Jonathan Pillow

Jonathan Pillow
Center for Perceptual Systems , University of Texas at Austin
Austin, USA

Speaker of Workshop 4

Will talk about: A model-based approach to functional connectivity

Bio sketch:

Jonathan Pillow is an assistant professor at the University of Texas at Austin in Psychology and Neurobiology, the Center for Perceptual Systems, and the Division of Statistics and Scientific Computation. He received his Ph.D. in neuroscience from NYU, working with Eero Simoncelli, and was a postdoctoral fellow at the Gatsby Computational Neuroscience Unit at University College London, working with Peter Latham.  Prior to this, Jonathan completed a B.A. in mathematics and philosophy at the University of Arizona and was a Fulbright fellow in Morocco for one year studying north African literature.  Jonathan's primary research interests lie at the intersection of computational neuroscience and statistics.  His recent work involves statistical methods for understanding the neural code in populations of spiking neurons and in intracellular recordings of single neurons.  His lab also conducts psychophysical experiments involving human visual perception and develops Bayesian models of sensory information processing.

Talk abstract:

One of the central problems in systems neuroscience is to understand how ensembles of neurons work together to process information. Functional connectivity within a population can affect both the amount of information conveyed and the manner by which downstream brain areas can decode it. In this talk, I will present a model-based approach to understanding functional connectivity in populations of spiking neurons.  We have recently shown that a multivariate point-process model, a type of generalized linear model (GLM), provides an accurate and highly tractable description of the spatio-temporal correlation structure of the responses from a complete population of retinal ganglion cells.  Bayesian decoding under this model provides a tool for assessing how correlations affect the information content of the neural code. I will discuss the implications of this framework for understanding the role of correlated activity in the encoding and decoding of sensory signals.

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