Adrienne Fairhall - US grants

The outputs from neuronal systems adapt following a change in a stimulus, through a variety of mechanisms acting on different timescales. Many systems show a rapid form of adaptation through which stimuli are encoded relative to the context within which they were presented. This form of context-modulated coding is ubiquitous through sensory and higher-level systems, and may support the ability of the nervous system to efficiently encode complex natural stimuli. Experiments have shown that this form of gain control occurs both in neural systems and in single neurons in the cortex. On longer timescales, neural systems show a decrease of activity after long exposure to a constant stimulus. It is proposed that this behavior is a reflection of intrinsic neuronal dynamics that may contribute to the efficient coding of the slowly-varying stimulus envelope. This project will further develop the hypothesis that these slow dynamics may reflect the system's ongoing estimate of the changing statistical characteristics of the input. The mechanisms for these adaptive behaviors will be pursued with a combination of theoretical analysis and simulation of realistic neuronal models. The project's goal is to elucidate the mathematical basis of fundamental neural mechanisms for the efficient encoding of time-varying signals and reveal optimal strategies for sampling and representing complex natural environments. This work examines neural coding mechanisms that apply to multiple sensory and higher brain systems and will help to develop a language and methodology that can translate across these areas. The interdisciplinary nature of the work will provide training opportunities for undergraduate and graduate students crossing over from mathematics and physics to biology. The project includes the development of a summer course for advanced high school students.

DESCRIPTION (provided by applicant): We propose an undergraduate and graduate (NRSA and non-NRSA) training program in computational neuroscience. Our campus has a rich history and an enormous 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. Support for undergraduate and graduate education and research will foster the ongoing growth of this area, enhance interaction between theorists and experimentalists, expand and integrate coursework in quantitative approaches in neuroscience, enhance interactions between undergraduate and graduate students, enhance opportunities for undergraduate research and draw together the community across campus to strengthen our already excellent interdisciplinary exchange and collaboration. Our undergraduate training program will establish a two-year sequence in computational neuroscience, with entry points for up to 12 trainees yearly either from neurobiology or from a computational major (Physics, Computer Science and Engineering, Applied and Computational Mathematics). In fall quarter students attend a series of seminars to introduce them to faculty research. Trainees will take a common core curriculum including both laboratory neurobiology courses and quantitative courses, where the laboratory section is enhanced with a parallel computational course. Choice of additional electives in an individualized curriculum is guided by a mentoring committee. All students will complete at least 1 and preferably 4 quarters of mentored laboratory research. Our graduate training program will support up to 6 students joining either from the Neurobiology & Behavior interdepartmental program or from departmental graduate programs. Students will apply for training grant support at the end of the first year and carry out a core curriculum consisting of two neurobiology courses and two quantitative 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. A biweekly journal club will survey mathematical and systems neuroscience papers and allow student research presentations. Students will have teaching opportunities in new computational courses in the undergraduate Neurobiology program. All trainees will attend a monthly seminar and present their research at an annual retreat. The program will be directed by Assoc. Prof. Adrienne Fairhall, Physiology and Biophysics, and a leadership team of Prof. Bill Moody, Professor of Biology, Director of the Undergraduate Neurobiology Program; Assoc. Prof. David Perkel, Otolaryngology and Physiology and Biophysics; Prof. Fred Rieke, Physiology and Biophysics, former Director of the Physiology and Biophysics graduate program and Asst. Prof. Eric Shea-Brown, Applied Mathematics.

DESCRIPTION (provided by applicant): This project addresses the fundamental question: how does the history of sensory experience affect behavioral decision-making? We address this problem in the neuroethological context of mosquito host seeking, in which the presence of thermal and carbon dioxide signals trigger and direct search behavior. This project has several novel aspects. It will quantitatively explore this synergistic stimulus interaction in the context f a natural behavior and develop models for multisensory integration based both on behavioral and neural data. It will address the spatial variations in the statistical structure of olfactory and thermal stimuli through recordings and direct numerical simulations, and examine whether the universal properties of turbulently advected scalar fields can provide sensory evidence for source location and shape neural responses. We will obtain novel neural recordings in response to multiple time-varying inputs. By a developing tethered flight preparation for mosquitoes, we will be able to record neural activity during constrained flight and directly relate sensory neural responses to behavioral outcomes. The results from this project may help in the design of noninvasive mosquito repellents or attractants and so have an impact on disease transmission. The work may also have impact beyond insect physiology in the design of algorithms for novel sensors in the olfactory domain. Additional broader impacts from this project arise from educational and community engagement through interdisciplinary training of undergraduate and graduate students and postdoctoral fellows; active involvement of our research groups in the broader goals of integrated education and research experiences in the areas of Computational Neuroscience and Neural Engineering through new campus-wide initiatives; communication of our results to the community through a wide variety of social media; and participation in outreach activities to teachers and K-12 classrooms.

Neurons that are being formed in the brain of a developing animal send out electrical signals that spread like waves over large parts of the brain. The waves are essential for normal brain development. The goal of this project is to find out how this spontaneous electrical activity controls brain development. Recently, a specific type of neuron has been identified as being the pacemaker, or trigger, for these waves. This project will study how these neurons trigger the spontaneous waves. The project will take advantage of a mouse that allows these neurons to be stimulated using light. The responses of the neurons to these stimuli will be monitored, and the results will be used to make computer models of the neurons. These models will reveal the properties of these neurons that allow them to produce the waves. The project will offer opportunities for undergraduate and graduate students to be trained in interdisciplinary research that involves both theoretical and experimental biology, under the guidance of two collaborating principal investigators with unique expertise in these areas. Emphasis will be placed on actively recruiting women into full participation in the computational aspects of the project.

This project investigates how the intrinsic electrical properties of developing GABAergic interneurons in the cerebral cortex allow them to initiate spontaneous waves of electrical activity during early development. Previous genetic and pharmacological data indicate that these neurons, which are excitatory during early development, are the primary pacemakers for waves of spontaneous activity in the mouse cortex between embryonic day 18 and and postnatal day 3. The detailed input:output relations of GABAergic neurons will be determined by using calibrated optical stimulation in a dlx5/6 channel-rhodopsin mouse and by recording the outputs of the neurons with extracellular electrode arrays. Conductance-based models of the neurons will be constructed that reflect the diversity of intrinsic properties that are encountered in the population. The model neurons will be connected into synaptic circuits to determine whether the measured properties lead to pacemaking activity in the circuit. By systematically varying the intrinsic properties in the model, aspects of the intrinsic properties that are critical for pacemaking function will be determined. If successful, this project will provide a new high-throughput method for determining how the intrinsic properties of neurons determine circuit output in the brain.

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.

US-Spain Collaboration in Computational Neuroscience, Madrid, February 15-16, 2018.

This award supports a workshop, led by Adrienne Fairhall and Javier Mart?n Buld?, on US-Spain collaboration in computational neuroscience. The workshop builds on the interests of multiple NSF directorates and NIH institutes, and Spain's State Research Agency (AEI), in this rapidly developing area of research.

The workshop has been organized to explore the intellectual opportunities, broader impacts, and practical considerations needed for US-Spain collaboration to be successful, with researchers and government representatives from the US and Spain in attendance. A report from the workshop will be made available at http://www.nsf.gov/crcns.

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.

One way to decipher a complex biological problem, such as understanding how the brain works, is by using a simpler system that enables greater experimental or computational access. Hydra is a small, transparent relative of the jellyfish, and represents the first animals to have evolved a nervous system. Correspondingly, the nervous system of Hydra is very simple, with a few hundred neurons forming a net which tiles the body of the animal, without ganglia or brain. In spite of this simplicity, Hydra's nerve net generates a rich range of nimble behaviors, including contracting, elongating, bending, searching and somersaulting. Recently, the investigators of this project developed genetically altered Hydra strains in which the activity of neurons and muscles causes them to generate a light signal. Thus, the investigators can directly observe the activation of every neuron and muscle cells in an animal while it is behaving. Because of this, they can use statistical methods to analyze how neural activity drives movements. They discover basic principles of how simple nervous systems control muscles to produce behaviors. Given that Hydra has no brain, this project may reveal how complex movement can be organized without any central coordination. Further, Hydra has an extraordinary ability to regrow: its cells are constantly being replaced, and a complete Hydra body can reform from even very small pieces of the animal. Understanding how the nerve net of Hydra continues to produce stable behavior in the face of rapid turnover may advance understanding of how nervous systems can repair themselves. The study of Hydra with an integrated imaging/computational approach serves as an appealing platform for outreach opportunities. The research introduces members of the general public to neuroimaging and essential biology and mathematical neuroscience. It also provides training opportunities for researchers at all levels. The Hydra system is deeply integrated into summer courses at the Marine Biological Laboratory and provides cross-cutting projects for students from diverse backgrounds.

This project aims to decipher the relation between the activity of a nervous system, the muscles it controls and the behavior the muscles generate using the cnidarian Hydra. The investigators focus on decoding the neural basis of a few elementary behaviors that can be rigorously identified and that are generated by the endodermal and ectodermal nerve nets. The investigators use calcium imaging of every neuron and every muscle cell in mounted Hydra preparations during contractile behaviors. To analyze the required data sets, the investigators develop algorithms to track cells in the moving, deforming animal and apply dimensionality reduction methods to discover spatiotemporal patterns of movement corresponding to muscle activation patterns. The end product is a quantitative model that explains how contractile behaviors are generated. As another deliverable, the techniques developed to track neurons and discover spatiotemporal patterns are made widely available in an open source platform and may be of use in other systems. This proposed work will help establish Hydra as a model neural system for which a complete accounting of neural activity and behavior may be rigorously approached.

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.

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.

This catalytic network of networks will spur advances in brain-inspired computation and fundamental theories of neuroscience, particularly with regard to biophysics and connectivity, the role of neuromodulation in creating rich network dynamics, and analytical methods to characterize and control these dynamics. The project combines expertise of researchers gathering brain data from animals engaged in complex tasks, theorists who make models of cognition, researchers who use machine learning to develop new ways of analyzing data, and experts in artificial intelligence. The network of networks will develop a student cohort with the ability to synthesize findings from new and sophisticated data analysis into novel algorithms for computation, and, vice versa, to translate findings from engineering of computational algorithms into hypotheses for brain function. International experience will enhance research opportunities for undergraduate and graduate students. The ethical emphasis of the network will develop a cohort with substantive experience and training in the application of neuroethics and ethical engineering practices.

The Accelerating Research through International Network-to-Network Collaborations (AccelNet) program is designed to accelerate the process of scientific discovery and prepare the next generation of U.S. researchers for multiteam international collaborations. The AccelNet program supports strategic linkages among U.S. research networks and complementary networks abroad that will leverage research and educational resources to tackle grand scientific challenges that require significant coordinated international efforts.

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.