Learning internal representation of visual context in a neural coding network

Authors:
Jun Miao;Baixian Zou;Laiyun Qing;Lijuan Duan;Yu Fu
Affiliations:
Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China;Department of Information Science and Technology, College of Arts and Science of Beijing, Union University, Beijing, China;School of Information Science and Engineering, Graduate University of the Chinese Academy of Sciences, Beijing, China;College of Computer Science and Technology, Beijing University of Technology, Beijing, China;Department of Computing, University of Surrey, Guildford, Surrey, UK
Venue:
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part I
Year:
2010

Citing 2
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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.