Liam

B

Paninski

Liam Paninski is pictured here.
Professor
1028 SSW 1255 Amsterdam Avenue
New York
NY
10027
Office Phone: 
(212) 851-2166
Fax: 
(212) 851-2164
Short Research Description: 

The statistical analysis of neural and imaging data

Full Research Description: 

The neural coding problem is perhaps the fundamental question in systems neuroscience: given some input stimulus (or movement, or thought, etc.), what is the conditional probability of a neural response? The roadblock is that we want to know about these response probabilities given any possible input, and there are typically more such inputs than we can ever hope to sample. Thus the neural coding problem is fundamentally a statistics problem: given a finite number of samples of physiological data, how do we learn the neural codebook? 
Below are more detailed descriptions of some (overlapping) themes in our recent neural coding research. These neural problems, in turn, have led to a number of problems which are interesting from a purely statistical point of view, with connections to machine learning, latent variable methods, high-dimensional regression, fast state-space smoothing methods, etc. 

Representative Publications: 
  • Ramirez, A., Pnevmatikakis, E., Merel, J., Miller, K., Paninski, L. & Bruno, R. The spatiotemporal receptive fields of barrel cortex neurons revealed by reverse correlation of synaptic input. In press, Nat. Neurosci. 
  • Pakman, A., Shababo, B. & Paninski, L. (2014). Bayesian sparse unsupervised learning for probit models of binary data. Submitted. 
  • Mena, G. & Paninski, L. (2014). On quadrature methods for refractory point process likelihoods. Submitted. 
  • Pnevmatikakis, E., Merel, J., Pakman, A. & Paninski, L. (2014). Bayesian spike inference from calcium imaging data. Asilomar Conf. on Signals, Systems, and Computers. 
  • Keshri, S., Pnevmatikakis, E., Pakman, A., Shababo, B. & Paninski, L. (2013). A shotgun sampling solution for the common input problem in neural connectivity inference. Arxiv 1309.3724. 
  • Shababo, B., Paige, B., Pakman, A. & Paninski, L. (2013). Bayesian inference and online experimental design for mapping neural microcircuits. To appear, NIPS. 
  • Pnevmatikakis, E. and Paninski, L. (2013). Sparse nonnegative deconvolution for compressive calcium imaging: algorithms and phase transitions. To appear, NIPS. 
  • Pfau, D., Pnevmatikakis, E. & Paninski, L. (2013). Robust learning of low-dimensional dynamics from large neural ensembles. To appear, NIPS. 
  • Pakman, A. and Paninski, L. (2013). Auxiliary-variable exact Hamiltonian Monte Carlo samplers for binary distributions. To appear, NIPS. 
  • Merel, J., Fox, R., Jebara, T. & Paninski, L. (2013). A multi-agent control framework for co-adaptation in brain-computer interfaces. To appear, NIPS. 
  • Pakman, A., Huggins, J., Smith, C. & Paninski, L. (2013). Fast penalized state-space methods for inferring dendritic synaptic connectivity. In press, J. Comput. Neurosci. 
  • Ramirez, A. & Paninski, L. (2013). Fast generalized linear model estimation via expected log-likelihoods. In press, J. Comput. Neurosci. 
  • Smith, C. & Paninski, L. (2013). Computing loss of efficiency in optimal Bayesian decoders given noisy or incomplete spike trains. In press, Network: Computation in Neural Systems. 
  • Pakman, A. & Paninski, L. (2013). Efficient multivariate truncated normal sampling via exact Hamiltonian Monte Carlo. In press, J. Comput. Graph. Stat. 
  • Pnevmatikakis, E., Rahnama Rad, K., Huggins, J., & Paninski, L. (2013). Fast Kalman filtering and forward-backward smoothing via a low-rank perturbative approach. In press, J. Comput. Graph. Stat. 
  • 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. 

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