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Abstract
  • An increasing number of research groups develop
    dedicated hybrid analog/digital very large scale integration
    (VLSI) devices implementing hundreds of spiking neurons with
    bio–physically realistic dynamics.
    However, despite the significant progress in their design, there
    is still little insight in translating circuitry of neural assemblies
    into desired (non-trivial) function.
    In this work, we propose to use neural circuits implementing the
    soft Winner–Take–All (WTA) function. By showing that recur-
    rently connected instances of them can have persistent activity
    states, which can be used as a form of working memory, we argue
    that such circuits can perform state–dependent computation.
    We demonstrate such a network in a distributed neuromorphic
    system consisting of two multi–neuron chips implementing soft
    WTA, stimulated by an event–based vision sensor. The resulting
    network is able to track and remember the position of a localized
    stimulus along a trajectory previously encoded in the system.
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