CRCNS Research Proposal: Collaborative Research: New dimensions of visual cortical organization

University Of California-San Francisco, San Francisco CA

Investigators

Abstract

The visual system of the mouse is now widely studied as a model for developmental neurobiology, as well as for the understanding of human disease, because it can be studied with the most powerful modern genetic and optical tools. This project aims to discover how neurons in the visual cortex of the mouse allow it to see well by measuring how the cortex represents ecologically-relevant properties of the visual world. Quantitative studies of neurons in the mouse's primary visual cortex to date reveal only very poor vision, but their behavior indicates that mice can see much better than that -- they avoid predators and catch crickets in the wild. To understand mouse vision, the investigators will study responses to novel, mathematically tractable stimuli resembling the flow of images across the retina as the mouse moves through a field of grass. Studies based on these new stimuli indicate that most V1 neurons respond reliably to fine details of the visual scene. A mathematical understanding of how the brain takes in the visual world should have real implications for how we see, and should have great benefits for artificial vision by computers and robots. Bringing these ideas into the classroom will provide the foundation for new technologies, and will expose students to both real and artificial vision systems. Analyses of the brain's visual function are limited by the stimuli used to probe them. Conventional quantitative approaches to understanding biological vision have been based on models with linear kernels in which only the output might be subject to a nonlinearity, all derived from responses of neurons in the brain to gratings of a range of spatial frequencies. This analysis fails to capture relevant features of natural images, which can not be constrained to linearity. The goal of this project is to probe the mouse visual system beyond the linear range but below the barrier posed by the complexity of arbitrary natural images. The investigators have identified an intermediate stimulus class--visual flow patterns--that formally approximate important features of natural visual scenes, resembling what an animal would see when running through grass. Flow patterns have a rich geometry that is mathematically tractable. This project will develop such stimuli and test them on awake-behaving mice, while recording the resultant neural activity in the visual cortex. Studying the mouse opens up the possibility of applying the entire range of powerful modern neuroscience tools-- genetic, optical, and electrophysiological. Visual responses will be analyzed using a novel variety of machine learning algorithms, which will allow the investigators to model the possible neural circuits and then test predictions from those model circuits. Such an understanding of the brain will inform both primate vision and the next generation of artificially-intelligent algorithms which, as a result, should benefit from being more "brain-like." This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.