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A talk of two distinct parts: 1) a parametric empirical Bayesian approach to integration of fMRI, EEG and MEG data, 2) prediction error in episodic memory encoding

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

In an attempt to find at least one patch of common ground, I will start by talking about a recent methodological approach to multimodal integration of human neuroimaging data (specifically EEG , MEG and fMRI) that uses a parametric empirical Bayesian framework (recently reviewed in Henson et al, 2011, Frontiers in Human Neuroscience). Using a linear, hierarchical model of the M/EEG inverse problem under Gaussian assumptions: 1) MEG and EEG are fused symmetrically, with separate noise regularisations (hyperparameters) estimated from the data, and 2) M/EEG and fMRI are integrated asymmterically, with separate fMRI clusters forming separate spatial priors. Then in the fifth sixth of the talk, I will switch topics completely to talk about recent behavioural experiments that test the role of prediction error in one-shot associative encoding, as a model of human episodic memory, based on our PIMMS (Predictive Interactive Multiple Memory Signals) framework for understanding the neuroscience of memory and perception (Henson & Gagnepain, Hippocampus, 2010). I will then ask for your help in understanding why we cannot fit these data with various simple Hebbian learning rules.

This talk is part of the Computational and Biological Learning Seminar Series series.

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