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Training spiking neural models using artificial bee colony.

Roberto A Vazquez1, Beatriz A Garro2

  • 1Intelligent Systems Group, Faculty of Engineering, La Salle University, Benjamín Franklin 47, Colonia Condesa, 06140 Mexico City, DF, Mexico.

Computational Intelligence and Neuroscience
|February 25, 2015
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Summary
This summary is machine-generated.

This study introduces a novel learning strategy using the Artificial Bee Colony (ABC) algorithm to train spiking neurons for efficient pattern recognition. The approach demonstrates the power of a single spiking neuron for complex tasks.

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Area of Science:

  • Computational Neuroscience
  • Artificial Intelligence
  • Bio-inspired Computing

Background:

  • Spiking neurons realistically model biological neurons and show potential for efficient pattern recognition.
  • A key limitation is the lack of effective learning strategies for training these spiking neuron models.
  • Bio-inspired algorithms offer solutions for optimization problems, including those in artificial neural networks (ANNs).

Purpose of the Study:

  • To propose and evaluate the Artificial Bee Colony (ABC) algorithm as a learning strategy for training spiking neurons.
  • To demonstrate the application of this approach to solve pattern recognition problems.
  • To analyze the performance improvements achieved by using ABC for spiking neuron training.

Main Methods:

  • The Artificial Bee Colony (ABC) algorithm, inspired by bee foraging behavior, is adapted as a learning strategy.
  • The ABC algorithm is used to train a single spiking neuron model.
  • The trained model is tested on various pattern recognition tasks.

Main Results:

  • The proposed ABC-based learning strategy effectively trains spiking neurons for pattern recognition.
  • The study validates the capability of a single spiking neuron for complex pattern recognition tasks.
  • Significant performance improvements are observed when using the ABC learning strategy.

Conclusions:

  • The Artificial Bee Colony (ABC) algorithm provides a viable and effective learning strategy for spiking neurons.
  • This approach enhances the applicability of spiking neurons in pattern recognition, even with a single neuron.
  • The integration of bio-inspired algorithms like ABC opens new avenues for advanced neural network training.