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Learning and memory in neural networks: statistically optimal computations

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If you have a question about this talk, please contact Duncan Simpson .

How do networks of neurons in our brain endow us with the capacity to learn from experience and remember our past?

We are using Bayesian statistical theory to answer a different but related question first: what is the best way in which networks of neurons could behave in order to endow us with the capacity of learning and memory? Such normative theories were then turned into specific predictions about the spike timing-dependent neural dynamics of hippocampal pyramidal cells, and about the relationship between spontaneous and stimulus-evoked activity in visual cortical cells and its change with visual experience.

Experimental data collected by our collaborators confirmed our predictions in both cases, thereby suggesting that the brain may implement highly efficient computational strategies for learning and memory.

This talk is part of the Networks & Neuroscience series.

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