Neural models for sampling-based probabilistic inference and learning (PhD)

My project will explore how probabilistic sampling-based inference and learning might be implemented in biological neural networks. Several areas in which contributions could be made have been identified including exploring changes to the network dynamics that improve sampling efficiency and considering whether sampling like behaviour can be achieved in a network with deterministic but chaotic dynamics. It is anticipated that the models developed will be validated by both testing their predictive power on multielectrode array recordings of network spike responses and by analysing their ability to replicate the results of psychophysical studies on probabilistic perception and learning.

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