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Sparse visual models for biologically inspired sensorimotor control

Yang, Li and Jabri, Marwan (2003) Sparse visual models for biologically inspired sensorimotor control. [Conference Paper]

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Abstract

Given the importance of using resources efficiently in the competition for survival, it is reasonable to think that natural evolution has discovered efficient cortical coding strategies for representing natural visual information. Sparse representations have intrinsic advantages in terms of fault-tolerance and low-power consumption potential, and can therefore be attractive for robot sensorimotor control with powerful dispositions for decision-making. Inspired by the mammalian brain and its visual ventral pathway, we present in this paper a hierarchical sparse coding network architecture that extracts visual features for use in sensorimotor control. Testing with natural images demonstrates that this sparse coding facilitates processing and learning in subsequent layers. Previous studies have shown how the responses of complex cells could be sparsely represented by a higher-order neural layer. Here we extend sparse coding in each network layer, showing that detailed modeling of earlier stages in the visual pathway enhances the characteristics of the receptive fields developed in subsequent stages. The yield network is more dynamic with richer and more biologically plausible input and output representation.

Item Type:Conference Paper
Keywords:hierarchical sparse coding network, visual ventral pathway, sensorimotor control
Subjects:Computer Science > Machine Vision
Computer Science > Neural Nets
Computer Science > Robotics
ID Code:3340
Deposited By:Prince, Dr Christopher G.
Deposited On:12 Feb 2004
Last Modified:11 Mar 2011 08:55

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