The brain remains the only known example of a truly general-purpose intelligent system. The study of human and animal cognition has revealed key insights, such as the ideas of parallel distributed processing, biological vision, and learning from reward signals, that have heavily influenced the design of artificial learning systems. Many AI researchers continue to look to neuroscience as a source of inspiration and insight. A key difficulty is that neuroscience is a vast and heterogeneous area of study, encompassing a bewildering array of subfields. In this tutorial, we will seek to provide both a broad overview of neuroscience as a whole, as well as a focused look at two areas -- computational cognitive neuroscience and the neuroscience of learning in circuits -- that we believe are particularly relevant for AI researchers today. We will conclude by highlighting several ongoing lines of work that seek to import insights from these areas of neuroscience into AI, and vice versa.
Jane Wang (DeepMind)
Jane Wang is a research scientist at DeepMind on the neuroscience team, working on meta-reinforcement learning and neuroscience-inspired artificial agents. Her background is in physics, complex systems, and computational and cognitive neuroscience.
Kevin Miller (DeepMind and University College London)
Kevin Miller is a research scientist on the Neuroscience Team at DeepMind and a postdoc at University College London. He is currently working on understanding structured reinforcement learning in mice and machines.
Adam Marblestone (DeepMind)
Adam Marblestone is a Schmidt Futures innovation fellow, was previously a research scientist at DeepMind, and earlier did a PhD in BioPhysics and worked at a brain computer interface company.