
Daniel A. Butts - US grants
Affiliations: | Biology | University of Maryland, College Park, College Park, MD |
Area:
Information theory, coding, estimation, visionWebsite:
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The funding information displayed below comes from the NIH Research Portfolio Online Reporting Tools and the NSF Award Database.The grant data on this page is limited to grants awarded in the United States and is thus partial. It can nonetheless be used to understand how funding patterns influence mentorship networks and vice-versa, which has deep implications on how research is done.
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High-probability grants
According to our matching algorithm, Daniel A. Butts is the likely recipient of the following grants.Years | Recipients | Code | Title / Keywords | Matching score |
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2010 — 2014 | Butts, Daniel | N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Characterizing Cortical Computation in the Context of Natural Vision @ University of Maryland College Park An understanding of how the brain processes visual stimuli is confounded by the complexity of the natural visual world, combined with the intricate neuronal processing that occurs within and across multiple cortical areas. Over the last few decades, substantial progress has been made by using simple laboratory stimuli, such as moving bars or dots, to develop simple descriptions of neuronal tuning to these elements. This approach has provided a reasonable functional description of lower cortical areas, but it is unlikely to be sufficient to characterize the regions of the extrastriate cortex whose responses are thought to represent elements particular to the natural visual world. Though a joint Canadian-US collaboration, this project couples new experimental approaches based on a set of complex stimuli approaching natural vision with appropriately complex models, in order to understand how neurons in successive stages of cortical processing are tuned to more natural visual features. |
0.915 |
2014 — 2019 | Butts, Daniel | N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Career: Network Modulation of Cortical Neuron Computation @ University of Maryland College Park The function of sensory neurons is typically defined by the relationship between sensory stimuli and their responses; however, in the cortex of awake animals, sensory responses account for only a fraction of neural activity. While activity not driven by the stimulus is often considered "noise" and neglected in experiments, such ongoing cortical activity has been linked to a number of processes related to cognition, and can be influenced by attention, tasks, and perception itself. It remains unclear how to relate such ongoing cortical activity to the processing of sensory stimuli, and more generally why it appears to play such a prominent role in sensory neuron function. |
0.915 |
2016 — 2017 | Butts, Daniel A | R21Activity Code Description: To encourage the development of new research activities in categorical program areas. (Support generally is restricted in level of support and in time.) |
Functional Specialization of Foveal Visual Cortex @ Univ of Maryland, College Park PROJECT SUMMARY Despite decades of research studying primate primary visual cortex (V1), remarkably little is known about cortical processing in the region representing the central few degrees of the visual field: the fovea. This is particularly notable given that a disproportionately large fraction of V1 is dedicated to foveal processing, and the ?central? role of the fovea in both human visual perception and behavior. Studies of foveal V1 function have been greatly limited by the inability to track an animal's eye position with sufficient accuracy to reconstruct the position of the visual stimulus on the retina. We have recently developed a model-based eye tracking method to address this problem, which can continuously infer eye position with sufficient accuracy to study foveal V1 processing. We thus propose to perform the first detailed studies of stimulus processing in foveal V1. Simply put, it is not known whether the enormous body of work studying V1 outside the fovea is applicable to foveal V1, and whether there are critical elements of cortical function that only might be observed in the fovea. Due to the different dynamics of visual input to the fovea that are driven by eye movements, the different composition of retinal inputs, and the distinct functional roles the fovea plays in visual perception, we expect to identify fundamental differences in foveal V1 function. We will perform multi-electrode recordings across eccentricity in awake macaque, and target analyses to three aspects of visual processing expected to be different: (1) tuning for spatiotemporal elements of vision; (2) tuning to binocular disparity; and (3) color processing. We expect that this proposed study will reveal substantial differences in the stimulus processing of foveal neurons, potentially transforming our current understanding of visual processing in the cortex. Health Relevance. Understanding the unique functional role of foveal V1 in visual processing will provide critical insight into how human visual perception is constructed, and will likewise inform our understanding of the effects of many disorders that impact central vision. For example, age-related macular degeneration (AMD) is a leading cause of vision loss of Americans 60 and older. Patients with advanced AMD retain their peripheral vision, but lose vision in the fovea, resulting in severe difficulties with a wide range of important tasks requiring high-acuity vision, including reading, writing, driving, and object/facial recognition. Likewise, a range of disorders such as nystagmus can disrupt normal eye movements and fixations, thereby disproportionally affecting foveal vision. A better understanding of neural function subserving foveal vision could yield insights into their effects on visual perception and potentially facilitate the development of novel therapeutic strategies. |
0.987 |
2016 — 2021 | Butts, Daniel Girvan, Michelle [⬀] Fagan, William (co-PI) [⬀] Varshney, Amitabh (co-PI) [⬀] Corrada Bravo, Hector |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Nrt-Dese: Network Biology: From Data to Information to Insights @ University of Maryland College Park An urgent issue facing today's researchers in the life sciences is coping with the data explosion resulting from the advent of powerful new technologies. More data does not yield better information without the interdisciplinary tools required for such a transformation. This National Science Foundation Research Traineeship (NRT) award to the University of Maryland, College Park will build an innovative, cross-disciplinary model for graduate education that addresses this challenge by preparing students to pursue a range of STEM careers at the nexus of the computer, physical, and life sciences. Trainees will learn to combine physics-style quantitative modeling with data processing, analysis, and visualization methods from computer science to gain deeper insights into the principles governing living systems. The project anticipates training approximately sixty (60) PhD students, including thirty-five (35) funded trainees, from the physical, computer, and life sciences. |
0.915 |
2021 — 2023 | Butts, Daniel | N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Collaborative Research: Ncs-Fo: Active Vision During Natural Behavior: More Than Meets the Eye? @ University of Maryland, College Park Vision is a process by which the image falling on the eyes is processed by specialized neurons within visual brain areas. Neurons in the early stages of visual processing convey information about small bits of the visual scene, like pixel-detectors in a camera. For example, a neuron in visual cortex might respond best to a small white bar at a particular location in visual space. Should this example neuron respond differently when the white bar is part of an object that we have seen before, or one that we are moving towards? Psychology might suggest so, but for almost 60 years, most scientists studying the neural basis of visual perception have implicitly assumed that responses of neurons in visual cortex depend only on the visual image falling on the eyes. It is increasingly clear that neurons in the visual cortex do indeed care about behavioral context – as well as the state of the brain itself. These external, internal, and contextual factors influence how neurons process the visual scene. Exactly how much these “non-visual” factors influence visual cortical neurons remains a significant open question that this project aims to address. |
0.915 |
2021 — 2024 | Butts, Daniel | N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
@ University of Maryland, College Park Color and form are often treated as separable features of an image. One can recognize shapes in achromatic photographs and conceptualize the color of an object abstracted from shape. Yet color-specific processing is embedded throughout the visual pathway from the first stage of the visual pathway, where three different types of light sensors (“cones”) with sensitivity to different parts of the visual spectrum initially convert light into electrical impulses. The color of a given point can in principle be determined by comparing the activation of the three different cone types, but the separate color channels are maintained until the primary visual cortex (V1), where they are finally combined in neurons that concurrently have sensitivity to different spatial patterns. Indeed, while it was initially thought that color and form were processed through separate pathways within V1, recent experiments have highlighted that a surprising fraction of V1 neurons mix them together in a diversity of ways. Exactly how the mixing occurs, and for what purpose, are critical open questions in understanding human vision, and have been difficult to answer because such mixing is too complicated to characterize using traditional approaches. This project combines large-scale recording of V1 neural activity during tailored “spatio-chromatic” visual stimulation with new computational approaches that offer an unprecedented high-resolution description of color processing within V1 while allowing determination of the underlying function of spatio-chromatic mixing in supporting natural color vision. The project also provides opportunity for cross-disciplinary training in neurophysiological and machine-learning based statistical modeling of undergraduate and graduate students. |
0.915 |