Integrating Causality with Neurochaos Learning: Proposed Approach and Research Agenda

TL;DR

This paper explores integrating causality with Neurochaos Learning to improve machine learning models, addressing issues like diminishing returns and high energy consumption of deep neural networks.

Contribution

It proposes a novel approach to combine causal learning and Neurochaos Learning, outlining a research agenda for enhanced classification, prediction, and reinforcement learning.

Findings

Neurochaos Learning draws inspiration from neuronal chaos.

Causal learning helps reduce spurious correlations.

Integration aims to improve performance in linked data domains.

Abstract

Deep learning implemented via neural networks, has revolutionized machine learning by providing methods for complex tasks such as object detection/classification and prediction. However, architectures based on deep neural networks have started to yield diminishing returns, primarily due to their statistical nature and inability to capture causal structure in the training data. Another issue with deep learning is its high energy consumption, which is not that desirable from a sustainability perspective. Therefore, alternative approaches are being considered to address these issues, both of which are inspired by the functioning of the human brain. One approach is causal learning, which takes into account causality among the items in the dataset on which the neural network is trained. It is expected that this will help minimize the spurious correlations that are prevalent in the learned representations of deep neural networks. The other approach is Neurochaos Learning, a recent development, which draws its inspiration from the nonlinear chaotic firing intrinsic to neurons in biological neural networks (brain/central nervous system). Both approaches have shown improved results over just deep learning alone. To that end, in this position paper, we investigate how causal and neurochaos learning approaches can be integrated together to produce better results, especially in domains that contain linked data. We propose an approach for this integration to enhance classification, prediction and reinforcement learning. We also propose a set of research questions that need to be investigated in order to make this integration a reality.