@InProceedings{Supelec678,
author = {Jérémy Fix and Matthieu Geist and Olivier Pietquin and Hervé Frezza-Buet},
title = {{Dynamic Neural Field Optimization using the Unscented Kalman Filter}},
year = {2011},
booktitle = {{Proceedings of the IEEE Symposium on Computational Intelligence, Cognitive Algorithms, Mind, and Brain (CCMB 2011)}},
publisher = {IEEE},
pages = {1 - 7},
month = {April},
address = {Paris (France)},
url = {http://www.metz.supelec.fr//metz/personnel/pietquin/pdf/CCMB_2011_JFMGOPHFB.pdf},
isbn = {978-1-4244-9890-1},
doi = {10.1109/CCMB.2011.5952113},
abstract = {Dynamic neural fields have been proposed as a continuous model
of a neural tissue. When dynamic neural fields are used in
practical applications, the tuning of their parameters is a
challenging issue that most of the time relies on expert
knowledge on the influence of each parameter. The methods that
have been proposed so far for automatically tuning these
parameters rely either on genetic algorithms or on gradient
descent. The second category of methods requires to explicitly
compute the gradient of a cost function which is not always
possible or at least difficult and costly. Here we propose to
use unscented Kalman filters, a derivative-free algorithm for
parameter estimation, which reveals to efficiently optimize the
parameters of a dynamic neural field.}
}