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A Bio-Inspired Chaos Sensor Model Based on the Perceptron Neural Network: Machine Learning Concept and Application

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This study introduces a bio-inspired chaos sensor using a perceptron neural network to estimate spike train entropy in neurodynamic systems. The model accurately approximates fuzzy entropy, aiding computational neuroscience and bio-robotics development.

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Hindmarsh–Rose neuron modelbiosensorchaos sensorentropyperceptron

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Area of Science:

  • Computational Neuroscience
  • Bio-inspired Engineering
  • Artificial Intelligence

Background:

  • Neurodynamic systems generate complex spike train signals.
  • Estimating the entropy of these signals is crucial for understanding system dynamics.
  • Existing methods may lack accuracy or efficiency for real-time analysis.

Purpose of the Study:

  • To develop a bio-inspired chaos sensor model for estimating spike train entropy.
  • To utilize a perceptron neural network for accurate entropy approximation.
  • To demonstrate the model's applicability in neurodynamic systems and robotics.

Main Methods:

  • A perceptron neural network with specified hidden and output layers was trained.
  • The Hindmarsh-Rose spike model generated time series data for training and testing.
  • K-block cross-validation was employed for hyperparameter selection and accuracy assessment.

Main Results:

  • The perceptron model achieved high accuracy (R² ≈ 0.9) in approximating fuzzy entropy.
  • Even simplified models with fewer neurons showed good approximation (R² ≈ 0.5–0.8).
  • The model successfully tracked chaotic behavior in real-world action potential recordings.

Conclusions:

  • The bio-inspired chaos sensor effectively estimates spike train entropy in neurodynamic systems.
  • The model offers a promising tool for computational neuroscience research.
  • It has potential applications in creating advanced humanoid, animal, and bio-robots.