Prefrontal cortex as a meta-reinforcement learning system

Recently, AI systems have mastered a range of video-games such as Atari classics Breakout and Pong. But as impressive as this performance is, AI still relies on the equivalent of thousands of hours of gameplay to reach and surpass the performance of human video game players. In contrast, we can usually grasp the basics of a video game we have never played before in a matter of minutes.The question of why the brain is able to do so much more with so much less has given rise to the theory of meta-learning, or learning to learn. It is thought that we learn on two timescales in the short term we focus on learning about specific examples while over longer timescales we learn the abstract skills or rules required to complete a task. It is this combination that is thought to help us learn efficiently and apply that knowledge rapidly and flexibly on new tasks. Recreating this meta-learning structure in AI systems called meta-reinforcement learning has proven very fruitful in facilitating fast, one-shot, learning in our agents (see our paper and closely related work from OpenAI). However, the specific mechanisms that allow this process to take place in the brain are still largely unexplained in neuroscience.Read More

Navigating with grid-like representations in artificial agents

Most animals, including humans, are able to flexibly navigate the world they live in exploring new areas, returning quickly to remembered places, and taking shortcuts. Indeed, these abilities feel so easy and natural that it is not immediately obvious how complex the underlying processes really are. In contrast, spatial navigation remains a substantial challenge for artificial agents whose abilities are far outstripped by those of mammals.In 2005, a potentially crucial part of the neural circuitry underlying spatial behaviour was revealed by an astonishing discovery: neurons that fire in a strikingly regular hexagonal pattern as animals explore their environment. This lattice of points is believed to facilitate spatial navigation, similarly to the gridlines on a map. In addition to equipping animals with an internal coordinate system, these neurons – known as grid cells – have recently been hypothesised to support vector-based navigation. That is: enabling the brain to calculate the distance and direction to a desired destination, as the crow flies, allowing animals to make direct journeys between different places even if that exact route had not been followed before.The group that first discovered grid cells was jointly awarded the 2014 Nobel Prize in Physiology or Medicine for shedding light on how cognitive representations of space might work.Read More

DeepMind, meet Android

Were delighted to announce a new collaboration between DeepMind for Google and Android, the worlds most popular mobile operating system. Together, weve created two new features that will be available to people with devices running Android P later this year:Read More