Processing visual stimuli using hierarchical spiking neural networks

Authors:
Q. X. Wu;T. M. McGinnity;L. P. Maguire;A. Belatreche;B. Glackin
Affiliations:
School of Computing and Intelligent Systems, University of Ulster at Magee, Derry BT48 7JL, Northern Ireland, UK;School of Computing and Intelligent Systems, University of Ulster at Magee, Derry BT48 7JL, Northern Ireland, UK;School of Computing and Intelligent Systems, University of Ulster at Magee, Derry BT48 7JL, Northern Ireland, UK;School of Computing and Intelligent Systems, University of Ulster at Magee, Derry BT48 7JL, Northern Ireland, UK;School of Computing and Intelligent Systems, University of Ulster at Magee, Derry BT48 7JL, Northern Ireland, UK
Venue:
Neurocomputing
Year:
2008

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Abstract

Based on spiking neuron models and different receptive field models, hierarchical networks are proposed to process visual stimuli, in which multiple overlapped objects are represented by different orientation bars. The main purpose of this paper is to show that hierarchical spiking neural networks are able to segment the objects and bind their pixels to form shapes of objects using local excitatory lateral connections. The presented architecture is based on biologically inspired hierarchical structures. Segmentation is achieved through temporal correlation of neuron activities. The properties of these networks are demonstrated using a series of visual scenes representing different stimuli settings.