Investigating Single-Neuron Mechanisms of Face Coding in the Human Brain
Abstract
How the brain encodes different face identities is one of the most fundamental and intriguing questions in neuroscience. There are currently two extreme hypotheses: (1) the exemplar-based model proposes that neurons respond in a remarkably selective and abstract manner to particular persons or objects, whereas (2) the axis-based model (a.k.a. feature-based model) posits that neurons distinguish facial features along specific axes (e.g., shape and skin color) in face space. However, a third under-explored coding scheme, the manifold-based coding, may exist in which neurons may encode the perceptual distance (i.e., similarity) between examples of faces at a macro level regardless of their individual features that may distinguish them at a micro level. This project aims to conduct one of the first studies to investigate face representation and coding in the human medial temporal lobe (MTL) at the single-neuron level. To the best of our knowledge, this will be the first study to directly compare different hypothesized neural coding schemes in the human MTL at the single-neuron level and also the first study to employ deep learning to study single-neuron responses in humans. Our single-neuron recordings will enable us to construct, validate, and explain neural face models to derive a general neural representation of faces. We will then use a deep neural network to explore the different neural face models listed above and thus be able to identify the predominant neural coding scheme in the human MTL. Together, our state-of-the-art human single-neuron recordings, powered by the latest image processing tools, will provide the most comprehensive and detailed analysis of neural representations of faces in humans with the highest possible spatial and temporal resolution to date.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Jan 21, 2022
- Source ID
- FA95502110088XX0
Entities
People
- Shuo Wang
Organizations
- Air Force Office of Scientific Research
- United States Air Force
- West Virginia University