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Neuromorphic VLSI Models of Selective Attention: From Single Chip Vision Sensors to Multi-chip Systems
Institute of Neuroinformatics, UZH-ETH Zurich, Winterthurerstrasse 190, 8052 Zurich, Switzerland
Received: 30 June 2008; in revised form: 1 September 2008 / Accepted: 2 September 2008 / Published: 3 September 2008
Abstract: Biological organisms perform complex selective attention operations continuously and effortlessly. These operations allow them to quickly determine the motor actions to take in response to combinations of external stimuli and internal states, and to pay attention to subsets of sensory inputs suppressing non salient ones. Selective attention strategies are extremely effective in both natural and artificial systems which have to cope with large amounts of input data and have limited computational resources. One of the main computational primitives used to perform these selection operations is the Winner-Take-All (WTA) network. These types of networks are formed by arrays of coupled computational nodes that selectively amplify the strongest input signals, and suppress the weaker ones. Neuromorphic circuits are an optimal medium for constructing WTA networks and for implementing efficient hardware models of selective attention systems. In this paper we present an overview of selective attention systems based on neuromorphic WTA circuits ranging from single-chip vision sensors for selecting and tracking the position of salient features, to multi-chip systems implement saliency-map based models of selective attention.
Keywords: Selective attention; winner-take-all (WTA); neuromorphic; Address-Event Representation (AER); integrate and fire (I and F) neuron
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Indiveri, G. Neuromorphic VLSI Models of Selective Attention: From Single Chip Vision Sensors to Multi-chip Systems. Sensors 2008, 8, 5352-5375.
Indiveri G. Neuromorphic VLSI Models of Selective Attention: From Single Chip Vision Sensors to Multi-chip Systems. Sensors. 2008; 8(9):5352-5375.
Indiveri, Giacomo. 2008. "Neuromorphic VLSI Models of Selective Attention: From Single Chip Vision Sensors to Multi-chip Systems." Sensors 8, no. 9: 5352-5375.