Computational Learning and Memory Group Welcome Trust Investigator Award

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The brain has a remarkable capacity to learn continuously about the environment and to use this knowledge flexibly to make predictions and guide its future decisions. Our group studies learning and memory from computational, algorithmic/representational and neurobiological viewpoints. We also maintain an active interest in the possible computational functions of neural oscillations, particularly those present in the hippocampus and neocortex.

Computationally and algorithmically, we use ideas from Bayesian approaches to statistical inference and reinforcement learning to characterize the goals and mechanisms of learning in terms of normative principles and behavioral results. We also perform dynamical systems analyses of reduced biophysical models to understand the mapping of these mechanisms into cellular and network models.

We collaborate very closely with experimental neuroscience groups, doing in vitro intracellular recordings, multi-unit recordings in behaving animals, and human psychophysical and fMRI experiments.

Computational Learning and Memory Lab

News

15 / 07 / 15 postdoc positions in the Lengyel group
20 / 01 / 15 5 presentations accepted at Cosyne, e.g. a talk by Yan Wu about representing context in the hippocampus
09 / 09 / 14 three papers accepted at NIPS, one talk, two posters
27 / 02 / 14 paper on optimal memory recall from bounded synapses published in PLoS Comput Biol
21 / 02 / 14 Jean-Pascal Pfister (first alumnus of the lab) receives SNF Professorship at INI (U Zurich / ETH), Zurich
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Best viewed with Firefox 3, Safari 4 or Explorer 8. 2009 Computational & Biological Learning Lab, Department of Engineering, University of Cambridge