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Brain algorithmics: reverse engineering dynamic information processing in brain networks from EEG/MEG time series

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The ultimate goal of cognitive neuroscience is to understand the brain as an organ of information processing. This will remain difficult unless we understand more directly what information the brain processes when it categorizes the external world. For example, our brain can extract from a face—a powerful social communication tool—information to categorize identity, age, gender, ethnicity, emotion, personality and even health. Though our brain knows what information to use for each task, as information receivers we typically do not have direct access to this knowledge. The current state of cognitive neuroscience is similar – we aim to understand the brain as an information processor, but we do not know what stimulus information it processes. Using face categorisations, I will present a framework and recent examples that started to address this fundamental problem. We start by first isolating what specific information underlies a given face categorization, and then we examine where, when and how the brain networks process this information.

This talk is part of the Zangwill Club series.

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