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Simulation of Visual Attention using Hierarchical Spiking Neural Networks

Wu, Qingxiang, McGinnity, TM, Maguire, LP, Cai, Rongtai and Chen, Meigui (2011) Simulation of Visual Attention using Hierarchical Spiking Neural Networks. In: International Conference on Interlligent Computing (ICIC 2011). Springer-Verlag. Vol 6840 6 pp. [Conference contribution]

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URL: http://www.springer.com/computer/ai/book/978-3-642-24552-7

Abstract

Based on the information processing functionalities of spiking neurons, a hierarchical spiking neural network model is proposed to simulate visual attention. The network is constructed with a conductance-based integrate-and-fire neuron model and a set of specific receptive fields in different levels. The simulation algorithm and properties of the network are detailed in this paper. Simulation results show that the network is able to perform visual attention to extract objects based on specific image features. Using extraction of horizontal and vertical lines, a demonstration shows how the network can detect a house in a visual image. Using this visual attention principle, many other objects can be extracted by analogy.

Item Type:Conference contribution (Lecture)
Keywords:Visual attention, spiking neural network, receptive field, visual system.
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
Research Institutes and Groups:Computer Science Research Institute > Intelligent Systems Research Centre
Computer Science Research Institute
ID Code:20374
Deposited By: Dr Qingxiang Wu
Deposited On:28 Oct 2011 09:24
Last Modified:09 Dec 2015 11:00

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