Current position
Associate Professor, Department of Statistics, Center for Theoretical Neuroscience, and Doctoral Program in Neurobiology and Behavior, Columbia University (2008-present).
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: May-Dec 2003.
Selected papers
[50] Lalor, E., Ahmadian, Y. & Paninski, L. (2008). Decoding stimulus
velocity using a probabilistic model of ganglion cell populations in
primate retina. In preparation.
[49] Butts, D. et al. (2008). Direct measurement of suppression in the
LGN in the context of natural stimuli and its implications for visual
coding. In preparation.
[48] Nikitchenko, M. & Paninski, L. (2008). An expectation-maximization
Fokker-Planck algorithm for the noisy integrate-and-fire model. In
preparation.
[47] Babadi, B., Casti, A., Xiao, Y. & Paninski, L. (2008). Visual response
in LGN neurons beyond the monosynaptic retinogeniculate
transmission. In preparation.
[46] Rahnama Rad, K. & Paninski, L. (2008). Efficient estimation of
two-dimensional firing rate surfaces via Gaussian process
methods. Under review, Network.
[45] Lewi, J., Butera, R. & Paninski, L. (2008). Designing neurophysiology
experiments to optimally constrain receptive field models along
parametric submanifolds. Under review, NIPS.
[44] Escola, S., Eisele, M. & Paninski, L. (2008). Maximally reliable Markov
chains under energy constraints. Under review.
[43] Vogelstein, J., Babadi, B. & Paninski, L. (2008). Fast inference of
spike times from noisy calcium traces via tridiagonal nonnegative
deconvolution methods. Under review, NIPS.
[42] Wu, W., Kulkarni, J., Hatsopoulos, N. & Paninski, L. (2008). Neural
decoding of goal-directed movements using a linear state-space model
with hidden states. Under review, IEEE
Trans. Biomed. Engineering.
[41] Toyoizumi, T., Rahnama Rad, K. & Paninski, L. (2008). Mean-field
approximations for coupled populations of generalized linear model
spiking neurons. Under review, Neural Computation.
[40] Ahmadian, Y., Pillow, J. & Paninski, L. (2008). Efficient Markov
Chain Monte Carlo methods for decoding population spike trains.
Under review, Neural Computation.
[39] Paninski, L. (2008). Inferring synaptic inputs given a noisy voltage
trace. Under review, Journal of Computational Neuroscience.
[38] Huys, Q. & Paninski, L. (2008). Model-based optimal interpolation and
filtering for noisy, intermittent biophysical recordings. Under
review, PLOS Computational Biology.
[37] Vogelstein, J. & Paninski, L. (2008). Optimal detection of spike trains
given noisy, intermittent calcium signals. Under review, Biophysical
Journal.
[36] Fudenberg, G. Paninski, L. (2008). Bayesian image recovery for low-SNR
dendritic structures. Under review, IEEE Trans. Image Processing.
[35] Escola, S. & Paninski, L. (2008). Hidden Markov models for the inference
of neural states and improved estimation of linear receptive fields.
Under review, Neural Computation.
[34] Pillow, J. & Paninski, L. (2008). Model-based decoding, information
estimation, and change-point detection in multi-neuron spike
trains. Under review, Neural Computation.
[33] Paninski, L. (2008). A coincidence-based test for
uniformity given very sparsely-sampled discrete
data.
Under review, IEEE Transactions
on Information Theory.
[32] 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.
In press, Nature.
[31] Paninski, L. & Yajima, M. (2008). Undersmoothed
kernel entropy estimators.
In press,
IEEE Transactions on Information Theory.
[30] Lewi, J., Butera, R. & Paninski, L. (2008).
Sequential optimal design of neurophysiology
experiments.
In press, Neural
Computation.
[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).
Application of cascade analysis to simultaneously
recorded motor cortical, cerebellar and EMG
data.
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
light responses analyzed with a noisy integrate-and-fire
model.
Journal of Neuroscience 25:
11003-11013.
[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 Computation 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.
Book
Paninski, L., Eden, U., Brown, E. & Kass, R. (2008). Statistical
analysis of neurophysiological data. In preparation.
Invited book chapters
Paninski, L., Kass, R., Brown, E. & Iyengar, I. (2008).
Statistical analysis of neuronal data via
integrate-and-fire models.
To
appear in: Stochastic Methods in Neuroscience, eds.
Laing, C. & Lord, G., Oxford University Press.
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.; pp. 493-507.
Simoncelli, E., Paninski, L., Pillow, J. & Schwartz, O. (2004).
Characterization of neural responses with
stochastic stimuli.
Chapter 23 of The New
Cognitive Neurosciences: Third Edition, ed. Gazzaniga,
M.; pp. 327-338.
Selected recent presentations
Computational and systems neuroscience 2008 meeting,
Salt Lake City:
Babadi, B., Casti, A., Xiao, Y. & Paninski, L. Visual response in LGN neurons
beyond the monosynaptic retinogeniculate transmission.
Doi, E., Paninski, L. & Simoncelli, E. Maximizing sensory information with
an arbitrary size of neural populations
Escola, S., Eisele, M. & Paninski, L. Maximally reliable Markov chains under
energy constraints
Ferreira, D. & Paninski, L. State-space methods for inferring synaptic inputs
and weights
Rahnama Rad, K. & Paninski, L. Efficient estimation of two-dimensional firing
rate surfaces via Gaussian process methods
Lewi, J., Butera, R. & Paninski, L. Designing neurophysiology experiments to
optimally constrain parametric receptive field models
Pillow, J. et al. The effects of correlated neural activity on
spiking variability in the primate retina
Koyama, S., Kass, R. & Paninski, L. Efficient computation of the most
likely path in integrate-and-fire and more general state-space models
Toyoizumi, T., Rahnama Rad, K. & Paninski, L. Mean-field
approximations for coupled populations of generalized linear model
spiking neurons with Markov refractoriness
Vogelstein, J. & Paninski, L. Model-based optimal inference of spike
times and calcium dynamics given noisy and intermittent
calcium-fluorescence imaging
Wu, W., Kulkarni, J., Hatsopoulos, N. & Paninski, L. Neural decoding
of goal-directed movements using a linear state-space model with
hidden states
Ahmadian, Y., Pillow, J. & Paninski, L. Markov Chain Monte Carlo
methods for decoding neural spike trains
Grants
NSF Faculty Early Career Development (CAREER) IOS-0641912, 2007-
Collaborative Research in Computational Neuroscience, NEI R01
EY018003, co-PI w/ E. Simoncelli and E.J. Chichilnisky, 2006-.
Gatsby Initiative in Brain Circuitry Pilot Grant, co-PI w/ S.
Woolley, 2006-.
Awards and Honors
McKnight Scholar award, 2008.
Alfred P. Sloan Research Fellowship, 2007.
``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 recent invited talks
Neuroscience, Biomed. Eng., and Statistics seminars (Johns
Hopkins 2005)
Statistics and Computational Neuroscience seminars (University
of Chicago 2006)
Stochastic dynamics of neurons and networks workshop, CNS
(Edinburgh 2006)
Statistics seminar (Yale 2006)
Brain and Cognitive Sciences seminar (MIT 2006)
Computational Neuroscience Forum (NYU 2006)
Statistics seminar (Carnegie Mellon 2007)
Emerging information-theoretic methods workshop, COSYNE (Salt Lake City 2007)
Applied Math and Computational Science Colloquium (U.
Penn 2007)
Joint Statistical Meetings panel on neural data analysis (Salt
Lake City 2007)
Neyman Seminar (Statistics dept.) and Redwood Center Seminar
(UC Berkeley 2007)
Statistical analysis of neural data conference (CMU 2008)
Principles of biological computation workshop (Santa Fe
Institute 2008)
SIAM conference on the life sciences (Montreal 2008)
IPM School of Cognitive Sciences international seminar
(Tehran 2008)
Selected Teaching
Co-instructor, Statistical analysis and
modeling of neural
systems
(NYU),
2002.
Invited lecturer, Advanced European computational neuroscience course
(Obidos), 2004.
Invited lecturer, Neural coding
(Gatsby theoretical neuroscience graduate
course), 2004.
STAT4107, Statistical inference, Columbia University,
2005.
STAT4315, Linear regression
models, Columbia University,
2006.
STAT4109, Probability and statistical inference, Columbia University,
2006, 2007.
STAT8285, Statistical analysis and modeling of neural spike
train data, Columbia, 2007.
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
Advising
Primary postdoctoral research advisor: J. Kulkarni, Q. Huys, Y.
Ahmadian
Ph.D. research advisor: S. Escola, J. Vogelstein, D. Ferreira, J. Lewi,
K. Rahnama, M. Nikitchenko
Undergraduate research advisor: G. Fudenberg
Other duties
Reviewer: Bayesian Analysis; COLT08; CRC Press; IEEE Transactions on: Biomedical Engineering, Information Theory, Pattern Analysis and Machine Intelligence, Neural Networks, and Signal Processing; ISIT08; J. Computat. Neuro.; J. Machine Learning Research; J. Neurophysiology; J. Neuroscience; J. Optical Soc. Am. A; J. Physics A; J. Vision; Machine Learning, Nature; Nature Neuroscience; Network: Computation in Neural Systems; Neural Computation; Neuron; NIPS02-7; Oxford University Press; PNAS; Science; SIAM J. Applied Math; Statistics in Medicine; Technometrics.
Invited participant: NSF Workshop, ``Brain Science at the Interface,''
2007.
NSF review panelist: 2007, 2008.