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Large developing receptive fields using a distributed and locally reprogrammable address-event receiver.

Simeon A Bamford1, Alan F Murray, David J Willshaw

  • 1Institute of Integrated Micro and Nano Systems, Neuroinformatics Doctoral Training Centre, University of Edinburgh, Edinburgh, UK. simeon.bamford@iss.infn.it

IEEE Transactions on Neural Networks
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PubMed
Summary

This study introduces a novel address-event receiver enabling large axonal fan-out and biologically realistic learning rules. It facilitates synaptic rewiring and weight plasticity for adaptive neural network development.

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Area of Science:

  • Neuroscience
  • Computer Engineering
  • Artificial Intelligence

Background:

  • Traditional neural network architectures face limitations in scalability and biological realism.
  • Developing systems that mimic brain plasticity is crucial for advanced AI.

Purpose of the Study:

  • To design a distributed address-event receiver with local reprogrammability.
  • To implement biologically realistic synaptic plasticity, including rewiring and weight changes.

Main Methods:

  • Simultaneous monitoring of address-events by all synapses for large axonal fan-out.
  • Synaptic address modification for implementing distributed learning rules.
  • Probabilistic synapse formation and competitive Hebbian learning (STDP) for plasticity.

Main Results:

  • Achieved arbitrarily large axonal fan-out without reducing channel capacity.
  • Demonstrated distributed implementation of synapse formation/elimination (rewiring).
  • Enabled topographic map development and receptive field modification via weight and rewiring plasticity.

Conclusions:

  • The designed receiver supports scalable, adaptive neural network architectures.
  • It integrates synaptic plasticity mechanisms for enhanced network function and topology.
  • This work advances the development of neuromorphic computing systems.