Neurochaos Inspired Hybrid Machine Learning Architecture for Classification

Neuromorphic computing systems are biologically inspired with an aim to understand the rich structure and behaviour of biological neural networks so that novel learning architectures can be designed in both software and hardware. Traditional machine learning and deep neural network architectures are only weakly inspired from the human brain. In this work, we propose a novel ‘neurochaos’ inspired hybrid machine learning architecture for classification. Specifically, we extract four ‘neurochaos’ features – firing time, firing rate, energy and entropy of the chaotic neural firing from the neurons in the ChaosNet architecture (which we have recently proposed). These are used to train a Support Vector Machine linear classifier. Such a hybrid approach yields superior performance in the low training sample regime on synthetically generated and real-world datasets. Our proposed method could be viewed as a novel application of chaos as a kernel trick and has the potential for combining with other machine learning algorithms.