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Tutorial
Flexible, Multivariate Point Process Models for Unlocking the Neural Code
Jonathan W Pillow

Mon Dec 12 12:30 AM -- 02:30 AM (PST) @ Andulucia II & III

A central goal of computational neuroscience is to understand the neural code, the semantic relationship between neural spike trains and the extrinsic (sensory, motor, & cognitive) variables that they represent. One powerful approach to this problem involves "cascade" point process models, which describe the neural encoding process in terms of a cascade of three stages: (1) linear dimensionality-reduction of a high-dimensional stimulus space; (2) a nonlinear transformation from feature space to spike rate; and (3) an inhomogeneous, conditional renewal (e.g., Poisson) spiking process. These models have been shown to provide accurate descriptions of single- and multi-neuron spike responses in a wide variety of brain areas, and have shed light on the fundamental units (rates, spike times, correlations, oscillations) that neurons use to convey information. Recent innovations have focused on extending these models to incorporate richer nonlinear dependencies and dynamics, and to capture more biologically realistic features of neural spike trains.

In this tutorial, I will provide a general introduction to cascade neural encoding models and then discuss some more recent advances, including models for non-Poisson spike trains and correlated neural population responses. Topics will include: Poisson & renewal processes, reverse correlation, spike-triggered average / covariance (STA/STC) analysis, inverse regression, maximally informative dimensions (MID), generalized linear models (GLMs), Ising models, latent variable / shared-noise models, functional connectivity, advanced regularization methods, and model-based (Bayesian) techniques for decoding multi-neuron spike trains.

Author Information

Jonathan W Pillow (UT Austin)

Jonathan Pillow is an assistant professor in Psychology and Neurobiology at the University of Texas at Austin. He graduated from the University of Arizona in 1997 with a degree in mathematics and philosophy, and was a U.S. Fulbright fellow in Morocco in 1998. He received his Ph.D. in neuroscience from NYU in 2005, and was a Royal Society postdoctoral reserach fellow at the Gatsby Computational Neuroscience Unit, UCL from 2005 to 2008. His recent work involves statistical methods for understanding the neural code in single neurons and neural populations, and his lab conducts psychophysical experiments designed to test Bayesian models of human sensory perception.

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