  In this paper, we review recent approaches for explaining concepts in neural
networks. Concepts can act as a natural link between learning and reasoning:
once the concepts are identified that a neural learning system uses, one can
integrate those concepts with a reasoning system for inference or use a
reasoning system to act upon them to improve or enhance the learning system. On
the other hand, knowledge can not only be extracted from neural networks but
concept knowledge can also be inserted into neural network architectures. Since
integrating learning and reasoning is at the core of neuro-symbolic AI, the
insights gained from this survey can serve as an important step towards
realizing neuro-symbolic AI based on explainable concepts.
