Title: Dissemination of a tool for data-driven multiscale modeling of brain circuits. PI: S Dura-Bernal We are developing a novel software tool, called NetPyNE, that enables users to consolidate complex experimental data from different scales into a uni?ed computational model. Users are then be able to simulate and analyze this model to better understand brain structure, dynamics and function in a unique framework that combines: 1. programmatic or GUI-driven model building using ?exible, rule-based, high-level standardized speci?cations; 2. separation of model parameters from underlying technical implementations, preventing coding errors and making models easier to read, modify, share and reuse; 3. support for multiple scales from molecule to cell to network; 4. support for complex subcellular mechanisms, dendritic connectivity and stimulation patterns; 5. ef?cient parallel simulation both on stand-alone computers and supercomputers; 6. automated data analysis and visualization (e.g., connectivity, neural activity, information theoretic analysis); 7. importing and exporting to/from multiple standardized formats; 8. automated parameter tuning (molecule to network level) using grid search and evolutionary algorithms. NetPyNE's potential to bene?t the research community is evidenced by several peer-reviewed publications and by the steady growth of users and advocates. Over 50 researchers and students in our lab and collaborators' labs have used a prototype of the tool for education or to investigate a variety of brain regions and phenomena. There is an active online community who collaboratively contribute to the project, post questions and request features via the GitHub platform, a mailing list and two Q&A forums. The Organization for Computational Neuroscience included a 2-page feature article on NetPyNE in their 2019 Winter Newsletter. NetPyNE is also being integrated with other resources in the neuroscience community: Human Neocortical Neurosolver, Open Source Brain, Neuroscience Gateway, and the NeuroML and SONATA international standardized network formats. Our proposal is aimed at transforming NetPyNE into a solid and well-tested tool with a fully-featured GUI, and widely disseminating the tool among the scienti?c community. The rapid growth of the tool means many features have been added at a fast pace, with limited resources and time. We will now ensure all these features are properly evaluated for reliability, robustness and scalability, well documented and incorporated into the GUI. The GUI will also be extended to provide online web-based access and support visualization of larger models. We will also develop interactive online tutorials to clearly explain and demonstrate the ample and diverse functionality included in our package. Through a yearly multi-day course and tutorials/workshops at neuroscience conferences we will engage and train students, experimental and computational neuroscientists, and clinicians in using NetPyNE for multiscale neural modeling. Multiscale modeling complements experimentation by combining and making interpretable previously incommensurable datasets. Simulations and analyses developed with NetPyNE provide a way to better understand interactions across the brain scales, including molecular concentrations, cell biophysics, electrophysiology, neural dynamics, population oscillations, EEG/MEG signals, and information theoretic measures.
Innovative neurotechnologies are being developed to record data from more and more parts of the brain at different scales, measuring various molecules and ion channels, cell types and cell activity and activity in networks of cells and large areas of the brain. We are developing a new multiscale computer simulation tool, called NetPyNE, to allow users to much more easily combine and make sense of this information via a user-friendly language and graphical interface. We will transform this prototype into a stable, reliable tool and disseminate it to the neuroscience community so users can build, simulate and analyze complicated models of the brain, leading to more sophisticated theories of how the brain works.
