  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.
