Unsupervised adaptive optimization of motion-sensitive systems guided by measurement uncertainty

Author

Jurica, Peter ; Gepshtein, Sergei ; Tyukin, Ivan ; Prokhorov, Danil ; Van Leeuwen, Cees

Author_Institution

RIKEN Brain Sci. Inst., Saitama

fYear

2007

fDate

3-6 Dec. 2007

Firstpage

179

Lastpage

184

Abstract

We propose a design for adaptive optimization of sensory systems. We consider a network of sensors that measure stimulus parameters as well as the uncertainties associated with these measurements. No prior assumptions about the stimulation and measurement uncertainties are built into the system, and properties of stimulation are allowed to vary with time. We present two approaches: one is based on estimation of the local gradient of uncertainty, and the other on random adjustment of cell tuning. Either approach steers the network towards its optimal state.

Keywords

adaptive estimation; measurement uncertainty; optimisation; parameter estimation; sensors; cell tuning; local gradient of uncertainty estimation; measurement uncertainty; motion-sensitive systems; sensor network; sensory systems; unsupervised adaptive optimization; Biomedical optical imaging; Biosensors; Mathematics; Measurement uncertainty; Motion measurement; Optical network units; Optical sensors; Statistics; Time varying systems; Tuning;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Sensors, Sensor Networks and Information, 2007. ISSNIP 2007. 3rd International Conference on

Conference_Location

Melbourne, Qld.

Print_ISBN

978-1-4244-1501-4

Electronic_ISBN

978-1-4244-1502-1

Type

conf

DOI

10.1109/ISSNIP.2007.4496840

Filename

4496840