Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.798537
Title: The computational neurology of active vision
Author: Parr, Thomas
ISNI:       0000 0004 8507 6847
Awarding Body: UCL (University College London)
Current Institution: University College London (University of London)
Date of Award: 2019
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Abstract:
In this thesis, we appeal to recent developments in theoretical neurobiology - namely, active inference - to understand the active visual system and its disorders. Chapter 1 reviews the neurobiology of active vision. This introduces some of the key conceptual themes around attention and inference that recur through subsequent chapters. Chapter 2 provides a technical overview of active inference, and its interpretation in terms of message passing between populations of neurons. Chapter 3 applies the material in Chapter 2 to provide a computational characterisation of the oculomotor system. This deals with two key challenges in active vision: deciding where to look, and working out how to look there. The homology between this message passing and the brain networks solving these inference problems provide a basis for in silico lesion experiments, and an account of the aberrant neural computations that give rise to clinical oculomotor signs (including internuclear ophthalmoplegia). Chapter 4 picks up on the role of uncertainty resolution in deciding where to look, and examines the role of beliefs about the quality (or precision) of data in perceptual inference. We illustrate how abnormal prior beliefs influence inferences about uncertainty and give rise to neuromodulatory changes and visual hallucinatory phenomena (of the sort associated with synucleinopathies). We then demonstrate how synthetic pharmacological perturbations that alter these neuromodulatory systems give rise to the oculomotor changes associated with drugs acting upon these systems. Chapter 5 develops a model of visual neglect, using an oculomotor version of a line cancellation task. We then test a prediction of this model using magnetoencephalography and dynamic causal modelling. Chapter 6 concludes by situating the work in this thesis in the context of computational neurology. This illustrates how the variational principles used here to characterise the active visual system may be generalised to other sensorimotor systems and their disorders.
Supervisor: Friston, K. ; Rees, G. Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.798537  DOI: Not available
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