Brain and Cognitive Science Inspired Deep Learning: A Comprehensive Survey

Abstract

Deep learning (DL) is increasingly viewed as a foundational methodology for advancing Artificial Intelligence (AI). However, its interpretability remains limited, and it often underperforms in certain fields due to its lack of human-like characteristics. Consequently, leveraging insights from Brain and Cognitive Science (BCS) to understand and advance DL has become a focal point for researchers in the DL community. However, BCS is a diverse discipline where existing studies often concentrate on cognitive theories within their respective domains. These theories are typically grounded in certain assumptions, complicating comparisons between different approaches. Therefore, this review is intended to provide a comprehensive landscape of more than 300 papers on the intersection of DL and BCS grounded in DL community. Unlike previous reviews that based on sub-disciplines of Cognitive Science, this article aims to establish a unified framework encompassing all aspects of DL inspired by BCS, offering insights into the symbiotic relationship between DL and BCS. Additionally, we present a forward-looking perspective on future research directions, with the intention of inspiring further advancements in AI research.