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Computational Neuroscience Journal Club

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  • UserCalvin Kao and Marine Schimel
  • ClockTuesday 15 December 2020, 15:00-16:30
  • HouseOnline on Zoom.

If you have a question about this talk, please contact Jake Stroud .

Please join us for our fortnightly journal club online via zoom where two presenters will jointly present a topic together. The next topic is ‘Successor representations in RL and neuroscience’ presented by Calvin Kao and Marine Schimel.

Zoom information:

https://us02web.zoom.us/j/84197886178?pwd=SVFKbjc0dmVFR3JwYTQ5ek5UM1pTQT09

Meeting ID: 841 9788 6178

Passcode: 659046

Topic summary:

Reinforcement learning algorithms are commonly categorised as either model-based, where the agent has an explicit model of the environment, or model-free, where it does not. Successor representations (SR) are used in a class of reinforcement learning algorithms that lie between these two extremes. These algorithms enable fast generalisation to new tasks with different reward distributions but the same transition dynamics. In recent years, there has been increasing interest in SR within both the neuroscience and machine learning communities, as SR are useful for meta-reinforcement learning and there is growing behavioural and neural evidence that the brain uses SR for learning. In this journal club, we will briefly review basic concepts in reinforcement learning before introducing the SR framework for reinforcement learning. We will then review two recent papers that provide evidence for SR in the brain:

1. The successor representation in human reinforcement learning, Momennejad et al., 2017, Nature Human Behaviour

2. The hippocampus as a predictive map, Stachenfeld et al., 2017, Nature Neuroscience

This talk is part of the Computational Neuroscience series.

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