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Self-gating stochastic-resonance-based autoencoder for unsupervised learning.

Yuhao Ren¹, Fabing Duan¹, François Chapeau-Blondeau²

¹Institute of Complexity Science, <a href="https://ror.org/021cj6z65">Qingdao University</a>, Qingdao 266071, People's Republic of China.

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Summary

This summary is machine-generated.

Adding noise to neuron networks enhances information representation, leading to self-gating stochastic resonance. This noise-boosted model improves autoencoder performance in unsupervised learning.

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Area of Science:

Computational Neuroscience
Artificial Intelligence

Background:

McCulloch-Pitts neurons are fundamental units in neural networks.
Information processing in neural networks can be limited by noise.

Purpose of the Study:

To introduce a unified noise-boosted activation model for neural networks.
To investigate the phenomenon of self-gating stochastic resonance.
To demonstrate the application of this model in unsupervised learning.

Main Methods:

Incorporating additive noise components to McCulloch-Pitts neuron ensembles.
Multiplying input by average firing probability for self-gating.
Designing autoencoders with noise-boosted activation functions.

Main Results:

Additive noise enhances information representation, approaching average firing probability.
Self-gating stochastic resonance observed through autoencoder loss optimization.
Optimal performance achieved at non-zero noise-scaling hyperparameters.

Conclusions:

The noise-boosted activation model with self-gating enhances neural network performance.
Self-gating stochastic resonance is a key factor in improving unsupervised learning.
This approach shows potential for advancing unsupervised learning techniques.