# When Noise meets Chaos: Stochastic Resonance in Neurochaos Learning

arXiv: https://arxiv.org/abs/2102.01316 · HTML: https://mlsanity.com/paper/2102.01316/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 29 references that are in the map.
- First posted on arXiv 2 Feb 2021.

## Builds on (most cited first, 1 of 1)

- [ChaosNet: A Chaos based Artificial Neural Network Architecture for Classification](https://mlsanity.com/paper/1910.02423/) (2019)

## Built on by (most cited first, 10 of 10)

- [Random Heterogeneous Neurochaos Learning Architecture for Data Classification](https://mlsanity.com/paper/2410.23351/) (2024)
- [Cause-Effect Preservation and Classification using Neurochaos Learning](https://mlsanity.com/paper/2201.12181/) (2022)
- [Self-Training the Neurochaos Learning Algorithm](https://mlsanity.com/paper/2601.01146/) (2026)
- [Learning Generalized Causal Structure in Time-series](https://mlsanity.com/paper/2112.03085/) (2021)
- [Predictive coding and stochastic resonance as fundamental principles of auditory perception](https://mlsanity.com/paper/2204.03354/) (2022)
- [Neurochaos Feature Transformation and Classification for Imbalanced Learning](https://mlsanity.com/paper/2205.06742/) (2022)
- [Integrating Causality with Neurochaos Learning: Proposed Approach and Research Agenda](https://mlsanity.com/paper/2501.13763/) (2025)
- [A Compression Based Classification Framework Using Symbolic Dynamics of Chaotic Maps](https://mlsanity.com/paper/2508.02330/) (2025)
- [Linked Data Classification using Neurochaos Learning](https://mlsanity.com/paper/2602.16204/) (2026)
- [From Cortical Synchronous Rhythm to Brain Inspired Learning Mechanism: An Oscillatory Spiking Neural Network with Time-Delayed Coordination](https://mlsanity.com/paper/2605.01656/) (2026)

## Abstract (arXiv)

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.