Liam Paninski is a Professor of Statistics, Co-Director of the Grossman Center for Statistics, a member of the Kavli Institute for Brain Science and a Co-PI of the NeuroTechnology Center (NTC) at Columbia University. He is an expert in data analysis and computational approaches to neuronal coding.
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.
- Likelihood-based neural data analysis: predicting and decoding spike trains
- Estimation of information-theoretic quantities given limited data
- Estimation and analysis of stochastic biophysical neural models
- Optimal information-theoretic design of experiments
- Population coding in primary motor cortex; neural prosthetic design
- Neural coding in the retina
- Pnevmatikakis E, Paninski L. (2013). Sparse nonnegative deconvolution for compressive calcium imaging: algorithms and phase transitions. NIPS. [PDF]
- Shababo B, Paige B, Pakman A, Paninski L. (2013). Bayesian inference and online experimental design for mapping neural microcircuits. NIPS. [PDF]
- Ramirez A, Pnevmatikakis EA, Merel J, Paninski L, Miller KD, Bruno RM. Spatiotemporal receptive fields of barrel cortex revealed by reverse correlation of synaptic input. Nature neuroscience, 2014;17(6):866875. [Abstract]
- Pakman A, Huggins J, Smith C, Paninski L. (2014). Fast penalized statespace methods for inferring dendritic synaptic connectivity. J. Comput. Neurosci. [PDF]