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Unified probabilistic modelling of adaptive spatial-temporal structures in the human brain.

Learning from experience and adapting our behaviour to new situations is a fundamental skill for our everyday interactions. But what are the brain plasticity mechanisms that mediate an individual's ability to make progress during training on complex tasks? What is it that differentiates `good' from `poor' learners in their ability to adapt? Recent advances in functional brain imaging technology provide us with the unique opportunity to study how the human brain changes with learning. However, the existing methods focus predominantly on modelling brain activity data within a single session rather than across training sessions. As such, these methods are not capable of capturing larger scale dependencies emerging in brain activity as training progresses. We will develop a novel methodology that allows holistic unified modelling of a series of brain imaging data measured during the course of learning. Using this methodology we will study brain changes that result from extensive training on complex visual tasks.

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“Neurocomputation: from brains to machines” 25th November 2015, Corpus Christi College

Nov 09, 2015

You are invited to attend the next in the series of Cambridge Neuroscience Interdisciplinary Workshops on “Neurocomputation: from brains to machines”. This workshop is being hosted by Professor Zoe Kourtzi and is being run in association with the Big Data Strategic Research Initiative.

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