When Noise meets Chaos: Stochastic Resonance in Neurochaos Learning. (November 2021)

• Record Type: Journal Article
• Authors: N.B., Harikrishnan; Nagaraj, Nithin
• Is Part Of: Neural networks. Volume 143(2021)
• Page Start: 425
• Page End: 435
• Publication Date: 2021-11
• Subjects: Neurochaos Learning -- ChaosNet -- Machine learning -- Stochastic Resonance -- Hindmarsh–Rose neuronal model -- Noise; Neural computers -- Periodicals; Neural networks (Computer science) -- Periodicals; Neural networks (Neurobiology) -- Periodicals; Nervous System -- Periodicals; Ordinateurs neuronaux -- Périodiques; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux neuronaux (Neurobiologie) -- Périodiques; Neural computers; Neural networks (Computer science); Neural networks (Neurobiology); Periodicals; 006.32
• DOI: 10.1016/j.neunet.2021.06.025
• Languages: English
• ISSNs: 0893-6080

Abstract

Abstract: 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, and in architectures with 1 D chaotic maps as well as Hindmarsh–Rose spiking neurons. Intermediate levels of noise in neurochaos learning enable peak performance in classification tasks thus highlighting the role of SR in AI applications, especially in brain inspired learning architectures.