Abstract
This paper proposes a neural network called “Hierarchical Overlapping Sensory Mapping (HOSM)”, motivated by the structure of receptive fields in biological vision. To extract the features from these receptive fields, a method called Candid covariance-free Incremental Principal Component Analysis (CCIPCA) is used to automatically develop a set of orthogonal filters. An application of HOSM on a robot with eyes shows that the HOSM algorithm can pay attention to different targets and get its cognition for different environments in real time.
This research is supported by the National Natural Science Foundation of China (NSF 60171036) and the Shanghai Science and Technology Committee (No. 045115020).
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Gao, Y., Lu, X., Zhang, L. (2005). A Neural Network Based on Biological Vision Learning and Its Application on Robot. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3498. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427469_31
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DOI: https://doi.org/10.1007/11427469_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25914-5
Online ISBN: 978-3-540-32069-2
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