ArticleEntropy (Basel, Switzerland)2025
Noise and Dynamical Synapses as Optimization Tools for Spiking Neural Networks.
Article in Entropy (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Bio-Inspired Spike-Timing-Dependent Plasticity Learning with Metal Halide Perovskites: Toward Artificial Synaptic Functionality.ACS applied materials & interfaces · 2026Article
- Encrypted Spiking Neural Networks Based on Adaptive Differential Privacy Mechanism.Entropy (Basel, Switzerland) · 2025Article
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3 authors.
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Abstract
Standard ANNs lack flexibility when handling corrupted input due to their fixed structure. In this paper, a spiking neural network utilizes biological temporal coding features in the form of noise-induced stochastic resonance and dynamical synapses to increase the model's performance when its parameters are not optimized for a given input. Using the analog XOR task as a simplified convolutional neural network model, this paper demonstrates two key results: (1) SNNs solve the problem that is linearly inseparable in ANN with fewer neurons, and (2) in leaky SNNs, the addition of noise and dynamical synapses compensate for non-optimal parameters, achieving near-optimal results for weaker inputs.
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