NetPyNE is a Python package to facilitate the development, simulation, parallelization, analysis, and optimization of multiscale biological neuronal networks using the NEURON simulator. Although NEURON already enables multiscale simulations ranging from the molecular to the network level, using NEURON for network simulations requires substantial programming, and often requires parallel simulations. NetPyNE greatly facilitates the development and parallel simulation of biological neuronal networks in NEURON for students and experimentalists. NetPyNE is also intended for experienced modelers, providing powerful features to incorporate complex anatomical and physiological data into models.
NetPyNE enables users to consolidate complex experimental data from different scales into a unified computational model. Users are then able to simulate and analyze this model to better understand brain structure, dynamics, and function in a unique framework that combines: 1) programmatic and/or GUI-driven model building using flexible, rule-based, high-level standardized specifications; 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) efficient parallel simulation both on stand-alone computers and supercomputers; 6) automated data analysis and visualization (e.g. connectivity, neural activity, information theoretic analyses); 7) importing and exporting to/from multiple standardized formats; 8) automated parameter tuning (molecule to network level) using grid search and evolutionary algorithms.
NetPyNE has been validated with a methods publication:
ModelDB, Open Source Brain, NeuroML, SONATA, NeuroMorpho