Noise as an Optimization Tool for Spiking Neural Networks

Yana Garipova; Shogo Yonekura; Yasuo Kuniyoshi

doi:10.20944/preprints202408.0704.v1

Preprint Brief Report Version 1 This version is not peer-reviewed

Version 1 : Received: 9 August 2024 / Approved: 9 August 2024 / Online: 9 August 2024 (10:28:12 CEST)

Standard ANNs lack flexibility when handling corrupted input due to their fixed structure. Spiking Neural Networks (SNNs) can utilize biological temporal coding features, such as noise-induced stochastic resonance and dynamical synapses to increase a model’s performance when its parameters are not optimized for a given input. Using the analog XOR task as a simplified Convolutional Neural Network analog, this paper demonstrates two key results: (1) SNNs solve the problem linearly inseparable in ANN with over 10% improvement in the optimal setting, and (2) in SNNs, the synaptic noise and dynamical synapses compensate for non-optimal parameters, achieving near-optimal results.

spiking neural networks; adaptability; noise

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

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Noise as an Optimization Tool for Spiking Neural Networks

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