Sharon Crook - US grants

interneurons; neural information processing; ethology; computer simulation; electrophysiology; Orthoptera;

A fundamental question in neuroscience is how natural sensory stimuli are encoded for information handling by the brain. Invertebrate animals often offer systems that are in some ways simpler than those of mammals, and including such features as identifiable single cells in networks of relatively few numbers. This collaborative project exploits a sensory system called the cercal system of the cricket, in which small appendages on the rear of the body contain fine hairs that are used to detect, identify and localize behaviorally relevant air current movements, such as those produced by a predator. The input from roughly 2000 receptor cells converges on 30 local interneurons and only 20 output interneurons that lead to behavior such as escape. Three collaborators at two institutions use computational and mathematical analyses of a database of anatomical and physiological measurements on the 'dynamic map' that does the central processing in the brain of the peripheral signals. The goals are to characterize the representation of dynamic sensory stimulus parameters at two processing stages within the mapped sensory system, and to examine the mechanisms that transform the representation at the interface between these two processing stages.
Results will be important for our understanding of information representation in nervous systems, particularly in dynamic processing. The project also will enhance the independent career of a woman faculty member in mathematics, and students will receive multi-disciplinary, highly quantitative training related to biology, in two states that do not currently have high profiles in federally funded research.

Understanding an adaptive and learning computing system such as the brain requires an understanding of the computations performed by its basic components individual neurons. At the single neuron level, the dendritic tree provides the means for interpreting spatiotemporal activity patterns of synaptic input. However, no comprehensive framework exists for translating dendritic features into a functional architecture that can be related directly to behavior. Our goal is to develop rules for how dendritic structure and synapse distributions follow functional architecture principles that relate directly to a neuron's individual behavioral requirements. In holometabolous insects, like the moth Manduca sexta, the structure, physiology and function of individually identifiable neurons are modified in parallel during metamorphosis, allowing for studies that directly relate structure and behavioral function. This remodeling has been described in particular detail for the motoneuron MN5, which transforms from a tonically firing larval crawling motoneuron into a phasically firing adult flight motoneuron. In this collaborative research effort, Dr. Sharon Crook and Dr. Carsten Duch will combine experimental and computational approaches to investigate the function of the structural and synaptic remodeling of MN5 for its changing behavioral role. We will also test Cajal's Neuron Doctrine, which envisions the neuron as the smallest functional entity, by examining the computational and functional independence of dendritic sub-domains. Another important aspect of our modeling studies is that we will investigate the roles of excitatory/inhibitory input synapse ratios and distributions in single neuron computation. Excitatory/inhibitory imbalances are known to contribute to several neurological diseases. The use of Manduca provides a unique model system where one neuron has completely different geometries, each of which is associated with dramatically different firing outputs. The results will contribute significantly to our understanding of single neuron computation, which is an important step towards understanding neural networks.

The retina is part of the central nervous system and an ideal region to study information processing in the brain. It is accessible, well documented, and studied by researchers spanning the clinical, experimental, and theoretical sciences. Image processing in the retina begins in the outer plexiform layer, where bipolar, horizontal, and photoreceptor cells interact. The goal of this research is to mathematically model, in detail, the subcircuits of the outer plexiform layer, capturing spatio-temporal dynamics on two spatial scales: the scale of an individual synapse and the scale of the receptive field. The availability of electrophysiological, anatomical, and molecular biological data provides a rare opportunity to develop a complex multiscale model for the system. Mathematical modeling will be used to discover the functional impact of various circuitry elements, much the way an experimentalist does with selective pharmacological agents, except that not all the agents that are needed actually exist. A primary objective is to explore two competing hypotheses for explaining synaptic feedback effects that are observed experimentally in the outer plexiform layer of the retina. Specifically, the project will investigate if feedback effects in the cone photoreceptor's synapse are driven by electrical or chemical mechanisms, or both. New insights into the understanding of retinal circuitry from this project will impact research in health and medicine, including biomedical engineering, image processing, visual psychophysics, and pharmacology. The modeling and computational techniques resulting from this project will provide efficient and innovative approaches for other problem areas in applied mathematics and engineering.

Award Abstract:

The complexity of problems in computational neuroscience requires research from multiple groups across many disciplines to be combined. In order to combine research from multiple groups, there must be an infrastructure for exchanging model specifications; however, the current use of multiple formats for encoding model information has hampered model exchange. NeuroML is a model description language developed in XML (extensible Markup Language) that was created to facilitate data archiving, data and model exchange, database creation, and model publication in computational neuroscience. One of the goals of the NeuroML project is to develop standards for model specifications that will allow for greater simulator interoperability and model exchange.

An international workshop will be held in March of 2009 to bring together members of the computational neuroscience community for further development of the NeuroML specifications. The workshop participants will include modelers, software developers and experimentalists with the goal of refining the NeuroML standards for single cell modeling including the modeling of channels and the biophysical description of cells. Workshop outcomes will include a list of updates to the standards for the ChannelML and Biophysics specifications, an agreement from simulator developers for future support of cell and channel descriptions including optimization of simulators for large-scale simulations, and a publicly available summary of the meeting proceedings.

DESCRIPTION (provided by applicant): The complexity of problems associated with structure and function in neuroscience requires that research from multiple groups across many disciplines be combined. In order to combine research from multiple groups, there must be an infrastructure for exchanging model specifications;however, the current use of multiple formats for encoding model information has hampered model exchange. NeuroML is an eXtensible Markup Language (XML) specification that was created to facilitate data archiving, data and model exchange, database creation, and model publication in the neurosciences. One of the goals of the NeuroML project is to develop standards for model specification that will allow for greater simulator interoperability and model exchange. The declarative specifications produced by the NeuroML standards project are arranged into levels, with higher levels adding extra concepts. Level 1 deals with neuroanatomical information and metadata. Level 2 allows for specification of cell models with realistic channel and synaptic mechanisms distributed on their membranes, and Level 3 describes networks of these cells in three dimensions. The specific aims of the proposed neuroinformatic activities are as follows: 1) the NeuroML standards will be extended to accommodate models of biomolecular interactions and will be linked to the Systems Biology Markup Language (SBML) in order to create an XML specification for multiscale models in neuroscience, 2) all levels of the NeuroML standards and associated tools will be extended and linked to other neuroinformatic efforts, and 3) within the context of both published studies and the PI's current research collaborations, modeling studies will be used to generate examples of the use of NeuroML and to test the schema design, tools, and compatibility with GENESIS, NEURON, and other appropriate model simulation platforms. PUBLIC HEALTH RELEVANCE: Building working models of neurons and neural circuits is one of the best ways of furthering our understanding of how the nervous system works and what goes wrong following neural trauma or disease. The investigators propose to continue the development of a markup language specification for computational neuroscience, NeuroML, which will serve as a common data format for many applications including those necessary for simulating models. They will also continue their development of tools for validation of NeuroML documents, for document exchange, for import/export of model specifications in NeuroML, and for conversion of submitted NeuroML document files into simulator scripts or HTML format.

DESCRIPTION (provided by applicant): Intellectual Merit: The complexity of problems associated with structure and function in neuroscience requires that research from multiple groups across many disciplines be combined. In order to combine research from multiple groups, there must be an infrastructure for sharing data and exchanging models;however, the current use of multiple formats for encoding model information in the computational neuroscience community has hampered model exchange. The PI has collaborated extensively on the Neural Open Markup Language project, NeuroML, which is an international, collaborative initiative to develop a structured, declarative language for describing complex neuronal and neuronal network models. The goals of the project are to create a simulator-independent description language that facilitates data archiving, data and model exchange, database creation, and model publication. This collaborative initiative focuses on the key objects that need to be exchanged among existing software applications, such as descriptions of neuronal morphology, ion channels, synaptic mechanisms, and network structure. This modular approach brings additional benefits: not only can entire models be published and exchanged, but each individual object--such as a specific potassium channel or excitatory synapse--can be shared and re-implemented in a different model. The openness of the standards and the encouragement of feedback from many sections of the community are some of the guiding principles of the NeuroML initiative. The use of XML as a definition language provides the transparency, portability and extensibility required in these efforts, and also brings an infrastructure of established tools for efficient software and database development. The activities described in this proposal will take advantage of this infrastructure to provide a stream-lined set of tools for the computational neuroscience community to share, find, view, and test NeuroML models and their components. The specific aims of the proposed data sharing activities are to (1) develop and populate a XMLbased database system for multiscale models in neuroscience that are described using NeuroML, (2) integrate the web-based interface for the database with other NeuroML tools, including a LEMS-based model viewer, and (3) create user-friendly documentation and tutorials to ensure that the database will be useful for research and education. Broader Impacts: The database system proposed here is complementary to other existing model database efforts. In contrast to these other efforts, the modular design of NeuroML and use of XML provides for efficient searching and makes it much easier for a researcher to choose components from different models to combine for re-use. The development of a stream-lined tool chain for finding, viewing and testing these complex models and model components and the ease of re-implementing them on a different simulator could have a large impact on the field of computational neuroscience and also makes these complex models accessible for educational purposes. The PI and co-PI are both heavily committed to interdisciplinary teaching, bringing computer science and computational concepts into other areas of the curriculum. Drs. Crook and Dietrich also both work in areas where women are underrepresented and have demonstrated a commitment to serve as role models and mentors for other underrepresented groups in these fields. Dr. Crook has been involved with many training programs that target minority access to research. Dr. Crook works with underrepresented undergraduate students through programs funded by other mechanisms at ASU, and several of these students will have the opportunity to work on this project as part of those programs.

The PIs and Co-PIs of grants supported through the NSF-NIH-ANR-BMBF-BSF Collaborative Research in Computational Neuroscience (CRCNS) program meet annually. This tenth meeting of CRCNS investigators brings together a broad spectrum of computational neuroscience researchers supported by the program, and includes poster presentations, talks, plenary lectures, and workshops. The meeting is scheduled for October 16-18, 2014 and is hosted by Arizona State University.

? DESCRIPTION (provided by applicant): Building computational models is one of the best ways of furthering our understanding of both the normal function of the nervous system and the pathology associated with aging, neural trauma, or disease. Biophysically detailed models provide a framework for integrating data across spatial scales and for exploring hypotheses about the biological mechanisms underlying neuronal and network dynamics. However, as models increase in complexity, additional barriers emerge to the creation, validation, exchange and re-use of models. The NeuroML project aims to address these issues by providing a standard format for describing multiscale models in neuroscience. NeuroML is supported by over 30 tools and databases and is the basis for model exchange at Open Source Brain, where 374 users are collaborating on 57 public modeling projects. In spite of this promising movement toward model sharing in the neuroscience community, it is extremely rare to see a specific, rigorous statement of the criteria used for evaluating models during model development, and multiple models for the same ion channels and neurons are not compared for concordance with the same suite of experimental data. The overall goal of this project is to create a flexible infrastructure for assessing the scope and quality of computational models in neuroscience and to make this information broadly available to the community for a large class of models. Aim 1 focuses on enhancing existing tools to work together seamlessly for validation of NeuroML models against experimental data. Aim 2 concentrates on the development of a dedicated web portal, incorporation of automated model validation into existing model sharing platforms, and the creation of documentation, tutorials, forums and other outreach for promoting uptake and obtaining user feedback. Aim 3 includes testing of the validation tool chain in multiple large-scale neuronal network modeling environments. The proposed activities will build bridges that connect multiple, existing initiatives in support of model development, validation, exchange, selection, and re-use, and will integrate experimental data with modeling efforts for more efficiency, better transparency, and greater impact of computational models in neuroscience research.