author = {Lucian Alecu and Hervé Frezza-Buet},
title = {Are Neural Fields Suitable for Vector Quantization\'e},
year = {2008},
booktitle = {Proceedings of The Seventh International Conference on Machine Learning and Applications (ICMLA'08)},
publisher = {IEEE},
pages = {239-244},
month = {December},
address = {San Diego (CA, USA)},
url = {http://www.metz.supelec.fr/~alecu_luc/papers/icmla08.pdf},
doi = {10.1109/ICMLA.2008.21},
abstract = {This paper focuses on the possibility of enabling vector quantization learning techniques into dynamic neural fields, as an attempt to enrich their usage in bio-inspired applications. As mathematical approaches prove rather difficult to propose a practical solution, due to the non-linear character of the field equations, we adopt a different perspective in order to deal with this problem. This consists in simulating the evolution of the field and design an empirical method able to measure its quality. The developed benchmark framework implementing this approach is used to check whether a given field is capable to behave as expected in various situations, in particular those involving self-organization by vector quantization.}