Simulation of visual attention using hierarchical spiking neural networks

Authors:
QingXiang Wu;T. Martin McGinnity;Liam Maguire;Rongtai Cai;Meigui Chen
Affiliations:
Intelligent Systems Research Center, School of Computing and Intelligent Systems, University of Ulster at Magee, Londonderry, Northern Ireland, UK;Intelligent Systems Research Center, School of Computing and Intelligent Systems, University of Ulster at Magee, Londonderry, Northern Ireland, UK;Intelligent Systems Research Center, School of Computing and Intelligent Systems, University of Ulster at Magee, Londonderry, Northern Ireland, UK;School of Physics and OptoElectronics Technology, Fujian Normal University, Fuzhou, China;School of Physics and OptoElectronics Technology, Fujian Normal University, Fuzhou, China
Venue:
ICIC'11 Proceedings of the 7th international conference on Intelligent Computing: bio-inspired computing and applications
Year:
2011

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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.