ArticleFrontiers in computational neuroscience2020
Stochastic Resonance Based Visual Perception Using Spiking Neural Networks.
Article in Frontiers in computational neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- Adrenergic Modulation of Cortical Gain and Sensory Processing in the Mouse Visual Cortex.Brain sciences · 2025Article
- Random fluctuations and synaptic plasticity enhance working memory activities in the neuron-astrocyte network.Cognitive neurodynamics · 2024Article
- Mutual information measure of visual perception based on noisy spiking neural networks.Frontiers in neuroscience · 2023Article
- Emergence of stochastic resonance in a two-compartment hippocampal pyramidal neuron model.Journal of computational neuroscience · 2022Article
- Aperiodic stochastic resonance in neural information processing with Gaussian colored noise.Cognitive neurodynamics · 2021Article
- Dynamics of a Large-Scale Spiking Neural Network with Quadratic Integrate-and-Fire Neurons.Neural plasticity · 2021Article
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Authors and funding
3 authors.
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Abstract
Our aim is to propose an efficient algorithm for enhancing the contrast of dark images based on the principle of stochastic resonance in a global feedback spiking network of integrate-and-fire neurons. By linear approximation and direct simulation, we disclose the dependence of the peak signal-to-noise ratio on the spiking threshold and the feedback coupling strength. Based on this theoretical analysis, we then develop a dynamical system algorithm for enhancing dark images. In the new algorithm, an explicit formula is given on how to choose a suitable spiking threshold for the images to be enhanced, and a more effective quantifying index, the variance of image, is used to replace the commonly used measure. Numerical tests verify the efficiency of the new algorithm. The investigation provides a good example for the application of stochastic resonance, and it might be useful for explaining the biophysical mechanism behind visual perception.
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