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From tuning curves to behaviour

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If you have a question about this talk, please contact Dr Máté Lengyel.

Trading off speed and accuracy in decision-making is a hard computational problem. This is partly because whenever we make a decision we are forgoing future rewards, whose value is hard to estimate, and partly because feedback comes only when we make a decision, which means we often have to learn from relatively sparse data.

We use reinforcement learning to find the optimal tradeoff between speed and accuracy in the random dot kinematogram task (Newsome et al,1989), a task in which subjects estimate the direction of moving dots against a noisy background. We employ a biologically plausible learning algorithm, Temporal Difference learning, to learn the optimal behavior through trial and error, as animals do. Based on this, we propose a rate-based neuronal network that could, in principle, explain the activity observed in area LIP in monkeys performing the random dot kinematogram task.

This talk is part of the Computational Neuroscience series.

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