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Neural networks have been rapidly expanding in recent years, with novelstrategies and applications. However, challenges such as interpretability,explainability, robustness, safety, trust, and sensibility remain unsolved inneural network technologies, despite the fact that they will unavoidably beaddressed for critical applications. Attempts have been made to overcome thechallenges in neural network computing by representing and embedding domainknowledge in terms of symbolic representations. Thus, the neuro-symboliclearning (NeSyL) notion emerged, which incorporates aspects of symbolicrepresentation and bringing common sense into neural networks (NeSyL). Indomains where interpretability, reasoning, and explainability are crucial, suchas video and image captioning, question-answering and reasoning, healthinformatics, and genomics, NeSyL has shown promising outcomes. This reviewpresents a comprehensive survey on the state-of-the-art NeSyL approaches, theirprinciples, advances in machine and deep learning algorithms, applications suchas opthalmology, and most importantly, future perspectives of this emergingfield.

Neuro-Symbolic Learning: Principles and Applications in Ophthalmology