The emerging field of NeuroAI has leveraged techniques from artificial intelligence to analyze large-scale brain data. In this talk, I will show that the connection between neuroscience and AI can be fruitful in both directions. Towards “AI driving neuroscience”, I will discuss recent advances in self-supervised learning with deep recurrent networks that yield a developmentally-plausible model of the primate visual system.  In the direction of “neuroscience guiding AI”, I will present a novel cognitively-grounded computational theory of perception that generates powerful new learning algorithms for real-world scene understanding.  Taken together, these ideas illustrate how neural networks optimized to solve cognitively-informed tasks provide a unified framework for both understanding the brain and improving AI.