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): Spontaneous waves of electrical activity propagate across many structures of the central nervous system during critical stages of early development. It is now known that specialized pacemaker neurons are responsible for initiating these waves, but it is not clear how such pacemakers operate, or what properties determine some neurons to initiate the waves and other neurons to propagate the waves once they are initiated. A great deal of attention has been paid to how the synaptic interactions between neurons serve to initiate and propagate spontaneous waves. Very little is known, however, about how these immature neurons transform their synaptic inputs to spike train outputs, and how this computation is involved in wave initiation and propagation. Recent collaborative experiments of our two laboratories have used white noise current stimuli delivered to single neurons in the developing mouse cortex to try to understand how these neurons extract features from their synaptic inputs and compute their outputs. This work has shown that near the end of the first postnatal week, cortical neurons acquire the ability to scale their output function to the amplitude of their inputs, thus reducing the gain between input amplitude and spike frequency output. At late embryonic and early postnatal stages, however, many cortical neurons lack this gain scaling ability. These early stages correspond to those at which spontaneous waves of activity are generated in the cortex. Recent experiments in one of our laboratories have shown that cortical waves are driven by a pacemaker population in the ventrolateral quadrant of the cortex. We propose here to test the hypothesis that the pacemaker neurons in this region are the neurons that show the most pronounced lack of gain scaling, and that this inability to scale output to input amplitude effectively is one of the properties required for their pacemaking function. High-speed calcium imaging will be used to identify the location of the pacemaker in individual slices, and then whole-cell recordings and white noise stimuli will be applied to neurons in that region to measure their gain scaling ability. This will be compared to neurons in follower regions to see whether lack of gain scaling correlates with pacemaker function. These data will be combined with recordings of synaptic inputs in the pacemaker neurons to create neuronal models to test whether the inability to scale spike train outputs to synaptic input amplitudes is the computational property that determines pacemaker function. PUBLIC HEALTH RELEVANCE: Waves of electrical activity cross the brain during early development and are essential for the correct wiring of brain circuitry. The present experiments study how the properties of single neurons cause them to trigger this activity. The work will provide insights into defects in brain development that occur in humans, and in particular how abnormal electrical activity, such as seizures, can disrupt brain development.

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.

We propose an undergraduate and graduate (NRSA and non-NRSA) training program in computational neuroscience. Our campus has a rich history and an enonnous 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 courseworic 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 wiil take a common core cunicuium 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 can7 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 joumal 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 and present their research at an annual retreat. The program will be directed by Assoc. Prof. Adrienne Falrhall, Physiology and Biophysics, and a leadership team of Prof. Bill Moody, Professor of Biology, Director of the Undergraduate Neurobiology Program; Assoc. Prof. David Peri

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.

Reinforcement learning is a powerful learning strategy in which random perturbations are made to an output and changes that lead to more successful outputs are reinforced. Animals learn to perform highly skilled tasks by trial and error, but how biological neural networks generate variability from trial to trial is not known. An ideal model for examining this relationship between variability and learning can be found in song learning by birds. Birds learn their songs by trial and error, and are able to adjust their songs into adulthood. In addition, the basic neural architecture of the learning circuit has been mapped out; some effects of behavioral context on variability are also known. How variability is generated, and how it is controlled, can thus be studied mechanistically.

This project will study two candidate mechanisms for the generation and control of variability: chaotic dynamics in a cortex-like area, and dynamical modulation of synchrony in a basal ganglia circuit. While generic neural networks are chaotic, this project will explore how such dynamics are modified in a cortical network of biophysically plausible neurons with structured connectivity based on the known topography of the learning circuit. Furthermore, the birdsong cortical area receives inputs generated by basal ganglia. How might this input influence the variability of the output? It is proposed that the ability of activity in basal ganglia to affect cortex may depend on the synchrony of basal ganglia outputs. The studies will be constrained by experimental data from these candidate areas through collaborations with two laboratories.

The work will contribute to a general understanding of variability in biological learning, and may suggest strategies for structuring noisy input during motor learning that can lead to efficient outcomes for robotics applications. Further, Dr Fairhall is developing materials for broad outreach programs that include a Massive Online Open Course to introduce concepts in computational neuroscience to a very diverse audience, and a workshop for middle school girls interested in mathematics applied to the life sciences.

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.

? DESCRIPTION (provided by applicant): The University of Washington conducts world-class research in the development of big data analytics, as well as in many areas of biomedical research. However, most predoctoral students in biomedical science do not receive cutting-edge training in statistical and computational methods for big data. Furthermore, most predoctoral students in statistics and computing do not receive in-depth training in biomedical science. In short, the university currently lacks an integrated training program that spans computation, statistics, and biomedical science. Given the growing importance of big data across many areas of biomedical research, such an integrated program is critically needed. In order to train a new generation of researchers with expertise in statistics, computing, and biomedical science, we propose the University of Washington PhD Training in Big Data from Genomics and Neuroscience (BDGN). This program will focus on two areas of biomedical science, both of which are characterized by huge amounts of data as well as extensive expertise at the University of Washington: genomics and neuroscience. The program will draw six predoctoral students per year from the following seven PhD programs: Applied Mathematics, Biology, Biostatistics, Computer Science & Engineering, Genome Sciences, Neuroscience, and Statistics. Trainees will be appointed to the training grant during their ?rst or second year of hD studies and will continue on the training grant for two years. They will take a rigorous curriculum that involves three courses in statistics, machine learning, and data science, and three courses in either genomics or neuroscience. Each trainee will be paired with two world-class faculty mentors: one specializing in either genomics or neuroscience, and a second specializing in the development of either computational or statistical methods for big data. Other key features of the training program include three one-quarter rotations, with at least one focusing on genomics or neuroscience and one focusing on statistical or computational methods, a summer internship program, opportunities to attend world-class summer courses run through UW programs, peer mentoring, seminars, journal clubs, and courses on reproducible research and on responsible conduct of research. All predoctoral trainees will leave the BDGN Training Program with a core set of skills and a common language required for generating, interpreting, and developing statistical and computational methods for big data from genomics or neuroscience.

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.

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.

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.

Data Science Core Project Summary The Data Science Core will be responsible for coordinating data management and analysis strategies across the five projects. Based at the University of Washington, the Core will integrate data science and computational neuroscience expertise to adapt or develop data analysis methods that can be applied in a coordinated way across the different data sets and to simulation outputs. Along with characterizing neural representations, we will particularly focus on new and developing methods that can extract information from small numbers of trials during learning, and that elucidate the dynamical basis of the neural computation. This analysis will be used in order to test and further develop hypotheses. The Core will establish standards and practices for data and software generated by and used the projects that will facilitate collaboration and ultimately sharing of data, algorithms and results. To facilitate efficient computation on large amounts of heterogenous data, the Data Science Core will develop portable high-performance computing systems that can be deployed on institutional resources, as well as using cloud computing platforms.

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.

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.

SUMMARY/ABSTRACT ? Project 5. Modeling and Theory This collaborative project will develop integrative, multi-scale models that incorporate data from all Projects in the Berghia Brain Project to describe how sensory inputs are represented and influence behavior. It will provide data analysis and modeling frameworks in which to synthesize information gathered from connectomics analysis, behavioral analysis, and large-scale neural activity recordings. Analysis of network activity along with behavioral outputs will be reduced to hierarchical low-dimensional systems. Novel methods will be developed to analyze connectomic data enriched with additional features including molecular information from analysis of gene expression. Specifically, the Project aims to determine what processing is carried out by the peripheral nerve plexuses, how these may influence behavior directly, what are the computational mechanisms of this processing and how such processing interfaces with the central brain. The three co-investigators bring complementary expertise and will interact closely with one another and with the experimental groups toward the development of an integrated model and the identification of interpretative and explanatory computational algorithms.