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.