Recent Advances at the Interface of Neuroscience and Artificial Neural Networks

Biological neural networks adapt and learn in diverse behavioral contexts. Artificial neural networks (ANNs) have exploited biological properties to solve complex problems. However, despite their effectiveness for specific tasks, ANNs are yet to realize the flexibility and adaptability of biological cognition. This review highlights recent advances in computational and experimental research to advance our understanding of biological and artificial intelligence. In particular, we discuss critical mechanisms from the cellular, systems, and cognitive neuroscience fields that have contributed to refining the architecture and training algorithms of ANNs. Additionally, we discuss how recent work used ANNs to understand complex neuronal correlates of cognition and to pro-cess high throughput behavioral data.

Automated summary

This review article presents recent advancements in the study of biological and artificial neural networks, discussing the critical mechanisms that contribute to the improvement of artificial neural network architecture and training algorithms. It also explores how artificial neural networks have been used to understand neuronal correlates of cognition and process large-scale behavioral data.