Adrián Hernández and José María Amigó (MHU)
Abstract: Neuroscience and machine learning are two interrelated fields with obvious synergies. In recent years we have seen profound advances in these fields and in the integration between them. However, there remain challenges for a greater integration between the two disciplines. In this chapter we describe two areas, different but related, that can serve as a guide in the future for this integration. On the one hand, it is necessary to have more explanatory algorithms in order to understand information processing in the brain. The combination of multilayer and adaptive networks can be the right framework to understand this processing and analyse the interesting computational capabilities that occur in the brain. On the other hand, machine learning algorithms should have, similar to brain processing, more innate structure. This prior structure could make the process of learning more efficient and intuitive, and support artificial intelligence.