# Biologically inspired ChaosNet architecture for Hypothetical Protein Classification

arXiv: https://arxiv.org/abs/2302.02427 · HTML: https://mlsanity.com/paper/2302.02427/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 32 references that are in the map.
- First posted on arXiv 5 Feb 2023.

## Builds on (most cited first, 2 of 2)

- [ChaosNet: A Chaos based Artificial Neural Network Architecture for Classification](https://mlsanity.com/paper/1910.02423/) (2019)
- [A Novel Chaos Theory Inspired Neuronal Architecture](https://mlsanity.com/paper/1905.12601/) (2019)

## Built on by (most cited first, 3 of 3)

- [Exploiting Chaotic Dynamics as Deep Neural Networks](https://mlsanity.com/paper/2406.02580/) (2024)
- [Self-Training the Neurochaos Learning Algorithm](https://mlsanity.com/paper/2601.01146/) (2026)
- [ChaosProbe: A Neurochaotic Lens on Frozen Transformer Input-Embedding Spaces](https://mlsanity.com/paper/2608.01968/) (2026)

## Abstract (arXiv)

ChaosNet is a type of artificial neural network framework developed for classification problems and is influenced by the chaotic property of the human brain. Each neuron of the ChaosNet architecture is the one-dimensional chaotic map called the Generalized Luroth Series (GLS). The addition of GLS as neurons in ChaosNet makes the computations straightforward while utilizing the advantageous elements of chaos. With substantially less data, ChaosNet has been demonstrated to do difficult classification problems on par with or better than traditional ANNs. In this paper, we use Chaosnet to perform a functional classification of Hypothetical proteins [HP], which is indeed a topic of great interest in bioinformatics. The results obtained with significantly lesser training data are compared with the standard machine learning techniques used in the literature.