. Nature Methods.
Pakman, A. et al (2020). Discrete neural processes. ICML.
Yemini, E. et al (2020). NeuroPAL: A Neuronal Polychromatic Atlas of Landmarks for whole-brain imaging in C. elegans. Cell.
Linderman, S. et al (2019). Hierarchical recurrent state space models reveal discrete and
continuous dynamics of neural activity in C. elegans. Biorxiv 621540.
Wei, X., Zhou, D. et al (2019). A zero-inflated gamma model for post-deconvolved calcium imaging traces. NBDT.
Shah, N. et al (2019). Efficient characterization of electrically evoked responses for neural interfaces. Neurips.
Sun, R. et al (2019). Scalable Bayesian inference of dendritic voltage via spatiotemporal recurrent state space models. Neurips.
Batty, E., Whiteway, M. et al (2019). BehaveNet: nonlinear embedding and Bayesian neural decoding of behavioral videos. Neurips.
Abdelfattah, A. et al (2019). Bright and photostable chemigenetic indicators for extended in vivo voltage imaging. Science.
Adam, Y. et al (2019). Voltage imaging and optogenetics reveal behaviour-dependent changes in hippocampal dynamics. Nature.
Lacefield, C., Pnevmatikakis, E., Paninski, L. & Bruno, R. (2019). Reinforcement learning recruits somata and apical dendrites across layers of primary sensory cortex. Cell Reports.
Naka, A. et al (2019). Complementary networks of cortical somatostatin interneurons enforce layer specific control. eLife.
Loper, J. et al (2018+). The Markov link method: a nonparametric approach to combine observations from multiple experiments. Biorxiv 457283.
Buchanan, E.K., Kinsella, I., Ding, Z. et al (2018+). Penalized matrix decomposition for denoising, compression, and improved demixing of functional imaging data. Biorxiv 334706.
Sun, R. and Paninski, L. (2018). Scalable approximate Bayesian inference for particle tracking data. ICML.
Zhou, P. et al (2018). Efficient and accurate extraction of in vivo calcium
signals from microendoscopic video data. eLife 7:e28728.
Jimenez, J. et al (2018). Anxiety cells in a hippocampal-hypothalamic circuit. Neuron 97: 670-683.
Berens et al (2018). Community-based benchmarking improves spike inference from two-photon calcium imaging data. PLoS Comput. Bio.
Linderman, S., Mena, G. et al (2018). Reparameterizing the Birkhoff Polytope for Variational Permutation Inference. AISTATS.
International Brain Lab (2017). An international laboratory for systems
and computational neuroscience. Neuron 96: 1213-1218.
Yu et al (2017). The central amygdala controls learning in the lateral amygdala. Nature Neuroscience 20: 1680-1685.
Klaus et al (2017). The spatiotemporal organization of the striatum encodes action space. Neuron 95: 1171-1180.
Giovanucci, A. et al (2017). OnACID: Online Analysis of Calcium Imaging Data in Real Time. NIPS.
Parthasarathy, N., Batty, E. et al (2017). Deep Networks for Decoding Natural Images from Retinal Signals.
NIPS.
Lee, J. et al (2017). YASS: Yet another spike sorter.
NIPS.
Giovanucci et al (2017). Cerebellar granule cells acquire a widespread feedback control signal during motor learning.
Nature Neuroscience 20: 727-734.
Buesing et al (2017). A Statistical Model of Shared Variability in the Songbird Auditory System. bioRxiv 113670.
Friedrich, J. et al (2017). Multi-scale approaches for high-speed imaging and analysis of large neural populations.
PLoS Comp. Bio. 13: e1005685.
Mena, G. et al (2017). Removing Stimulation Artifacts From Neural Recordings Using Structured Gaussian Processes.
PLoS Comp. Bio.
Batty, E. et al (2017). Multilayer Network Models of Primate Retinal Ganglion Cells. ICLR.
Sun, R., Archer, E. & Paninski, L. (2017). Variational inference for super resolution microscopy. AISTATS.
Linderman, S., Miller, A., Adams, R., Blei, D., Paninski, L., Johnson, M. (2017). Recurrent Switching Linear Dynamical Systems. AISTATS.
Pakman, A., Gilboa, D., Carlson, D. & Paninski, L. (2017). Stochastic Bouncy Particle Sampler. ICML.
Rahnama Rad, K., Machado, T. & Paninski, L. (2017). Robust and scalable Bayesian analysis of spatial neural tuning function data. Ann. Applied Stat.
Sumbul, U., Roossien, D., Chen, F., Barry, N., Boyden, E., Cai, D., Cunningham, J. & Paninski, L. (2016). Automated scalable segmentation of neurons from
multispectral images. NIPS.
Gao, Y., Archer, E., Paninski, L. & Cunningham, J. (2016). Latent linear-dynamical neural population models
through nonlinear embedding. NIPS.
Friedrich, J., Zhou, P. & Paninski, L. (2016). Fast Active Set Method for Online Spike Inference
from Calcium Imaging. NIPS; PLoS Comput. Bio. 13: e1005423.
Merel, J., Shababo, B., Naka, A., Adesnik, H. & Paninski, L. (2016). Bayesian methods for event analysis of intracellular currents. Journal of Neuroscience Methods 269: 21-32.
Merel, J., Carlson, D., Paninski, L. & Cunningham, J. (2016). Neuroprosthetic decoder training as imitation learning. PLoS Comp. Bio 12: e1004948.
Carlson, D., Stinson, P., Pakman, A. & Paninski, L. (2016). Partition Functions from Rao-Blackwellized Tempered Sampling. ICML.
Picardo, M., Merel, J., Katlowitz, K., Vallentin, D., Okobi, D, Benezra, S., Clary, R., Pnevmatikakis, E., Paninski, L., and Long, M. (2016). Population-level representation of a temporal sequence underlying skilled behavior. Neuron 90: 866-876.
Gabitto M., Pakman A., Bikoff J., Abbott L., Jessell T. & Paninski, L. (2016).
Bayesian sparse regression analysis reveals the extent of spinal V1 interneuron diversity. Cell, 165: 220-33.
Pnevmatikakis, E., Soudry, D., Gao, Y., Machado, T., Merel, J., Pfau, D.,
Reardon, T., Mu, Y., Lacefield, C., Yang, W., Ahrens, M., Bruno, R., Jessell, T.,
Yuste, R., Peterka, D. & Paninski, L. (2016). Simultaneous denoising, deconvolution, and demixing of calcium imaging data. Neuron 89: 285-299.
Yang, W., Miller, J., Carillo-Reid, L. Pnevmatikakis, E.,
Paninski, L., Yuste, R., & Peterka, D.
(2016). Simultaneous multi-plane imaging of neural circuits. Neuron 89: 269-84.
Archer, E., Park, M., Buesing, L., Cunningham, J. & Paninski, L. (2015). Black-box variational inference for state-space models. arXiv:1511.07367
Freeman, J., Field, G., Li, P., Greschner, M., Gunning, D., Mathieson, K., Sher, A., Litke, A., Paninski, L., Simoncelli, E. & Chichilnisky, E.J. (2015). Mapping nonlinear receptive field structure in primate retina at single cone resolution. eLife 4:e05241.
Soudry, D., Keshri, S., Stinson, P., Oh, M.-W., Iyengar, G. & Paninski, L. (2015). Efficient ``shotgun" inference of neural connectivity from highly sub-sampled activity data. PLoS Comp. Bio. 11: e1004464.
Machado, T., Miri, A., Pnevmatikakis, E., Paninski, L. & Jessell, T. (2015). Primacy of flexor locomotor pattern revealed by ancestral reversion of motor neuron identity. Cell 162: 338-350.
Merel, J., Pianto, D., Cunningham, J. & Paninski, L. (2015).
Encoder-decoder optimization for brain-computer interfaces. PLoS Comp. Bio 11: e1004288.
Buesing, L., Machado, T., Cunningham, J. & Paninski, L. (2014).
Clustered factor analysis of multineuronal spike data. NIPS.
Ramirez, A., Pnevmatikakis, E., Merel, J., Miller, K., Paninski, L. & Bruno, R. (2014). Spatiotemporal receptive fields of barrel cortex neurons revealed by reverse correlation of synaptic input. Nat. Neurosci. 17: 866-75.
Mena, G. & Paninski, L. (2014). On quadrature methods for refractory point process likelihoods. Neural Computation 26: 2790-7.
Pnevmatikakis, E., Merel, J., Pakman, A. & Paninski, L. (2014). Bayesian spike inference from calcium imaging data. Asilomar Conf. on Signals, Systems, and Computers.
Pakman, A., Huggins, J., Smith, C. & Paninski, L. (2014).
Fast penalized state-space methods for inferring
dendritic synaptic
connectivity.
J. Comput. Neurosci. 36: 415-43
Ramirez, A. & Paninski, L. (2014). Fast generalized
linear model estimation via expected
log-likelihoods.
J. Comput. Neurosci. 36: 215-34.
Shababo, B., Paige, B., Pakman, A. & Paninski, L. (2013).
Bayesian inference and online experimental design
for mapping neural
microcircuits.
NIPS.
Pnevmatikakis, E. and Paninski, L. (2013).
Sparse nonnegative deconvolution for compressive
calcium imaging: algorithms and phase
transitions.
NIPS.
Pfau, D., Pnevmatikakis, E. & Paninski, L. (2013).
Robust learning of low-dimensional dynamics from
large neural ensembles.
NIPS.
Pakman, A. and Paninski, L. (2013).
Auxiliary-variable exact Hamiltonian Monte Carlo
samplers for binary
distributions.
NIPS.
Merel, J., Fox, R., Jebara, T. & Paninski, L. (2013).
A multi-agent control framework for co-adaptation
in brain-computer interfaces.
NIPS.
Smith, C. & Paninski, L. (2013). Computing loss of efficiency in
optimal Bayesian decoders given noisy or incomplete spike trains.
Network: Computation in Neural Systems 24: 75-98.
Pakman, A. & Paninski, L. (2013). Efficient
multivariate truncated normal sampling via exact Hamiltonian Monte
Carlo.
J. Comput. Graph. Stat. 23.
Pnevmatikakis, E., Rahnama Rad, K., Huggins, J., & Paninski, L. (2013).
Fast Kalman filtering and forward-backward
smoothing via a low-rank perturbative
approach.
J. Comput. Graph. Stat. 23.
Sadeghi et al. (2013). Monte Carlo methods
for localization of cones given multielectrode retinal ganglion cell
recordings.
Network: Computation in Neural Systems 24: 27-51.
Doi et al. (2012). Efficient coding of
spatial information in the primate
retina.
Journal of
Neuroscience 32: 16256-16264.
Pnevmatikakis, E., Kelleher, K., Chen, R., Josic, K., Saggau,
P. & Paninski, L. (2012). Fast nonnegative spatiotemporal
calcium smoothing in dendritic trees.
PLoS Comp. Bio. 8: e1002569.
Paninski, L., Rahnama Rad, K. & Vidne, M. (2012).
Robust particle filters via sequential pairwise
reparameterized Gibbs sampling.
CISS `12.
Mishchenko, Y. & Paninski, L. (2012) Bayesian
compressed sensing approach to reconstructing neural connectivity from
subsampled anatomical data.
J. Comput. Neuro. 33: 371-88.
Pnevmatikakis & Paninski, L. (2012). Fast
interior-point inference in high-dimensional sparse, penalized
state-space models.
AISTATS `12.
Smith, C., Wood, F. & Paninski, L. (2012). Low rank
continuous-space graphical models.
AISTATS `12.
Vidne et al. (2012). The impact of common
noise on the activity of a large network of retinal ganglion
cells.
J. Comput. Neuro. 33: 97-121.
Paninski, L., Vidne, M., DePasquale, B., & Ferreira, D. (2012).
Inferring synaptic inputs given a noisy voltage
trace.
J. Comput. Neuro. 33:
1-19.
Huggins, J. & Paninski, L. (2012). Optimal
experimental design for sampling voltage on dendritic
trees.
J. Comput. Neuro. 32:
347-66.
Nazarpour, K., Ethier, C., Paninski, L., Rebesco, J., Miall, C., &
Miller, L. (2011). EMG prediction from motor
cortical recordings via a non-negative point process
filter.
IEEE Transactions on Biomedical
Engineering 59: 1829-1838.
Rahnama Rad, K. & Paninski, L. (2011). Information
rates and optimal decoding in large neural
populations.
NIPS.
Mishchenko, Y. & Paninski, L. (2011). Efficient
methods for sampling spike trains in networks of coupled
neurons.
Annals of
Applied Statistics 5: 1893-1919.
Ahmadian, Y., Packer, A., Yuste, R. & Paninski, L. (2011).
Designing optimal stimuli to control neuronal
spike timing.
J. Neurophys. 106:
1038-1053.
Butts, D., Weng, C., Jin, J. Alonso, J.-M. & Paninski, L. (2011).
Temporal precision in the visual pathway through
the interplay of excitation and stimulus-driven
suppression
J. Neurosci. 31: 11313-11327.
Mishchenko, Y., Vogelstein, J. & Paninski, L. (2011).
A Bayesian approach for inferring neuronal
connectivity from calcium fluorescent imaging
data.
Annals of Applied
Statistics 5: 1229-1261.
Ramirez, A., Ahmadian, Y., Schumacher, J., Schneider, D.,
Woolley, S. & Paninski, L. (2011). Incorporating
naturalistic correlation structure improves spectrogram reconstruction
from neuronal activity in the songbird auditory
midbrain.
J. Neurosci. 31:
3828-42.
Escola, S., Fontanini, A., Katz, D. & Paninski, L.
(2011). Hidden Markov models for the inference
of neural states and improved estimation of linear receptive
fields.
Neural Computation 23:
1071-1132.
Calabrese, A. & Paninski, L. (2011). Kalman
filter mixture model for spike sorting of non-stationary
data.
J. Neurosci. Methods 196:
159-169.
Calabrese, A., Schumacher, J., Schneider, D., Woolley, S. & Paninski, L.
(2011). A penalized GLM approach for estimating
spectrotemporal receptive fields from responses to natural
sounds.
PLoS One 6(1): e16104.
Lewi, J., Schneider, D., Woolley, S. & Paninski, L. (2011).
Automating the design of informative sequences of
sensory stimuli.
Journal of
Computational Neuroscience 30: 181-200 (special issue on methods of
information theory in neuroscience research).
Ahmadian, Y., Pillow, J. & Paninski, L. (2011).
Efficient Markov Chain Monte Carlo methods for
decoding population spike trains.
Neural
Computation 23: 46-96.
Pillow, J., Ahmadian, Y. & Paninski, L. (2011).
Model-based decoding, information estimation, and
change-point detection in multi-neuron spike
trains.
Neural Computation 23: 1-45.
Vogelstein, J., Packer, A., Machado, T., Sippy, T., Babadi, B.,
Yuste, R. & Paninski, L. (2010). Fast non-negative
deconvolution for spike train inference from calcium
imaging.
J. Neurophys. 104: 3691-3704
Field, G., Gauthier, J., Sher, A. et al. (2010).
Functional connectivity in the retina at the
resolution of photoreceptors.
Nature 467,
673-677.
Rahnama Rad, K. & Paninski, L. (2010). Efficient
estimation of two-dimensional firing rate surfaces via Gaussian
process methods.
Network:
Computation in Neural Systems 21: 142-68.
Paninski, L., Ahmadian, Y., Ferreira, D., Koyama, S., Rahnama, K., Vidne,
M., Vogelstein, J. & Wu, W. (2010). A new look
at state-space models for neural
data.
Journal of Computational
Neuroscience (special issue on statistical analysis of neural data)
29: 107-126.
Koyama, S. & Paninski, L. (2010). Efficient
computation of the most likely path in integrate-and-fire and more
general state-space models.
Journal of
Computational Neuroscience 29: 89-105.
Lawhern, V., Wu, W., Hatsopoulos, N. & Paninski, L. (2010).
Population neuronal decoding using a generalized
linear model with hidden states.
J. Neurosci. Methods 189: 267-280.
Babadi, B., Casti, A., Xiao, Y. & Paninski, L. (2010).
A generalized linear model of the impact of direct
and indirect inputs to the lateral geniculate
nucleus.
Journal of Vision 10: 22.
Field, R., Lary, J., Cohn, J., Paninski, L. & Shepard, K. (2010). A
low-noise, single-photon avalanche diode in standard 0.13 micron
complementary metal-oxide-semiconductor process. Applied Physics
Letters 97, 211111.
Paninski, L. (2010). Fast Kalman filtering on
quasilinear dendritic trees.
Journal of
Computational Neuroscience 28: 211-28.
Lalor, E., Ahmadian, Y. & Paninski, L. (2009). The
relationship between optimal and biologically plausible decoding of
stimulus velocity in the retina.
Journal of
the Optical Society of America A (special issue on ideal observers and
efficiency) 26: B25-42.
Vogelstein, J., Watson, B., Packer, A., Yuste, R., Jedynak, B. &
Paninski, L. (2009). Spike inference from calcium imaging
using sequential Monte Carlo methods.
Biophysical Journal 97: 636-655.
Wu, W., Kulkarni, J., Hatsopoulos, N. & Paninski, L. (2009).
Neural decoding of goal-directed movements using a
linear state-space model with hidden
states.
IEEE Transactions on Neural Systems and
Rehabilitation Engineering 17: 370-378.
Escola, S., Eisele, M., Miller, K. & Paninski, L. (2009).
Maximally reliable Markov chains under energy
constraints.
Neural
Computation 21: 1863-912.
Toyoizumi, T., Rahnama Rad, K. & Paninski, L. (2009).
Mean-field approximations for coupled populations
of generalized linear model spiking
neurons.
Neural Computation 21,
1203-1243.
Huys, Q. & Paninski, L. (2009). Smoothing of, and
parameter estimation from, noisy biophysical
recordings.
PLOS Computational Biology 5:
e1000379.
Lewi, J., Butera, R. & Paninski, L. (2009).
Sequential optimal design of neurophysiology
experiments.
Neural Computation 21:
619-687.
Fudenberg, G. Paninski, L. (2009). Bayesian image
recovery for low-SNR dendritic
structures.
IEEE Trans. Image
Processing 18: 471-482.
Lewi, J., Butera, R., Schneider, D., Woolley, S. & Paninski, L. (2008).
Designing neurophysiology experiments to optimally
constrain receptive field models along parametric
submanifolds.
NIPS.
Paninski, L. (2008). A coincidence-based test for
uniformity given very sparsely-sampled discrete
data.
IEEE Transactions on Information
Theory 54: 4750-4755.
Pillow, J., Shlens, J., Paninski, L., Sher, A., Litke, A., Chichilnisky,
E. & Simoncelli, E. (2008). Spatiotemporal
correlations and visual signaling in a complete neuronal
population.
Nature 454: 995-999.
Paninski, L. & Yajima, M. (2008). Undersmoothed
kernel entropy estimators.
IEEE
Transactions on Information Theory 54: 4384-4388.
Kulkarni, J. & Paninski, L. (2008). Efficient
analytic computational methods for state-space decoding of
goal-directed movements.
IEEE
Signal Processing Magazine 25 (special issue on brain-computer
interfaces): 78-86.
Ahrens, M., Paninski, L. & Sahani, M. (2008).
Inferring input nonlinearities in neural encoding
models.
Network: Computation in
Neural Systems 19: 35-67.
Paninski, L., Haith, A. & Szirtes, G. (2008).
Differentiable integral equation methods for
computing likelihoods in the stochastic integrate-and-fire
model.
J. Comput. Neuroscience 24:
69-79.
Kulkarni, J. & Paninski, L. (2007). Common-input
models for multiple neural spike train
data.
Network: Computation in Neural
Systems 18: 375-407.
Lewi, J., Butera, R. & Paninski, L. (2007).
Efficient active learning with generalized linear
models.
Artificial Intelligence and
Statistics (AISTATS) 11.
Townsend, B., Paninski, L. & Lemon, R. (2006).
Linear encoding of muscle activity in primary motor
cortex and cerebellum.
J. Neurophys. 96: 2578-92.
Huys, Q., Ahrens, M. & Paninski, L. (2006).
Efficient estimation of detailed single-neuron
models.
Journal of Neurophysiology 96: 872-890.
Paninski, L. (2006). The spike-triggered average of the
integrate-and-fire cell driven by Gaussian white noise.
Neural Computation 18: 2592-2616.
Paninski, L. (2006). The most likely voltage path and
large deviations approximations for integrate-and-fire
neurons.
Journal of Computational
Neuroscience 21: 71-87.
Pillow, J., Paninski, L., Uzzell, V., Simoncelli, E. & Chichilnisky,
E. (2005). Structure and precision of retinal
responses analyzed with a noisy integrate-and-fire
model.
J. Neurosci. 25: 11003-13.
Paninski, L. (2005). Inferring prior probabilities from
Bayes-optimal behavior.
Advances in Neural
Information Processing 18.
Shoham, S., Paninski, L., Fellows, M., Hatsopoulos, N., Donoghue, J. &
Normann, R. (2005). Optimal decoding for a primary
motor cortical brain-computer interface.
IEEE
Transactions on Biomedical Engineering 52: 1312-1322.
Paninski, L. (2005). Asymptotic theory of
information-theoretic experimental design.
Neural
Computation 17: 1480-1507.
Paninski, L. (2004). Log-concavity results on
Gaussian process methods for supervised and unsupervised
learning.
Advances in Neural Information
Processing 17.
Paninski, L. (2004). Variational minimax estimation
of discrete distributions under Kullback-Leibler
loss.
Advances in Neural Information
Processing 17.
Paninski, L. (2004). Maximum likelihood estimation of
cascade point-process neural encoding
models.
Network: Computation in
Neural Systems 15: 243-262.
Paninski, L., Pillow, J. & Simoncelli, E. (2004).
Comparing integrate-and-fire-like models estimated
using intra- and extra-cellular data.
Neurocomputing 65: 379-385.
Paninski, L., Pillow, J. & Simoncelli, E. (2004).
Maximum likelihood estimation of a stochastic
integrate-and-fire neural encoding model.
Neural Computation 16: 2533-2561.
Paninski, L. et al. (2004). Superlinear population
encoding of dynamic hand trajectory in primary motor
cortex.
Journal of Neuroscience 24:
8551-8561.
Paninski, L. (2004). Estimating entropy on m bins
given fewer than m samples.
IEEE
Transactions on Information Theory 50: 2200-2203.
Paninski, L., Fellows, M., Hatsopoulos, N. & Donoghue, J. (2004).
Spatiotemporal tuning properties for hand position
and velocity in motor cortical neurons.
Journal of Neurophysiology 91: 515-532.
Hatsopoulos, N., Paninski, L. & Donoghue, J. (2003).
Sequential movement representations based on
correlated neuronal activity.
Experimental Brain Research 149:
478-486.
Serruya, M., Hatsopoulos, N., Paninski, L., Fellows, M. & Donoghue, J.
(2003). Robustness of neuroprosthetic decoding
algorithms.
Biological Cybernetics 88: 219-228.
Paninski, L. (2003). Estimation of entropy and mutual
information.
Neural Comp. 15:
1191-1253.
Paninski, L. (2003). Convergence properties of three
spike-triggered analysis techniques.
Network: Computation in Neural Systems 14: 437-464. (Special issue on
natural scene statistics and neural codes.)
Paninski, L., Lau, B. & Reyes, A. (2003). Noise-driven
adaptation: in vitro and mathematical
analysis.
Neurocomputing 52: 877-883.
Serruya, M., Hatsopoulos, N., Paninski, L., Fellows, M. & Donoghue, J. (2002).
Instant neural control of a movement
signal.
Nature 416: 141-142.
Paninski, L. & Hawken, M. (2001). Stochastic optimal control
and the human oculomotor system.
Neurocomputing,
38-40: 1511-1517.
Hatsopoulos, N,, Ojakangas, C., Paninski, L. & Donoghue, J. (1998).
Information about movement direction obtained from
synchronous activity of motor cortical neurons.
PNAS 95: 15706-11.
Books
Gerstner, W., Kistler, W., Naud, R. & Paninski, L. (2014). Neuronal dynamics.
Cambridge U. Press.
Invited reviews / book chapters
Pnevmatikakis, E. & Paninski, L. (2018). Analysis of functional imaging data at single-cellular resolution. SFN Short Course on Functional, Structural, and Molecular Imaging, and Big Data Analysis.
Paninski, L. & Cunningham, J. (2018). Neural data science: accelerating the experiment-analysis-theory cycle in large-scale
neuroscience. Invited review, Current Opinion in Neurobiology; BioRxiv 196949.
International Brain Laboratory (2017). An International Laboratory for Systems and Computational Neuroscience. NeuroView, Neuron 96: 1213-1218.
Yuste, R., Watson, B., Paninski, L., Vogelstein, J. (2009). Imaging action
potentials with calcium indicators. Imaging Neurons: A
Laboratory Manual, 2ed., eds. Yuste, R. & Konnerth, A., CSHL
Press.
Paninski, L., Kass, R., Brown, E. & Iyengar, I. (2008).
Statistical analysis of neuronal data via
integrate-and-fire models.
Stochastic Methods in Neuroscience, eds. Laing, C. &
Lord, G., Oxford.
Paninski, L., Pillow, J. & Lewi, J. (2007). Statistical
models for neural encoding, decoding, and optimal stimulus
design.
Computational Neuroscience:
Progress in Brain Research, eds. Cisek, P., Drew, T. &
Kalaska, J.
Simoncelli, E., Paninski, L., Pillow, J. & Schwartz, O. (2004).
Characterization of neural responses with
stochastic stimuli.
Chapter 23 of The New
Cognitive Neurosciences, 3ed, ed. Gazzaniga,
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Grants
Collaborative Research in Computational Neuroscience, NEI R01
EY018003, co-PI w/ E. Simoncelli and E.J. Chichilnisky, 2006-12.
Gatsby Initiative in Brain Circuitry Pilot Grant, co-PI w/ S. Woolley, 2006-8.
Alfred P. Sloan Research Fellowship, 2007.
NSF Faculty Early Career Development (CAREER) IOS-0641912, 2007-12
McKnight Scholar award, 2008-12.
Collaborative Research in Comput. Neuroscience, NSF
IIS-0904353, co-PI w/ R. Yuste, 2009-12.
DARPA award, Reliable Neural-interface Technology program,
co-PI w/ B. Pesaran, 2011-3.
MURI award, ``Imaging how a neuron computes,'' co-PI w/ R. Yuste et al., 2012-8.
ONR award, Generalized Factor Analysis, Exact Hamiltonian Monte Carlo Methods, and Spike-and-Slab Models for Non-Gaussian Multivariate Analysis, 2014-.
Collaborative Research in Comput. Neuroscience, NSF
IIS-1430239 (Simoncelli and Chichilnisky, co-PIs), 2014-.
Simons Global Brain Research Awards (4) with M. Long, M. Ahrens, J. Freeman, L. Abbott, J. Cunningham, M. Churchland, S. Fusi, W. Freiwald, 2014-.
DARPA SIMPLEX program (Blei, Yuste, Jebara co-PIs), 2015-7.
IARPA MICrONS program (multiple co-PIs), 2015-.
NSF BIGDATA: Collaborative Research: IA: Hardware and software for spike detection and sorting in massively parallel electrophysiological recording systems for the brain (multiple co-PIs), 2015-19.
Google Faculty Award, 2015.
NIH BRAIN Initiative R01 EB22913: Next-Generation Calcium Imaging Analysis Methods, 2016-20.
NIH BRAIN Initiative R21 EY027592: Optimal calcium imaging with shaped excitation, co-PI D. Peterka, 2016-19.
DARPA NESD program (multiple co-PIs), 2016-.
NIH BRAIN Initiative 1U01NS103489-01: High-speed volumetric imaging of neural activity throughout the living brain, co-PI N. Ji et al, 2017-20.
International Brain Lab (multiple co-PIs), 2017-.
NSF Neuronex (multiple co-PIs), 2017-.
NIH BRAIN Initiative U19 Team: Computational and circuit mechanisms underlying motor control (multiple co-PIs), 2017-.
NIH BRAIN Initiative U19 Team: Understanding V1 circuit dynamics and computations (multiple co-PIs), 2018-.
CZI award: New statistical machine learning methods for fully exploiting heterogeneous, multimodal Human Cell Atlas data, 2018-20.
NIH BRAIN Initiative UF1NS107696.
kHz-rate in vivo imaging of neural activity throughout the living brain. Ji et al, 2018-.
NIH BRAIN Initiative 1RF1MH120680. High-throughput Physiological Micro-connectivity Mapping in Vivo. Adesnik et al, 2019-.
CRCNS: Topological and Dynamical Structures of Brain Development and Sexual-Dimorphism in C. Elegans (multiple co-PIs). 2019-.
Advising
Postdoctoral research advisor: J. Kulkarni, Q. Huys, Y. Ahmadian, Y. Mishchenko, L. Badel, E. Pnevmatikakis, K. Sadeghi,
A. Pakman, D. Pianto, L. Buesing, D. Soudry, U. Sumbul, E. Archer, A. Dubbs, J. Friedrich, L. Grosenick, D. Carlson, X. Deng, X. Wei, S. Chen, S. Linderman, D. Hernandez, P.C. Zhou, S. Saxena, M. Whiteway, J. Glaser, R. Zhu, J. Loper, C. Mitelut, E. Varol, A. Wu, M. Triplett
Ph.D. research advisor: S. Escola, J. Vogelstein, J. Lewi,
M. Nikitchenko, K. Rahnama Rad, M. Vidne, A. Ramirez, D. Ferreira, A. Calabrese, C. Smith, T. Machado, D. Pfau, J. Merel, G. Mena, E. Batty, R. Sun, P. Stinson, J. Lee, H. Razaghi, D. Zhou, I. Kinsella, E.K. Buchanan, A. Nejatbakhsh, S. Chen
M.A. research advisor: M. Yajima, C. Gohil, J. Bahk, W. Yao, N. Dethe, H. Lee
Undergraduate research advisor: G. Fudenberg, J. Huggins, A. Qian, T. Rutten, W. Falcon, S. Wu, Y.J. Kim, K. Li, A. Pasarkar, J. Zhou
Other duties
Mindscope advisory council, Allen Institute for Brain Science
Scientific advisor board, CTRL-Labs