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Neuron type processor modeling using a timed Petri net.

M K Habib1, R W Newcomb

  • 1Dept. of Electr. and Comput. Eng., Kuwait Univ., Safat.

IEEE Transactions on Neural Networks
|January 1, 1990
PubMed
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This study models digital neurons using timed Petri nets, enabling analysis of neuron-type processors (NTPs) before hardware implementation. This approach facilitates the design and verification of complex neural networks.

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Neuroscience

Background:

  • Digital neurons are fundamental components in artificial neural networks.
  • Modeling and analysis of neuron-type processors (NTPs) are crucial for efficient hardware implementation.
  • Timed Petri nets offer a powerful framework for representing and analyzing dynamic systems.

Purpose of the Study:

  • To review the basic operation of a digital neuron.
  • To introduce the theory of time Petri nets for modeling and analyzing NTPs.
  • To develop a timed Petri net model for a digital NTP.

Main Methods:

  • The study reviews the theory of time Petri nets.
  • A timed Petri net model is developed for the neuron-type processor (NTP).

Related Experiment Videos

  • The model is structured using interconnected essential module units (EMUs).

    • Main Results:

    • A timed Petri net model representing the digital NTP's asynchronous and sequential operation is produced.
    • The model captures essential functions like temporal and spatial summation and thresholding.
    • The interconnection of EMUs allows for scalability based on required input dendrites.

    Conclusions:

    • The developed timed Petri net model provides a method for analyzing neural networks with NTPs.
    • This analysis can be performed prior to hardware implementation, saving resources.
    • The approach facilitates the design and verification of digital neural processing systems.