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Injecting artificial noise into threshold neural networks enables gradient-based training and improves generalization. This stochastic resonance approach optimizes noise levels for better machine learning performance.

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Area of Science:

  • Machine Learning
  • Artificial Intelligence
  • Computational Neuroscience

Background:

  • Feedforward threshold neural networks traditionally lack gradient-based trainability.
  • Parameter space and synaptic weight ranges are often limited in standard threshold networks.

Purpose of the Study:

  • To investigate the impact of injecting artificial noise into feedforward threshold neural networks.
  • To develop a stochastic-resonance-based threshold neural network trainable by gradient-based methods.
  • To enhance the generalization capabilities of threshold neural networks.

Main Methods:

  • Introduced artificial noise into a feedforward threshold neural network architecture.
  • Developed an adaptive mechanism for noise level convergence to an optimal value.
  • Provided theoretical proof for noise acting as a generalized Tikhonov regularizer.
  • Conducted experiments on regression and classification tasks.

Main Results:

  • The noise injection enabled gradient-based training of the threshold neural network.
  • The noise level adaptively converged to a nonzero optimal value.
  • Theoretical analysis confirmed noise's role as a generalized Tikhonov regularizer.
  • Experimental results showed improved generalization for regression and classification problems.

Conclusions:

  • Injecting noise into threshold neural networks enhances their trainability and generalization.
  • The proposed stochastic-resonance-based network offers adaptive noise optimization.
  • This work demonstrates the potential of adaptive stochastic resonance in machine learning applications.