  Even though the brain operates in pure darkness, within the skull, it can
infer the most likely causes of its sensory input. An approach to modelling
this inference is to assume that the brain has a generative model of the world,
which it can invert to infer the hidden causes behind its sensory stimuli, that
is, perception. This assumption raises key questions: how to formulate the
problem of designing brain-inspired generative models, how to invert them for
the tasks of inference and learning, what is the appropriate loss function to
be optimised, and, most importantly, what are the different choices of mean
field approximation (MFA) and their implications for variational inference
(VI).

Neural and Evolutionary Computing;Machine Learning