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    block this user Giacomo Indiveri Trusted member

    Associate Professor

    University of Zurich and ETH Zurich

    Modeling selective attention using a neuromorphic analog VLSI device.

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    Attentional mechanisms are required to overcome the problem of flooding a limited processing capacity system with information. They are present in biological sensory systems and can be a useful engineering tool for artificial visual systems. In this article we present a hardware model of a selective attention mechanism implemented on a very large-scale integration (VLSI) chip, using analog neuromorphic circuits. The chip exploits a spike-based representation to receive, process, and transmit signals. It can be used as a transceiver module for building multichip neuromorphic vision systems. We describe the circuits that carry out the main processing stages of the selective attention mechanism and provide experimental data for each circuit. We demonstrate the expected behavior of the model at the system level by stimulating the chip with both artificially generated control signals and signals obtained from a saliency map, computed from an image containing several salient features.

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    Description

    Title : Modeling selective attention using a neuromorphic analog VLSI device.
    Author(s) : G Indiveri
    Abstract : Attentional mechanisms are required to overcome the problem of flooding a limited processing capacity system with information. They are present in biological sensory systems and can be a useful engineering tool for artificial visual systems. In this article we present a hardware model of a selective attention mechanism implemented on a very large-scale integration (VLSI) chip, using analog neuromorphic circuits. The chip exploits a spike-based representation to receive, process, and transmit signals. It can be used as a transceiver module for building multichip neuromorphic vision systems. We describe the circuits that carry out the main processing stages of the selective attention mechanism and provide experimental data for each circuit. We demonstrate the expected behavior of the model at the system level by stimulating the chip with both artificially generated control signals and signals obtained from a saliency map, computed from an image containing several salient features.
    Subject : unspecified
    Area : Other
    Language : English
    Year : 2000

    Affiliations University of Zurich and ETH Zurich
    Journal : Neural Computation
    Volume : 12
    Issue : 12
    Publisher : MIT Press
    Pages : 2857-80
    Url : http://www.ncbi.nlm.nih.gov/pubmed/11112258

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