Behavioral Social Neuroscience Seminar
Baxter B125
A Bayesian Approach to Internal Models: Of Ferret and Men
Máté Lengyel,
Reader in Computational Neuroscience,
Computational and Biological Learning Lab, Department of Engineering,
University of Cambridge,
Abstract
Our percepts rely on an internal model of the environment, relating physical processes of the world to inputs received by our senses, and thus their veracity critically hinges upon how well this internal model is adapted to the statistical properties of the environment. We used a combination of Bayesian inference-based theory and novel data analysis techniques applied to a range of human behavioural experiments, as well as ferret electrophysiological recordings, to reveal the principles by which complex internal models (1) are acquired through experience, (2) are represented in neural activities, (3) can be shown to be task-independent, and in fact (4) generalise across very different response modalities.
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