Thomas Parr
Wellcome Centre for Human Neuroimaging,
Institute of Neurology, UCL, UK
Title:
The anatomy of (active) inference
Abstract:
Modern theories of
brain function often appeal to the metaphor of the brain as a scientist,
engaged in a process of inference about incoming sensory data, and selecting
actions to obtain new data. This can be formalised through an appeal to
Bayesian inference – where perception and action can be thought of as
optimising the evidence (a.k.a. marginal likelihood) for a statistical model of
the world. This view has important consequences for brain circuitry, in virtue
of the Markov blankets (statistical independencies) implied by a given model of
the world. In brief, if we knew everything about the Markov blanket of a variable,
we would need no additional information to draw inferences about it. This fact
is exploited to derive efficient inferential message passing schemes in machine
learning and provides a useful perspective on the local (synaptic) computations
that underwrite inference in biological systems. Importantly, this means that
the generative model employed by the brain is an important constraint upon (and
is constrained by) brain architectures. Drawing from examples in active vision
and oculomotion, this talk will unpack the notion of a generative model and the
neuronal message passing it implies. In doing so, we will illustrate through
simulation how this approach may be used to reproduce features of behaviour,
electrophysiology, and pathology.
Bio-sketch:
Thomas began his
academic career at University College London (UCL) medical school. After
completing an undergraduate degree in Medical Sciences with Neuroscience, he
enrolled on the UCL MBPhD programme, combining his
PhD with clinical studies. He works in the theoretical neurobiology group, led
by Professor Karl Friston, at the Wellcome Centre for Human Neuroimaging at the
UCL Institute of Neurology. His research interests include active inference,
computational neuropsychology, and the oculomotor system.