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    block this user Giacomo Indiveri Trusted member

    Associate Professor

    University of Zurich and ETH Zurich

    Orientation-selective aVLSI spiking neurons.

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    We describe a programmable multi-chip VLSI neuronal system that can be used for exploring spike-based information processing models. The system consists of a silicon retina, a PIC microcontroller, and a transceiver chip whose integrate-and-fire neurons are connected in a soft winner-take-all architecture. The circuit on this multi-neuron chip approximates a cortical microcircuit. The neurons can be configured for different computational properties by the virtual connections of a selected set of pixels on the silicon retina. The virtual wiring between the different chips is effected by an event-driven communication protocol that uses asynchronous digital pulses, similar to spikes in a neuronal system. We used the multi-chip spike-based system to synthesize orientation-tuned neurons using both a feedforward model and a feedback model. The performance of our analog hardware spiking model matched the experimental observations and digital simulations of continuous-valued neurons. The multi-chip VLSI system has advantages over computer neuronal models in that it is real-time, and the computational time does not scale with the size of the neuronal network.

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    Description

    Title : Orientation-selective aVLSI spiking neurons.
    Author(s) : S C Liu, J Kramer, G Indiveri, T Delbrück, T Burg, R Douglas
    Abstract : We describe a programmable multi-chip VLSI neuronal system that can be used for exploring spike-based information processing models. The system consists of a silicon retina, a PIC microcontroller, and a transceiver chip whose integrate-and-fire neurons are connected in a soft winner-take-all architecture. The circuit on this multi-neuron chip approximates a cortical microcircuit. The neurons can be configured for different computational properties by the virtual connections of a selected set of pixels on the silicon retina. The virtual wiring between the different chips is effected by an event-driven communication protocol that uses asynchronous digital pulses, similar to spikes in a neuronal system. We used the multi-chip spike-based system to synthesize orientation-tuned neurons using both a feedforward model and a feedback model. The performance of our analog hardware spiking model matched the experimental observations and digital simulations of continuous-valued neurons. The multi-chip VLSI system has advantages over computer neuronal models in that it is real-time, and the computational time does not scale with the size of the neuronal network.
    Keywords : action potentials, action potentials physiology, animals, feedback, feedback physiology, humans, microcomputers, neural networks (computer), neurons, neurons physiology, pattern recognition, visual, visual physiology, retina, retina physiology, visual cor

    Subject : unspecified
    Area : Other
    Language : English
    Year : 2001

    Affiliations University of Zurich and ETH Zurich
    Journal : Neural Networks
    Volume : 14
    Issue : 6-7
    Publisher : Elsevier
    Pages : 629-643
    Url : http://www.ncbi.nlm.nih.gov/pubmed/11665759

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