Random Heterogeneous Neurochaos Learning Architecture for Data Classification

TL;DR

This paper introduces a novel Random Heterogeneous Neurochaos Learning architecture that mimics brain-like randomness and heterogeneity, demonstrating superior performance in data classification tasks over existing neural network models.

Contribution

It proposes the first random heterogeneous extension of Neurochaos Learning, combining chaos-based neurons with randomness to better emulate brain structure and improve classification accuracy.

Findings

RHNL outperforms homogeneous NL and fixed heterogeneous NL architectures.

RHNL achieves near-perfect F1 scores on multiple datasets.

RHNL outperforms stand-alone ML classifiers, especially with limited training data.

Abstract

Inspired by the human brain's structure and function, Artificial Neural Networks (ANN) were developed for data classification. However, existing Neural Networks, including Deep Neural Networks, do not mimic the brain's rich structure. They lack key features such as randomness and neuron heterogeneity, which are inherently chaotic in their firing behavior. Neurochaos Learning (NL), a chaos-based neural network, recently employed one-dimensional chaotic maps like Generalized L\"uroth Series (GLS) and Logistic map as neurons. For the first time, we propose a random heterogeneous extension of NL, where various chaotic neurons are randomly placed in the input layer, mimicking the randomness and heterogeneous nature of human brain networks. We evaluated the performance of the newly proposed Random Heterogeneous Neurochaos Learning (RHNL) architectures combined with traditional Machine Learning (ML) methods. On public datasets, RHNL outperformed both homogeneous NL and fixed heterogeneous NL architectures in nearly all classification tasks. RHNL achieved high F1 scores on the Wine dataset (1.0), Bank Note Authentication dataset (0.99), Breast Cancer Wisconsin dataset (0.99), and Free Spoken Digit Dataset (FSDD) (0.98). These RHNL results are among the best in the literature for these datasets. We investigated RHNL performance on image datasets, where it outperformed stand-alone ML classifiers. In low training sample regimes, RHNL was the best among stand-alone ML. Our architecture bridges the gap between existing ANN architectures and the human brain's chaotic, random, and heterogeneous properties. We foresee the development of several novel learning algorithms centered around Random Heterogeneous Neurochaos Learning in the coming days.