Liam Paninski

paninski-300x300-newResearch Area IV: Computational Methods for Neuroscience Leader

Columbia University

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.


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    1. Pnevmatikakis E, Paninski L. (2013). Sparse nonnegative deconvolution for compressive calcium imaging: algorithms and phase transitions. NIPS. [PDF]
    2. Shababo B, Paige B, Pakman A, Paninski L. (2013). Bayesian inference and online experimental design for mapping neural microcircuits. NIPS. [PDF]
    3. 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]
    4. Pakman A, Huggins J, Smith C, Paninski L. (2014). Fast penalized state­space methods for inferring  dendritic synaptic connectivity. J. Comput. Neurosci. [PDF]