Current position
Associate Professor, Department of Statistics, Center for Theoretical Neuroscience, Doctoral Program in Neurobiology and Behavior, and Kavli Institute for Brain Science, Columbia University.
Co-director, Grossman Center for the Statistics of Mind.
Education
New York University;
Ph.D., Neural Science (2003).
Brown
University; B.S., Neuroscience (1999).
Previous experience
Assistant Professor, Department of
Statistics, Center
for Theoretical Neuroscience,
and Doctoral Program in Neurobiology and
Behavior,
Columbia University (2005-8).
Senior research fellow,
Gatsby Computational Neuroscience
Unit, University College London
(2004-5).
Postdoctoral fellow, Center for
Neural Science, HHMI, NYU (2003).
Papers
[78] Ramirez, A. & Paninski, L. (2012). Fast generalized
linear model estimation via expected
log-likelihoods.
Under review.
[77] Smith, C. & Paninski, L. (2012). Computing loss of efficiency in
optimal Bayesian decoders given noisy or incomplete spike trains.
Under review.
[76] Pakman, A. & Paninski, L. (2012). Efficient multivariate truncated
normal sampling via exact Hamiltonian Monte Carlo.
Under minor
revision.
[75] Pakman, A., Huggins, J., & Paninski, L. (2012). Fast
penalized state-space methods for inferring dendritic synaptic
connectivity.
Under minor
revision.
[74] Sadeghi et al. (2012). Monte Carlo methods
for localization of cones given multielectrode retinal ganglion cell
recordings.
In press, Network: Computation in Neural Systems.
[73] Pnevmatikakis, E., Rahnama Rad, K., Huggins, J., & Paninski, L. . (2012)
Fast Kalman filtering and forward-backward
smoothing via a low-rank perturbative
approach.
In press,
J. Comput. Graph. Stat.
[72] Doi et al. (2012). Efficient coding of
spatial information in the primate
retina.
Journal of
Neuroscience 32: 16256-16264.
[71] 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.
[70] Paninski, L., Rahnama Rad, K. & Vidne, M. (2012).
Robust particle filters via sequential pairwise
reparameterized Gibbs sampling.
CISS '12.
[69] Mishchenko, Y. & Paninski, L. (2012) Bayesian
compressed sensing approach to reconstructing neural connectivity from
subsampled anatomical data.
J. Comput. Neuro. 33: 371-88.
[68] Pnevmatikakis & Paninski, L. (2012). Fast
interior-point inference in high-dimensional sparse, penalized
state-space models.
AISTATS '12.
[67] Smith, C., Wood, F. & Paninski, L. (2012). Low rank
continuous-space graphical models.
AISTATS '12.
[66] 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.
[65] Paninski, L., Vidne, M., DePasquale, B., & Ferreira, D. (2012).
Inferring synaptic inputs given a noisy voltage
trace.
J. Comput. Neuro. 33:
1-19.
[64] Huggins, J. & Paninski, L. (2012). Optimal
experimental design for sampling voltage on dendritic
trees.
J. Comput. Neuro. 32:
347-66.
[63] 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.
[62] Rahnama Rad, K. & Paninski, L. (2011). Information
rates and optimal decoding in large neural
populations.
NIPS.
[61] Mishchenko, Y. & Paninski, L. (2011). Efficient
methods for sampling spike trains in networks of coupled
neurons.
Annals of
Applied Statistics 5: 1893-1919.
[60] Ahmadian, Y., Packer, A., Yuste, R. & Paninski, L. (2011).
Designing optimal stimuli to control neuronal
spike timing.
J. Neurophys. 106:
1038-1053.
[59] 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.
[58] 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.
[57] 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.
[56] 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.
[55] Calabrese, A. & Paninski, L. (2011). Kalman
filter mixture model for spike sorting of non-stationary
data.
J. Neurosci. Methods 196:
159-169.
[54] 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.
[53] 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).
[52] Ahmadian, Y., Pillow, J. & Paninski, L. (2011).
Efficient Markov Chain Monte Carlo methods for
decoding population spike trains.
Neural
Computation 23: 46-96.
[51] 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.
[50] 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
[49] Field, G., Gauthier, J., Sher, A. et al. (2010).
Functional connectivity in the retina at the
resolution of photoreceptors.
Nature 467,
673-677.
[48] 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.
[47] 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.
[46] 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.
[45] 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.
[44] 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.
[43] 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.
[42] Paninski, L. (2010). Fast Kalman filtering on
quasilinear dendritic trees.
Journal of
Computational Neuroscience 28: 211-28.
[41] 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.
[40] 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.
[39] 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 Trans. Neural Systems and
Rehabilitation Engineering 17: 370-378.
[38] Escola, S., Eisele, M., Miller, K. & Paninski, L. (2009).
Maximally reliable Markov chains under energy
constraints.
Neural
Computation 21: 1863-912.
[37] 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.
[36] Huys, Q. & Paninski, L. (2009). Smoothing of, and
parameter estimation from, noisy biophysical
recordings.
PLOS Computational Biology 5:
e1000379.
[35] Lewi, J., Butera, R. & Paninski, L. (2009).
Sequential optimal design of neurophysiology
experiments.
Neural Computation 21:
619-687.
[34] Fudenberg, G. Paninski, L. (2009). Bayesian image
recovery for low-SNR dendritic
structures.
IEEE Trans. Image
Processing 18: 471-482.
[33] Lewi, J., Butera, R., Schneider, D., Woolley, S. & Paninski, L. (2008).
Designing neurophysiology experiments to optimally
constrain receptive field models along parametric
submanifolds.
NIPS.
[32] Paninski, L. (2008). A coincidence-based test for
uniformity given very sparsely-sampled discrete
data.
IEEE Transactions on Information
Theory 54: 4750-4755.
[31] 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.
[30] Paninski, L. & Yajima, M. (2008). Undersmoothed
kernel entropy estimators.
IEEE
Transactions on Information Theory 54: 4384-4388.
[29] 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.
[28] Ahrens, M., Paninski, L. & Sahani, M. (2008).
Inferring input nonlinearities in neural encoding
models.
Network: Computation in
Neural Systems 19: 35-67.
[27] 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.
[26] Kulkarni, J. & Paninski, L. (2007). Common-input
models for multiple neural spike train
data.
Network: Computation in Neural
Systems 18: 375-407.
[25] Lewi, J., Butera, R. & Paninski, L. (2007).
Efficient active learning with generalized linear
models.
Artificial Intelligence and
Statistics (AISTATS) 11.
[24] Townsend, B., Paninski, L. & Lemon, R. (2006).
Linear encoding of muscle activity in primary motor
cortex and cerebellum.
J. Neurophys.
96: 2578-92.
[23] Huys, Q., Ahrens, M. & Paninski, L. (2006).
Efficient estimation of detailed single-neuron
models.
Journal of Neurophysiology 96: 872-890.
[22] Paninski, L. (2006). The spike-triggered average of the
integrate-and-fire cell driven by Gaussian white noise.
Neural Computation 18: 2592-2616.
[21] Paninski, L. (2006). The most likely voltage path and
large deviations approximations for integrate-and-fire
neurons.
Journal of Computational
Neuroscience 21: 71-87.
[20] 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.
[19] Paninski, L. (2005). Inferring prior probabilities from
Bayes-optimal behavior.
Advances in Neural
Information Processing 18.
[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.
[17] Paninski, L. (2005). Asymptotic theory of
information-theoretic experimental design.
Neural
Computation 17: 1480-1507.
[16] Paninski, L. (2004). Log-concavity results on
Gaussian process methods for supervised and unsupervised
learning.
Advances in Neural Information
Processing 17.
[15] Paninski, L. (2004). Variational minimax estimation
of discrete distributions under Kullback-Leibler
loss.
Advances in Neural Information
Processing 17.
[14] Paninski, L. (2004). Maximum likelihood estimation of
cascade point-process neural encoding
models.
Network: Computation in
Neural Systems 15: 243-262.
[13] Paninski, L., Pillow, J. & Simoncelli, E. (2004).
Comparing integrate-and-fire-like models estimated
using intra- and extra-cellular data.
Neurocomputing 65: 379-385.
[12] Paninski, L., Pillow, J. & Simoncelli, E. (2004).
Maximum likelihood estimation of a stochastic
integrate-and-fire neural encoding model.
Neural Computation 16: 2533-2561.
[11] Paninski, L. et al. (2004). Superlinear population
encoding of dynamic hand trajectory in primary motor
cortex.
Journal of Neuroscience 24:
8551-8561.
[10] Paninski, L. (2004). Estimating entropy on m bins
given fewer than m samples.
IEEE
Transactions on Information Theory 50: 2200-2203.
[9] 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.
[8] Hatsopoulos, N., Paninski, L. & Donoghue, J. (2003).
Sequential movement representations based on
correlated neuronal activity.
Experimental Brain Research 149:
478-486.
[7] Serruya, M., Hatsopoulos, N., Paninski, L., Fellows, M. & Donoghue, J.
(2003). Robustness of neuroprosthetic decoding
algorithms.
Biological Cybernetics 88: 219-228.
[6] Paninski, L. (2003). Estimation of entropy and mutual
information.
Neural Comp. 15:
1191-1253.
[5] 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.)
[4] Paninski, L., Lau, B. & Reyes, A. (2003). Noise-driven
adaptation: in vitro and mathematical
analysis.
Neurocomputing 52: 877-883.
[3] Serruya, M., Hatsopoulos, N., Paninski, L., Fellows, M. & Donoghue, J. (2002).
Instant neural control of a movement
signal.
Nature 416: 141-142.
[2] Paninski, L. & Hawken, M. (2001). Stochastic optimal control
and the human oculomotor system.
Neurocomputing,
38-40: 1511-1517.
[1] 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
Paninski, L., Eden, U., Brown, E. & Kass, R. Statistical
analysis of neurophysiological data.
Under contract, Springer.
Gerstner, W., Kistler, W., Naud, R. & Paninski, L. (2013). Spiking neuron
models (2nd ed.). Cambridge U. Press.
Invited book chapters
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,
M..
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-
McKnight Scholar award, 2008-.
Collaborative Research in Computational Neuroscience, NSF
IIS-0904353, co-PI w/ R. Yuste, 2009-.
DARPA award, Reliable Neural-interface Technology program,
co-PI w/ B. Pesaran, 2011-.
MURI award, ``Imaging how a neuron computes,'' co-PI w/ R.
Yuste et al., 2012-.
Other awards and honors
``Scientist to watch,'' The
Scientist magazine,
June 2007.
Named one of top 35 innovators under 35 years old by
Technology Review
MIT, 2006.
Honorable mention, outstanding student paper award (to J.
Lewi), NIPS, 2006.
Royal Society International Research Fellowship, 2004.
Best student paper award (w/ J. Pillow), NIPS, 2003.
Howard Hughes Medical Institute Predoctoral Fellowship in Biological
Sciences, 1999.
National Science Foundation Predoctoral Fellowship, 1999.
Royce Fellowship, Brown University, 1998.
Selected teaching
Co-instructor, Statistical analysis and
modeling of neural
systems
(NYU),
2002.
Invited lecturer, Advanced European computational neuroscience course
(Obidos), 2004.
STAT4107, Statistical inference, Columbia University,
2005.
STAT4315, Linear regression
models, Columbia University,
2006.
STAT4109, Probability and statistical inference, Columbia University,
2006-2010.
STAT8285, Statistical analysis and modeling of neural spike
train data, Columbia, 2007,9,11,12.
STAT6104, Computational statistics, Columbia, 2012-13.
Invited lecturer, Program in Comput. Bio., Gulbenkian
Science Institute, Lisbon, 2007.
Invited lecturer, Computational Modeling of Neuronal Systems, NYU,
2007.
Invited lecturer, Ignorance, Columbia University Biology Dept., 2008.
Invited lecturer, Princeton PICASso program, 2008.
Invited lecturer, Okinawa Computational Neuroscience Course,
2009.
Invited lecturer, Kyoto University workshop
on state-space analysis in
neuroscience,
2010.
Advising
Postdoctoral research advisor: J. Kulkarni, Q. Huys, Y.
Ahmadian, Y. Mishchenko, L. Badel, E. Pnevmatikakis, K. Sadeghi,
A. Pakman
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
M.A. research advisor: M. Yajima, C. Gohil, J. Bahk, S. Keshri
Undergraduate research advisor: G. Fudenberg, J. Huggins
Other duties
Action editor: J. Comput. Neuro.
Program committe: COSYNE.
Reviewer: Bayesian Analysis; Biometrika; COLT08; CRC Press; Frontiers in Comput. Neuro.; IEEE Transactions on: Biomedical Engineering, Information Theory, Pattern Analysis and Machine Intelligence, Neural Networks, and Signal Processing; ISIT08; J. Comput. Neuro.; J. Machine Learning Research; J. Neurophysiology; J. Neuroscience; J. Optical Soc. Am. A; J. Physics A; J. Vision; Machine Learning, Nature; Nature Neurosci.; Network: Computation in Neural Systems; Neural Computation; Neuron; NIPS; Oxford University Press; PNAS; PLOS Comp. Bio.; Science; SIAM J. Appl. Math; Statistics in Medicine; Technometrics.
NSF review panelist: 2007-.
Co-organizer: Statistical analysis of neural data meeting, 2010-.
Co-organizer: COSYNE workshop on new techniques for online neural characterization and optimal control, 2011.