Uri Eden

Eden’s research focuses on developing mathematical and statistical methods to analyze neural spiking activity. He has worked to integrate methodologies related to model identification, statistical inference, signal processing, and stochastic estimation and control, and expand these methodologies to incorporate point process observation models, making them more appropriate for modeling the dynamics of neural systems observed through spike train data. This research can be divided into two categories; first, a methodological component, focused on developing a statistical framework for relating neural activity to biological and behavioral signals and developing estimation algorithms, goodness-of-fit analyses, and mathematical theory that can be applied to any neural spiking system; second, an application component, wherein these methods are applied to spiking observations in real neural systems to dynamically model the spiking properties of individual neurons, to characterize how ensembles maintain representations of associated biological and behavioral signals, and to reconstruct these signals in real time.