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  • 1
    Electronic Resource
    Electronic Resource
    Springer
    Neural processing letters 11 (2000), S. 197-208 
    ISSN: 1573-773X
    Keywords: spiking neurons ; competitive processing ; temporal inhibition ; attentional control mechanisms ; bio-inspired neural systems
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract The paper describes the implementation of competitive neural structures based on a spiking neural model that includes multiplicative or shunting synapses enabling non-saturated stable states in response to different stationary inputs as well as controllable transient responses. A VLSI-viable implementation of this model has been previously proposed and tested [1]. It has the possibility of modulating the output spike frequency by an additional input without affecting other neuron variables such as the membrane potential. This feature is exploited in the simulation of a Selective Temporal Inhibition network that is suitable for implementing attentional control systems.
    Type of Medium: Electronic Resource
    Library Location Call Number Volume/Issue/Year Availability
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  • 2
    Electronic Resource
    Electronic Resource
    Springer
    Analog integrated circuits and signal processing 15 (1998), S. 263-275 
    ISSN: 1573-1979
    Source: Springer Online Journal Archives 1860-2000
    Topics: Electrical Engineering, Measurement and Control Technology
    Notes: Abstract In this paper, three alternative VLSI analog implementations of CNNs are described, which have been devised to perform image processing and vision tasks: a programmable low-power CNN with embedded photo-sensors, a compact fixed-template CNN based on unipolar current-mode signals, and basic CMOS circuits to implement an extended CNN model using spikes. The first two VLSI approaches are intended for focal-plane image processing applications. The third one allows, since its dynamics is defined by process-independent local ratios and its input/outputs can be efficiently multiplexed in time, the construction of very large multiple chip CNNs for more complex vision tasks.
    Type of Medium: Electronic Resource
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