UC Berkeley

A primary focus of neuroscience is understanding how information about the world is encoded in the activity of neurons. The classic approach to studying neural representation is to observe how activity in a neural system is modulated when presented with stimuli that vary along a specific, hypothesis-driven dimension (e.g. luminance, contrast, orientation, spatial frequency). This approach offers straight-forward interpretation and has allowed neuroscientists to develop computational models of the stimulus-response function for neurons in early processing stages of the visual system. However, the interpretations of these studies often do not generalize to other stimuli such as natural images, and the corresponding computational models often fail to explain later processing stages where the stimulus-response function is strikingly nonlinear. What is needed is an alternative approach that can a) inform new hypotheses without relying on the intuitions of the experimenter, b) generate tractable and interpretable nonlinear models of neural function based on these hypotheses, and c) directly assess model accuracy at predicting neural function under natural conditions. In this dissertation I propose a simple but powerful framework that uses statistical feature learning to automatically derive potential forms of neural representation and generates predictive and testable models of neural function based on these potential representations. The framework is general and can potentially be applied to data acquired using any physiological measurement technique (e.g. neurophysiology, neuroimaging, behavioral) in order to model neural representation throughout the brain. I demonstrate the potential of the proposed framework by first modeling the neural representation of image structure in early cortical stages of the macaque visual system (areas V1 and V2). I then apply the framework to investigate high-level scene processing in the human visual system. In both case studies, I am able to objectively derive accurate and interpretable nonlinear models of neural representation. In each study, I am also able to replicate and extend findings from a multitude of classic neurophysiology and neuroimaging studies using a single, simple experiment based on natural visual stimuli.