当噪声遇到混沌：神经混沌学习中的随机共振
When Noise meets Chaos: Stochastic Resonance in Neurochaos Learning
摘要：
混乱和噪音在大脑中无处不在。受神经元的混沌放电和噪声在神经元模型中的建设性作用的启发，我们首次将混沌、噪声和学习联系起来。在本文中，我们证明了随机共振（SR）现象在神经混沌学习（NL）。SR表现在NL的单个神经元的水平上，并且使得能够进行有效的亚阈值信号检测。此外，SR发生在单个和多个神经元NL架构的分类任务-无论是在模拟和真实世界的口语数字数据集。神经混乱学习中的中间水平噪音可以使分类任务达到峰值性能，从而凸显了SR在人工智能应用中的作用，特别是在大脑启发的学习架构中。
英文摘要：
Chaos and Noise are ubiquitous in the Brain. Inspired by the chaotic firing of neurons and the constructive role of noise in neuronal models, we for the first time connect chaos, noise and learning. In this paper, we demonstrate Stochastic Resonance (SR) phenomenon in Neurochaos Learning (NL). SR manifests at the level of a single neuron of NL and enables efficient subthreshold signal detection. Furthermore, SR is shown to occur in single and multiple neuronal NL architecture for classification tasks - both on simulated and real-world spoken digit datasets. Intermediate levels of noise in neurochaos learning enables peak performance in classification tasks thus highlighting the role of SR in AI applications, especially in brain inspired learning architectures.
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