• DocumentCode
    1379815
  • Title

    Large Developing Receptive Fields Using a Distributed and Locally Reprogrammable Address–Event Receiver

  • Author

    Bamford, Simeon A. ; Murray, Alan F. ; Willshaw, David J.

  • Author_Institution
    Neuroinformatics Doctoral Training Centre, Univ. of Edinburgh, Edinburgh, UK
  • Volume
    21
  • Issue
    2
  • fYear
    2010
  • Firstpage
    286
  • Lastpage
    304
  • Abstract
    A distributed and locally reprogrammable address-event receiver has been designed, in which incoming address-events are monitored simultaneously by all synapses, allowing for arbitrarily large axonal fan-out without reducing channel capacity. Synapses can change the address of their presynaptic neuron, allowing the distributed implementation of a biologically realistic learning rule, with both synapse formation and elimination (synaptic rewiring). Probabilistic synapse formation leads to topographic map development, made possible by a cross-chip current-mode calculation of Euclidean distance. As well as synaptic plasticity in rewiring, synapses change weights using a competitive Hebbian learning rule (spike-timing-dependent plasticity). The weight plasticity allows receptive fields to be modified based on spatio-temporal correlations in the inputs, and the rewiring plasticity allows these modifications to become embedded in the network topology.
  • Keywords
    Hebbian learning; network topology; neural chips; Euclidean distance; Hebbian learning rule; axonal fan out; network topology; presynaptic neuron; receptive fields; reprogrammable address event receiver; spatio temporal correlations; synapses; synaptic rewiring; Address–event representation (AER); Euclidean distance; neural network architecture; neural network hardware; neuromorphic very large scale integration (VLSI); synapse elimination; synapse formation; synaptic rewiring; topographic map; Action Potentials; Algorithms; Axons; Computers; Humans; Learning; Memory; Neural Networks (Computer); Neuronal Plasticity; Normal Distribution; Presynaptic Terminals; Probability; Synapses; Time Factors;
  • fLanguage
    English
  • Journal_Title
    Neural Networks, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1045-9227
  • Type

    jour

  • DOI
    10.1109/TNN.2009.2036912
  • Filename
    5378466