Modeling and Analysis of Neural Codes for Spatial Computation

As our brains' develop in early life, intrinsic representations of geometric space begin to set the groundwork for establishing connections between the world and the sensory world we perceive. This includes knowing where you are and knowing how to navigate to a target. In neurodegenerative diseases such as Alzheimer's' disease (AD), these facilities are some of the first behavioural traits to deteriorate. This opens the door to using spatial navigation as a behavioural marker in the early diagnosis of AD. Spatial navigation is proposed to be driven by both cognitive planning and intrinsic cognitive maps. Using virtual reality (VR), we can dissociate spatial navigation strategies and begin to understand which of these features is most affected. With these VR task, we hope to train machine learning algorithms to learn the relationships between task performance and neurodegenerative deficits. This may well provide diagnostic tools which are affordable, accessible and stress free.
We propose to investigate the utility of machine learning prediction first in neurodegenerative mouse models, followed by various AD mouse models, before finally validating our methods on humans including healthy, at-risk and AD patient cohorts. We further hope to develop new reinforcement learning algorithms aided by biologically plausible models of spatial computation in the brain. These models will form the basis of understanding the finer features of spatial computation deficits and more broadly neural computation in general.