Mathematics Colloquia and Seminars

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I will present some recent computational models of brain circuits that can support predictive error-driven learning, along with a discussion of prior work on how the brain might support something like error backpropagation more generally. Error backpropagation is the engine of modern deep neural network models, and there has been a bit of a resurgence of interest in its possible biological basis recently. Top-down connections in the cortex can potentially provide a mechanism of error propagation, and there are various proposals that make distinct biological predictions, which will be reviewed. Predictive learning provides an attractive solution to a remaining challenge: where do all the error signals come from in the first place? Specific circuits between the thalamus and cortex appear ideally configured to support a form of predictive learning, which differs significantly from other machine-learning / Bayesian approaches. Our models show that this mechanism can learn abstract categorical representations from movies of rotating and translating 3D objects.