Eric Shea-Brown, Ph.D. - US grants
Affiliations: | Applied Mathematics | University of Washington, Seattle, Seattle, WA |
Area:
computational neuroscienceWebsite:
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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, Eric Shea-Brown is the likely recipient of the following grants.Years | Recipients | Code | Title / Keywords | Matching score |
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2004 — 2008 | Shea-Brown, Eric | N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Postdoctoral Research Fellowship @ Shea-Brown, Eric T Postdoctoral Research Fellowship |
0.906 |
2008 — 2012 | Shea-Brown, Eric | N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Collaborative Research: Correlations in Neural Dynamics and Coding @ University of Washington Multi-electrode recording technology and voltage sensitive dyes allow researchers to probe the structure of correlated, stimulus-driven neural activity in groups of cells. However, the diversity of brain areas and stimuli make a complete sampling of these patterns and their effects impossible. Furthermore, the evidence of correlations in stimulus response is strong, yet its role in neural coding difficult to intuit. Therefore, a combined, predictive theory of correlation formation and impact is required. This challenge is approached in three stages. First, a general mathematical theory is developed that relates input correlations of a stochastic forcing to the output correlations of resultant spike trains. The underlying tools will be linear response, population density, and Monte-Carlo methods for the nonlinear stochastic differential equations of spiking neural circuits. Next, this theory is applied to a variety of neural models to quantify how neuron biophysics, morphology, and coupling influence input-output correlation transfer. Finally, information-theoretic analyses are performed to estimate the impact of spike train correlations on the encoding and propagation of sensory inputs in representative neural circuits. Throughout the project, the investigators will work with experimental collaborators to refine and test these predictions. |
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2011 — 2015 | Shea-Brown, Eric | N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
@ University of Washington New recording methods allow researchers to probe the structure of neural activity with unprecedented scope and detail. As a result there is an explosion of interest in understanding the patterns of activity that emerge in entire neuronal populations and relating these patterns to the function of the nervous system. However, the overwhelming range of different sensory inputs that these populations receive -- and the vast range of different responses that these inputs evoke -- make it impossible to achieve this goal based on empirical observations alone. This challenge is compounded due to the nonlinearity of neuronal network dynamics, which makes it difficult to predict patterns of activity by extrapolation from observations of simpler systems. Predictive mathematical modeling and a deeper understanding of the dynamics of neuronal circuits is therefore required. With previous NSF support, the investigators developed numerical and analytic tools at the interface of statistics, stochastic analysis and nonlinear dynamics, to understand the genesis and impact of correlations in simple, but fundamental microcircuits. They build on these results by extending the underlying mathematical theory to more complex and realistic networks. Using this approach, the team of researchers examines how collective activity is controlled by network architecture, cell dynamics, and stimulus drive in a set of neural networks that typify structures across the nervous system. |
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2011 — 2017 | Shea-Brown, Eric | N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
@ University of Washington Questions of neural synchrony -- correlations in cell-to-cell spiking -- have |
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2012 — 2017 | Shea-Brown, Eric Rieke, Frederick |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Crcns: Collective Coding in Retinal Circuits @ University of Washington Circuits across the brain display correlated activity, in which the responses among neurons are coordinated at a level beyond that expected from the stimulus structure itself. What role does such correlated activity play in neural coding? The retina provides a strong opportunity for progress: widespread correlated activity is generated by key circuit mechanisms that recur throughout the brain. Moreover, the impact of these mechanisms can be studied in the context of stimuli with clear functional significance. Nevertheless, circuit-level nonlinearities at multiple locations cause retinal outputs to depend on stimuli in a manner that defies traditional filter-based models. This demands a tightly coupled computational and experimental approach: purely computational attacks will become lost in the combinatorics of all possible circuit configurations at the difficult "mesoscales" relevant to retinal computation, where averaging approaches fail; purely experimental strategies cannot predict and prioritize the most critical circuit mechanisms and stimulus parameters to explore. The investigators apply this interdisciplinary approach to the mechanisms producing correlated activity in directionally-selective (DS) ganglion cells. In particular, they determine how convergent and divergent pathways in the DS cell circuit interact to shape correlations and encoded information across the cell population, and asses the possible roles of recurrent coupling in modulating this process. These circuit features are the basis of correlated activity in many retinal pathways, and in circuits throughout the brain. Thus, our findings will guide studies of collective neural computation and dynamics currently under intense study in a variety of domains. |
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2015 — 2018 | Shea-Brown, Eric | N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
@ University of Washington Our brains are constantly changing. Experiences and memories leave their imprints on connections between neurons. Understanding this process is fundamental to understanding how the brain works. While this question has been of central importance to neuroscience for decades, at this moment researchers are well positioned to make significant progress -- new recording devices and imaging techniques are revealing the activity and changes within the networks of the brain at unprecedented scale and resolution. Sound mathematical models are essential to keep up with the mounting avalanche of data. The goal of this project is to develop mathematical tools to assist with improving understanding how networks of neurons are shaped by experiences. Developing this theory is crucial for understanding learning, as well as associated disorders. The project will focus on how learning improves the brain's ability to make decisions and store memories. Graduate students and postdocs joining this project will be part of an established, interdisciplinary mathematics research community. Trainees will gain a wide perspective of mathematical neuroscience through integrated research at three institutions, including extensive visits among them. |
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2016 — 2020 | Fairhall, Adrienne L [⬀] Shea-Brown, Eric Todd |
R90Activity Code Description: To support comprehensive interdisciplinary research training programs at the undergraduate, predoctoral and/or postdoctoral levels, by capitalizing on the infrastructure of existing multidisciplinary and interdisciplinary research programs. This Activity Code is for trainees who do not meet the qualifications for NRSA authority. |
Training Program in Neural Computation and Engineering @ University of Washington Summary This proposal continues and evolves an undergraduate and graduate (NRSA and non-NRSA) Training Program in Neural Computation and Engineering. The University of Washington has a rich history and a large and growing breadth of active teaching and research in this area, with faculty mentors distributed through many departments and schools, including Physiology and Biophysics, Biological Structure, Computer Science and Engineering, Applied Math, Biology, Psychology and Bioengineering. This program evolves the extremely successful previous five-year program which saw the development of an active and highly visible training program, including new undergraduate and graduate program, website, community activities, to take advantage of new opportunities and momentum in Seattle. Support for undergraduate and graduate education and research will enhance interaction between theorists and experimentalists; expand and integrate coursework in emerging approaches in neuroscience, particularly novel offerings in neuroengineering and big data; enhance interactions between undergraduate and graduate students; provide opportunities for undergraduate research and draw together the community across campus to strengthen our already excellent interdisciplinary exchange and collaboration. The undergraduate training program is a 2-year sequence in computational neuroscience, with support for 6 trainees yearly from neurobiology or from a computational/engineering major (Physics, Computer Science and Engineering, Bioengineering, Applied and Computational Mathematics). Trainees take a core curriculum including a research seminar, a choice of laboratory neurobiology sequence and common quantitative courses. Choice of additional electives in an individualized curriculum and career development is guided by a mentoring committee. All students will complete at least 1 and preferably 4 quarters of mentored laboratory research. The graduate training program will support up to 6 students from multiple graduate programs. Students will apply for training grant support at the end of the first year and carry out a core curriculum consisting of neurobiology, quantitative and journal club courses. Individually tailored curricula including electives selected from offerings in computational neuroscience, mathematics, computer science and physics will be devised in consultation with a mentoring committee. Trainees will have access to the UW/Allen Institute Summer Workshop for the Dynamic Brain on San Juan Island. All trainees will attend a regular seminar and and present their research at an annual retreat. The program will be co-directed by Profs. Adrienne Fairhall, Physiology and Biophysics and Eric Shea-Brown, Applied Mathematics, assisted by Leadership Team Prof. Bill Moody, Director, Undergraduate Neurobiology Program and Prof. David Perkel, Director, Neuroscience Graduate Program. |
0.958 |
2016 — 2020 | Fairhall, Adrienne L [⬀] Shea-Brown, Eric Todd |
T90Activity Code Description: To support comprehensive interdisciplinary research training programs at the undergraduate, predoctoral and/or postdoctoral levels, by capitalizing on the infrastructure of existing multidisciplinary and interdisciplinary research programs. |
Undergraduate and Graduate Training in Neural Computation and Engineering @ University of Washington Summary This proposal continues and evolves an undergraduate and graduate (NRSA and non-NRSA) Training Program in Neural Computation and Engineering. The University of Washington has a rich history and a large and growing breadth of active teaching and research in this area, with faculty mentors distributed through many departments and schools, including Physiology and Biophysics, Biological Structure, Computer Science and Engineering, Applied Math, Biology, Psychology and Bioengineering. This program evolves the extremely successful previous five-year program which saw the development of an active and highly visible training program, including new undergraduate and graduate program, website, community activities, to take advantage of new opportunities and momentum in Seattle. Support for undergraduate and graduate education and research will enhance interaction between theorists and experimentalists; expand and integrate coursework in emerging approaches in neuroscience, particularly novel offerings in neuroengineering and big data; enhance interactions between undergraduate and graduate students; provide opportunities for undergraduate research and draw together the community across campus to strengthen our already excellent interdisciplinary exchange and collaboration. The undergraduate training program is a 2-year sequence in computational neuroscience, with support for 6 trainees yearly from neurobiology or from a computational/engineering major (Physics, Computer Science and Engineering, Bioengineering, Applied and Computational Mathematics). Trainees take a core curriculum including a research seminar, a choice of laboratory neurobiology sequence and common quantitative courses. Choice of additional electives in an individualized curriculum and career development is guided by a mentoring committee. All students will complete at least 1 and preferably 4 quarters of mentored laboratory research. The graduate training program will support up to 6 students from multiple graduate programs. Students will apply for training grant support at the end of the first year and carry out a core curriculum consisting of neurobiology, quantitative and journal club courses. Individually tailored curricula including electives selected from offerings in computational neuroscience, mathematics, computer science and physics will be devised in consultation with a mentoring committee. Trainees will have access to the UW/Allen Institute Summer Workshop for the Dynamic Brain on San Juan Island. All trainees will attend a regular seminar and and present their research at an annual retreat. The program will be co-directed by Profs. Adrienne Fairhall, Physiology and Biophysics and Eric Shea-Brown, Applied Mathematics, assisted by Leadership Team Prof. Bill Moody, Director, Undergraduate Neurobiology Program and Prof. David Perkel, Director, Neuroscience Graduate Program. |
0.958 |
2020 — 2024 | Shea-Brown, Eric Steinmetz, Nicholas (co-PI) [⬀] |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Ncs-Fo: Variability and the Global Brain @ University of Washington While the brain's computational abilities are in many respects unrivaled, its workings appear to be far noisier than those of almost any engineered computational system. Even in carefully controlled experiments in which the same conditions are presented over multiple trials, neural activity is strikingly variable from one trial to the next. This project aims to resolve the apparent contradiction between the brain's computational proficiency and its apparently high levels of noise. The core hypothesis is that much of the observed neural variability is driven not by noise but by internal brain modes -- that is, by coordinated patterns of activity across the brain. Thus, variability may be a signature of dynamic and uniquely biological computations rather than noisy fluctuations. If this hypothesis is correct, then observation of these global modes should explain choices that subjects make in behavioral tasks, and perturbation of the modes should alter their patterns of choices in systematic ways. To test this hypothesis, the project employs a novel joint experimental and theoretical approach to measure the variability in brain-wide neural activity across scales, to define its relationship to behavior, and to dynamically perturb these modes to impact behavioral performance both within and across individuals. |
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2021 — 2023 | Shea-Brown, Eric Fairhall, Adrienne [⬀] |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Accelnet: International Network For Brain-Inspired Computation @ University of Washington Brains have intricate complexity at many scales, from the detailed structure and connections of neurons to the brain-wide swirl of electrical activity that underlies thought and experience. Making sense of these complexities and using new understanding to design improved computational algorithms requires new combinations of research expertise. This AccelNet project builds international links between academic, private research and industrial partners and prepares the next generation of researchers at the interface between neuroscience and artificial intelligence. The project connects leading centers for academic and industry research in the Pacific Northwest, Paris, France, and Montreal, Canada, to advance understanding of brain structure and dynamics and to use these insights to develop more powerful and efficient computing frameworks that can help mankind to solve the very challenging issues now confronting us. |
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