Anthony Chen’s PhD Thesis Proposal

Title:Towards Probabilistic Modeling of Vestibular Integration from Peripheral Afferents to Multiple Senses”

Thesis Supervisors: Dr. Jean-Sébastien Blouin (Kinesiology), Dr. Robert Boushel (Kinesiology)
Committee Member: Dr. Chris Dakin (Utah State University)
Chair: Dr Bill Sheel

Abstract: The brain continuously transmits and integrates vestibular information with other sensory systems for self-motion perception and spatial orientation. This neural process allows for proper navigation, gaze stabilization, and postural balance; therefore, the ability to accurately map our place in three-dimensional space is essential for functioning in everyday life. The vestibular sense, due to its predominantly passive nature, is often underappreciated until deficiencies in the system occurs leading to erroneous motion perception. Such errors stem from the physical properties and neural organization of the vestibular system; however, they are difficult to reveal in everyday experiences. Experimentally controlled sensory stimulation, conversely, has allowed researchers to examine the neurophysiological and psychophysical behaviour of the system. The cumulation of these observations has allowed for the development of algorithmic structures that serves to provide insights regarding the brain’s neural process and, in turn, generate predictive power.

The overall aim of this doctoral proposal is to advance current knowledge of vestibular processing along various stages from peripheral to central therefore allowing us to better predict human motion perception. Specifically, we propose four experiments that will address different aspects of this overall goal from afferent to multi-modal encoding. Experiment 1 will evaluate whether primary afferent encoding during electrical stimulation mimics those during real motions. Experiment 2 will explore how differently adapting afferents are fused to influence motion perception. Data from Experiment 2 will help formulate a working vestibular model using Monte Carlo methods to frame Experiment 3 and 4 which will expand upon the neural encoding that arises from multiple vestibular end-organs and multiple sensory systems respectively. The results from these experiments will advance existing knowledge within sensory neuroscience by providing a novel probabilistic model of sensory processing.