Location: Olsson Hall Conference Room 104
Add to Calendar 2023-06-13T10:00:00 2023-06-13T12:00:00 America/New_York PhD Proposal: Merat Rezaei All Invited Committee Members: Dr. Greg Gerling (advisor), SIE Dr. Matthew Bolton (chair), SIE Dr. Afsaneh Doryab, SIE Dr. Stephen Baek, SDS Dr. Brian Guthrie, Cargill, Incorporated Title: Computational Modeling of Mechanosensitive Neuron Populations in the Human Tongue During Oral Processing Abstract: Olsson Hall Conference Room 104

All Invited

Committee Members:

  • Dr. Greg Gerling (advisor), SIE
  • Dr. Matthew Bolton (chair), SIE
  • Dr. Afsaneh Doryab, SIE
  • Dr. Stephen Baek, SDS
  • Dr. Brian Guthrie, Cargill, Incorporated

Title: Computational Modeling of Mechanosensitive Neuron Populations in the Human Tongue During Oral Processing

Abstract:

When placed in the oral cavity, we almost immediately recognize the physical characteristics of food, such as its compliance, surface roughness, and geometry. Moreover, we can instinctively judge the amount of mastication necessary to break a food down into a bolus ready to be swallowed. The perceptual encoding of food during stages of oral processing is of significant importance in the research of food science, but efforts to reproduce percepts such as ‘firmness,’ ‘smoothness,’ and ‘thickness,’ via the modulation of tribological and rheological properties have been especially evasive. Furthermore, these efforts have overlooked roles of sensory and proprioceptive feedback from the tongue. In this proposed work, we seek to develop predictive computational models that will clarify the interplay of subtypes of sensory neural afferents during food breakdown, and their capacity to contribute to the neural encoding of stimulus compliance, geometry, and relative movement patterns over time. First, we will employ differential equation models that abstract the neural biophysics in generating mechanosensitive currents and spike firing. Second, we will build models of afferent population that vary in spatial configuration and density. Finally, we will leverage machine learning approaches using Convolutional Neural Networks and Gaussian Mixture Models to learn correlations and emergent features between afferent population responses and input stimulus parameters at timepoints during and beyond the first bite phase of oral processing. Our initial efforts in this space aim in the longer-term the development of a computational platform to constrain the solution space in creating new food products, while paring the need for tedious perceptual experimentation of foods during their spatiotemporal breakdown.