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Agreement in Spiking Neural Networks.

Martin Kunev1, Petr Kuznetsov1, Denis Sheynikhovich2

  • 1LTCI, Télécom Paris, Institut Polytechnique de Paris, Palaiseau, France.

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|March 25, 2022
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Summary

This study demonstrates binary agreement in spiking neural networks (SNNs) using auxiliary neurons. The findings suggest efficient agreement achievement and size-optimality in biologically plausible SNN models.

Keywords:
binary agreementcomplexityconsensusspiking neural networkwinner-take-all

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Area of Science:

  • Computational neuroscience
  • Artificial intelligence

Background:

  • Spiking neural networks (SNNs) are computational models inspired by biological neurons.
  • Binary agreement is a fundamental problem in distributed computing and neural computation.

Purpose of the Study:

  • To investigate the feasibility of achieving binary agreement in SNNs.
  • To analyze the resource requirements (auxiliary neurons and time) for binary agreement.
  • To introduce and study size-independent SNNs and their implications for agreement problems.

Main Methods:

  • Theoretical analysis of SNNs for binary agreement.
  • Simulations to evaluate network performance and convergence time.
  • Mathematical proofs for resource requirements in size-independent SNNs.

Main Results:

  • Binary agreement is achievable in SNNs with a specific number of auxiliary neurons.
  • Simulation results indicate agreement can be reached within a certain time frame.
  • Size-independent SNNs require a proven minimum number of auxiliary neurons for agreement and related tasks.

Conclusions:

  • The proposed SNN model efficiently solves the binary agreement problem.
  • The study introduces size-independence as a key property for biologically plausible and efficient SNNs.
  • The agreement network is shown to be size-optimal within the studied subclass of SNNs.