EXPLORING NEURAL REPRESENTATIONS IN MACAQUE PRIMARY VISUAL CORTEX THROUGH DATA-DRIVEN MODELS

Abstract

The study of the primary visual cortex (V1) holds profound significance for our understanding of the neural underpinnings of visual perception. Computational models have emerged as invaluable tools to decode the intricate computations occurring within V1 neurons. This dissertation embarks on a comprehensive exploration of V1 by fitting statistical models to electrophysiological data and scrutinizing model properties. My approach not only provides direct insights into how V1 neurons represent information but also furnishes mathematical descriptions of V1 computations, thereby contributing to the construction of a unified model of V1 function.I begin in Chapter 2 by employing state-of-the-art statistical and machine learning techniques to unravel the high-resolution components of V1 receptive fields as they respond to random bar stimuli. I demonstrate how these models not only replicate classical findings but also offer superior explanations of the computations V1 undertakes. These results highlight how the simultaneous processing of multiple overlapping inputs enables cells to represent high-resolution information while also responding to full-field inputs, an intricate organization unattainable using conventional stimuli. In chapter 3, I expand this modeling approach by adding mechanisms for binocular integration and apply them to data obtained from random bar stimuli that also vary in binocular disparity. This approach reveals that V1 disparity selectivity is enhanced and well characterized using spatial convolutions. Finally, I further modify the approach in chapter 4 to map spatiotemporal receptive fields in luminance and color using data recorded from the fovea and present the first spatiochromatic measurements illustrating the scale of V1 processing at the fovea. I find that color signals operate at lower spatial scales compared to luminance signals, and that receptive field substructure can allow even cells with large receptive fields to represent fine-scale information throughout the fovea.