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Structural Plasticity on the SpiNNaker Many-Core Neuromorphic System.

Petruț A Bogdan1, Andrew G D Rowley1, Oliver Rhodes1

  • 1School of Computer Science, University of Manchester, Manchester, United Kingdom.

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|July 24, 2018
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Summary
This summary is machine-generated.

This study models topographic map formation on the SpiNNaker neuromorphic platform. Synaptic rewiring and spike-timing dependent plasticity (STDP) were used to refine neural network connectivity.

Keywords:
SpiNNakerneuromorphic computingspiking neural networksstructural synaptic plasticitysynaptic rewiringsynaptogenesistopographic map

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Area of Science:

  • Computational neuroscience
  • Neuromorphic engineering

Background:

  • Topographic maps are crucial for sensory processing, enabling efficient neural wiring and integration.
  • Understanding topographic map formation is key to developing advanced artificial intelligence systems.

Purpose of the Study:

  • To implement and evaluate a model of topographic map formation on the SpiNNaker neuromorphic platform.
  • To investigate the combined roles of synaptic rewiring and spike-timing dependent plasticity (STDP) in refining topographic maps.

Main Methods:

  • Real-time simulation of a point neuron model on the SpiNNaker neuromorphic platform.
  • Utilizing both synaptic rewiring and STDP for learning and network refinement.
  • Generating topographic maps between neural layers with sparse initial connectivity.

Main Results:

  • Synaptic rewiring significantly refines topographic maps beyond the capabilities of STDP alone.
  • STDP-induced input selectivity is effectively embedded into network connectivity via rewiring.
  • The model successfully generates topographic maps and stabilizes otherwise unstable mappings using lateral inhibition.

Conclusions:

  • A novel model demonstrates effective topographic map formation on neuromorphic hardware.
  • The synergistic interaction between synaptic rewiring and STDP is crucial for precise neural map development.
  • This approach offers a pathway for creating more sophisticated and efficient artificial neural systems.