Running deep reinforcement learning models
Deep Mind’s Nature paper has created an understandable buzz. In it they create an algorithm than can learn to play a huge slew of Atari computer games better than a human expert. Particularly compelling are the videos, which I encourage you to check out. One delightful feature of the paper is that they provide the \[…\]
Substantia Nigra and Reinforcement Learning
Robb Rutledge and I have a piece just out in the Journal of Neuroscience. It’s a Journal Club article – a ‘short, critical, review’ of a recent paper published by Ramayya et al in Michael Kahana’s group at University of Pennsylvania. The paper concerns a simple learning experiment performed in a group of people who \[…\]
How can we build computational models of happiness?
My colleagues recently published a seminal paper examining the dynamics of happiness within a prediction-error framework (Eh? What does this mean? Read on to find out).
Einstein in Berlin
At the end of July I enjoyed a few days in Berlin, attending the 4th (and, sadly, final) Einstein Fellowship Symposium on Decision-making, organised by my supervisor Ray Dolan. After 2 days of exceptionally high-quality talks and posters, we finished with a superb boat trip around Potsdam, a rather plush suburb riddled with lakes and \[…\]
SfN 2013- San Diego
This November, me and a gang from Queen Square descended, along with 30’000 other neuroscientists, upon San Diego, California. Amongst this maelstrom of intimidatingly brilliant minds I was to be found presenting a poster on happiness in a social context, which you can read here if you’re interested.