@inproceedings{fc166423b149424fbfcd5ae7d23ec861,
title = "Optimal encoding and decoding for point process observations: An approximate closed-form filter",
abstract = "The process of dynamic state estimation (filtering) based on point process observations is in general intractable. Numerical sampling techniques are often practically useful, but lead to limited conceptual insight about optimal encoding/decoding strategies, which are of significant relevance to Computational Neuroscience. We develop an analytically tractable Bayesian approximation to optimal filtering based on point process observations, which allows us to introduce distributional assumptions about sensor properties, that greatly facilitate the analysis of optimal encoding in situations deviating from common assumptions of uniform coding. Numerical comparison with particle filtering demonstrate the quality of the approximation. The analytic framework leads to insights which are difficult to obtain from numerical algorithms, and is consistent with biological observations about the distribution of sensory cells' tuning curve centers.",
keywords = "assumed density filtering, filtering, neuroscience, nonlinear filters, point process, state estimation",
author = "Yuval Harel and Ron Meir and Manfred Opper",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE International Conference on the Science of Electrical Engineering, ICSEE 2016 ; Conference date: 16-11-2016 Through 18-11-2016",
year = "2017",
month = jan,
day = "4",
doi = "https://doi.org/10.1109/ICSEE.2016.7806119",
language = "الإنجليزيّة",
series = "2016 IEEE International Conference on the Science of Electrical Engineering, ICSEE 2016",
booktitle = "2016 IEEE International Conference on the Science of Electrical Engineering, ICSEE 2016",
}