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.
Multiscale modeling of neurons and neuronal networks has become an important tool for neuroscience research that impacts our understanding of neural systems including neural pathologies. This project will have a large impact on the development of detailed models of neurons and networks by facilitating the exhange and reuse of these detailed models and their components.
|Vella, Michael; Cannon, Robert C; Crook, Sharon et al. (2014) libNeuroML and PyLEMS: using Python to combine procedural and declarative modeling approaches in computational neuroscience. Front Neuroinform 8:38|
|Cannon, Robert C; Gleeson, Padraig; Crook, Sharon et al. (2014) LEMS: a language for expressing complex biological models in concise and hierarchical form and its use in underpinning NeuroML 2. Front Neuroinform 8:79|