Probabilistic computation in natural vision
A central goal of vision science is to understand the principles underlying the perception and neural coding of the complex visual environment of our everyday experience. In the visual cortex, foundational work with artificial stimuli, and more recent work combining natural images and deep convolutional neural networks, have revealed much about the tuning of cortical neurons to specific image features. However, a major limitation of this existing work is its focus on single-neuron response strength to isolated images. First, during natural vision, the inputs to cortical neurons are not isolated but rather embedded in a rich spatial and temporal context. Second, the full structure of population activity—including the substantial trial-to-trial variability that is shared among neurons—determines encoded information and, ultimately, perception. In the first part of this talk, I will argue for a normative approach to study encoding of natural images in primary visual cortex (V1), which combines a detailed understanding of the sensory inputs with a theory of how those inputs should be represented. Specifically, we hypothesize that V1 response structure serves to approximate a probabilistic representation optimized to the statistics of natural visual inputs, and that contextual modulation is an integral aspect of achieving this goal. I will present a concrete computational framework that instantiates this hypothesis, and data recorded using multielectrode arrays in macaque V1 to test its predictions. In the second part, I will discuss how we are leveraging this framework to develop deep probabilistic algorithms for natural image and video segmentation.
Can subjective experience be quantified? Critically examining computational cognitive neuroscience approaches
Computational and cognitive neuroscience techniques have made great strides towards describing the neural computations underlying perceptual inference and decision-making under uncertainty. These tools tell us how and why perceptual illusions occur, which brain areas may represent noisy information in a probabilistic manner, and so on. However, an understanding of the subjective, qualitative aspects of perception remains elusive: qualia, or the personal, intrinsic properties of phenomenal awareness, have remained out of reach of these computational analytic insights. Here, I propose that metacognitive computations, and the subjective feelings that go along with them, give us a solid starting point for understanding subjective experience in general. Specifically, perceptual metacognition possesses ontological and practical properties that provide a powerful and unique opportunity for studying the studying the neural and computational correlates of subjective experience using established tools of computational and cognitive neuroscience. By capitalizing on decades of developments in formal computational model comparisons as applied to the specific properties of perceptual metacognition, we are now in a privileged position to reveal new and exciting insights about how the brain constructs our subjective conscious experiences.