Abstract
Visual context plays a significant role in humans' gaze movement for target searching. How to transform the visual context into the internal representation of a brain-like neural network is an interesting issue. Population cell coding is a neural representation mechanism which was widely discovered in primates' visual neural system. This paper presents a biologically inspired neural network model which uses a population cell coding mechanism for visual context representation and target searching. Experimental results show that the population-cell-coding generally performs better than the single-cell-coding system. © 2010 Springer-Verlag Berlin Heidelberg.
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CITATION STYLE
Miao, J., Zou, B., Qing, L., Duan, L., & Fu, Y. (2010). Learning internal representation of visual context in a neural coding network. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6352 LNCS, pp. 174–183). https://doi.org/10.1007/978-3-642-15819-3_22
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