Unsupervised learning in second-order neural networks for motion analysis

Maul, T.; Baba, S. (2011) Unsupervised learning in second-order neural networks for motion analysis. Neurocomputing, 74 (6). pp. 884-895. ISSN 0925-2312

Full text not available from this repository.


This paper demonstrates how unsupervised learning based on Hebb-like mechanisms is sufficient for training second-order neural networks to perform different types of motion analysis. The paper studies the convergence properties of the network in several conditions, including different levels of noise and motion coherence and different network configurations. We demonstrate the effectiveness of a novel variability dependent learning mechanism, which allows the network to learn under conditions of large feature similarity thresholds, which is crucial for noise robustness. The paper demonstrates the particular relevance of second-order neural networks and therefore correlation based approaches as contributing mechanisms for directional selectivity in the retina.

Item Type: Article
  1. Maul, T.
  2. Baba, S.(University of Malaya)
Journal or Publication Title: Neurocomputing
Uncontrolled Keywords: Computer Science, Artificial Intelligence;
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Computer Science & Information Technology
Depositing User: Zanaria Saupi Udin
Date Deposited: 26 Aug 2011 16:06
Last Modified: 12 Dec 2014 16:00
URI: http://eprints.um.edu.my/id/eprint/2073

Actions (For repository staff only: Login required)

View Item