Uri Eden
Keynote speakers
- Kenji Doya
- Alon Halevy
- Astrid Prinz
- Andrew Schwartz
- Shankar Subramaniam
- Arthur Toga
Workshop speakers
- Bart ter Haar Romeny
- Uri Eden
- Klaus Linkenkaer-Hansen
- Tim Clark
- Alan Ruttenberg
- Jeffrey Grethe
- Arnd Roth
- Wulfram Gerstner
- Peter Hunter
- Markus Diesmann
- Andrey Semin
- Pietro Liò
- Albert Cardona
- Giorgio Ascoli
- Rolf Kötter
Workshop 1, Advances in the automatic analysis of multi-dimensional data
Uri Eden
Title: Analyzing ensemble spiking activity using point process filters
Boston University, Boston, USA
Abstract: Brain areas are able to maintain dynamic representations of biological stimuli and behavioral variables through coordinated spiking activity of large neural ensembles. Technological advances in electrophysiology now allow us to record simultaneous activity from increasingly large populations of spiking neurons. Developing modeling methods to describe the neural representations present within these high dimensional signals presents an important statistical challenge for neural data analysis. The theory of point processes offers a unified, principled approach to modeling and estimating the firing properties of spiking neural systems, and assessing goodness-of-fit between a neural model and observed spike train data.
We develop a state space estimation framework to track the evolution of dynamic signals using spike train observations from large neural ensembles. This allows us to derive a toolbox of estimation algorithms and adaptive filters to address questions of static and dynamic encoding and decoding. In our analysis of these filtering algorithms, we draw analogies to well-studied linear estimation algorithms for continuous valued processes, such as the Kalman filter and its discrete and continuous time extensions.
These methods will be illustrated in the context of the analysis of place field activity in the rodent hippocampus. Place cells, which tend to fire preferentially when the animal is in specific locations, have been implicated in cognitive tasks such as navigation and decision making. Using simple point process models, we are able to accurately characterize the localized spiking activity of these neurons as a function of the animal's position in its environment, track plasticity in their firing properties, and reconstruct the animal's movements from the spiking of a hippocampal population.
Bio sketch: Uri Eden is an Assistant Professor in the Department of Mathematics and Statistics at Boston University. He received the B.S. degree in mathematics and in engineering and applied sciences from the California Institute of Technology, Pasadena, in 1999, and the S.M. degree in engineering sciences and the Ph.D. degree in engineering sciences from Harvard University, Cambridge, MA, in 2002 and 2005, respectively. He was a Postdoctoral Fellow in the Brain and Cognitive Sciences Department at the Massachusetts Institute of Technology, Cambridge, from 2005-2006. His research focuses on developing mathematical and statistical methods to analyze neural spiking activity, integrating methodologies related to model identification, statistical inference, signal processing, and stochastic estimation and control.