Xin Huang - US grants

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. OBJECTIVE: To understand the neural mechanisms underlying visual perception and visually guided action. Specifically, we are investigating neural mechanisms of visual motion and form processing including their interactions using behaving, laboratory-acclimatized adult rhesus monkeys. An important aim of the study is to gain insight into how the visual system selectively integrates and segments multiple visual features to form perception of objects and to guide eye movements. Visual motion and form information is represented and processed by a large number of neurons distributed across many specialized brain areas. Each of these neurons is sensitive to certain features of the visual image and has a spatially constrained "view" of the world. Moreover, because many visual neurons are broadly tuned to stimulus features, any given visual feature is represented by the discharge of a large population of neurons. It is not clear how spatially localized representations of visual features are synthesized to form perception;it is also unknown how attributes of visual stimuli are decoded from distributed neural activity to make perceptual decisions and to guide eye movements. These are fundamental questions of vision research. Our study is directed at addressing these questions. A particular challenging scenario of visual processing occurs when multiple visual features are present in the visual scene. Visual system needs to selectively integrate and segment these features into distinct objects or surfaces. Representations of these objects or surfaces are then used to guide appropriate action. Our specific research goal is to clarify the neural mechanisms underlying these processes. Insights learned from our studies will help to gain better understanding of fundamental principles of normal brain functions, with the promise of better understanding the causes and developing new treatment of visual disorders. This new research uses start up funding and relies on WNRPC Animal Services.

Project Summary/Abstract In natural vision, it is rare to encounter an isolated object presented on a blank background. Instead, natural scenes are often complex and contain multiple entities. Image segmentation refers to the process of partitioning visual scenes into distinct objects and surfaces, which includes segmenting a figure from the background (figure- ground segregation) and segmenting multiple objects/surfaces from each other. Segmentation is a fundamental function of vision and is a gateway to perception, recognition and visually guided action. However, the neural underpinning of segmentation remains to be understood. A key question is to understand how the brain represents multiple visual stimuli such that information regarding individual stimuli can be extracted from the activity of populations of neurons. We address this question in the proposed project to elucidate the neural mechanisms underlying segmentation and the principles of coding sensory information in neuronal populations. Visual motion and depth provide potent cues for segmentation. Therefore we focus on understanding how the brain uses motion and depth cues to achieve segmentation. We have made substantial progress in defining how middle-temporal (MT) cortex, an area important for motion and depth processing, represents multiple overlapping visual stimuli. We found that MT neurons show various types of response biases toward one component of multiple stimuli, revealing a set of novel rules by which multiple stimuli interact within neurons? receptive fields. These physiological findings together with our preliminary data on natural scene statistics led us to hypothesize that the visual system exploits the statistical regularities in natural scenes that differentiate figure from the background and represents multiple visual stimuli efficiently to achieve segmentation. To test this overarching hypothesis, we will integrate the approaches of natural scene statistics, neurophysiology, and theoretical consideration of optimal coding. Specifically, we will characterize natural scene statistics of depth and motion pertinent to image segmentation, elucidate the functional roles of stereoscopic depth in figure-ground segregation, define the rules by which neurons in area MT represent multiple spatially-separated stimuli, which are commonly encountered in natural vision, and determine the signal transformation across multiple brain areas in the dorsal visual pathway to achieve segmentation. Finally, we will use an Information-Maximization approach to determine whether the neural representation of multiple visual stimuli is optimal for segmentation. The proposed study rigorously explores the interaction of multiple stimuli and is expected to provide important insight into how the visual system solves the challenging problem of segmentation in natural vision.