Abstract
The increasing availability of computational resources is enabling more detailed, realistic modeling in computational neuroscience, resulting in a shift toward more heterogeneous models of neuronal circuits, and employment of complex experimental protocols. This poses a challenge for existing tool chains, as the set of tools involved in a typical modeler's workflow is expanding concomitantly, with growing complexity in the metadata flowing between them. For many parts of the workflow, a range of tools is available; however, numerous areas lack dedicated tools, while integration of existing tools is limited. This forces modelers to either handle the workflow manually, leading to errors, or to write substantial amounts of code to automate parts of the workflow, in both cases reducing their productivity. To address these issues, we have developed Mozaik: a workflow system for spiking neuronal network simulations written in Python. Mozaik integrates model, experiment and stimulation specification, simulation execution, data storage, data analysis and visualization into a single automated workflow, ensuring that all relevant metadata are available to all workflow components. It is based on several existing tools, including PyNN, Neo, and Matplotlib. It offers a declarative way to specify models and recording configurations using hierarchically organized configuration files. Mozaik automatically records all data together with all relevant metadata about the experimental context, allowing automation of the analysis and visualization stages. Mozaik has a modular architecture, and the existing modules are designed to be extensible with minimal programming effort. Mozaik increases the productivity of running virtual experiments on highly structured neuronal networks by automating the entire experimental cycle, while increasing the reliability of modeling studies by relieving the user from manual handling of the flow of metadata between the individual workflow stages.
Highlights
IntroductionOne of the primary goals of computational neuroscience is to create models of neuronal structures and their function that as closely as possible adhere to the known anatomy of brain, while at the same time match as wide a range of experimental measurements as possible
One of the primary goals of computational neuroscience is to create models of neuronal structures and their function that as closely as possible adhere to the known anatomy of brain, while at the same time match as wide a range of experimental measurements as possible. In pursuit of this goal, supported by ever more plentiful computational resources, neuroscientists are building increasingly detailed, heterogeneous neuronal models and testing them under more and more elaborate experimental conditions. This process has accelerated in the last decade due to the increasing availability and capability of high-performance computing (HPC), enabling the simulation of neuronal structures at unprecedented levels of detail (Reimann et al, 2013), while expanding the simulations into multi-layer or even multiareal contexts (Potjans and Diesmann, 2012; Nakagawa et al, 2013) and reproducing complex stimulation and recording experimental protocols (Shushruth et al, 2012)
It is our belief that this lack of support for the full simulation workflow greatly reduces the productivity of the field (Wilson, 2006) and constitutes a major challenge for future development of brain simulation infrastructure
Summary
One of the primary goals of computational neuroscience is to create models of neuronal structures and their function that as closely as possible adhere to the known anatomy of brain, while at the same time match as wide a range of experimental measurements as possible. In pursuit of this goal, supported by ever more plentiful computational resources, neuroscientists are building increasingly detailed, heterogeneous neuronal models and testing them under more and more elaborate experimental conditions This process has accelerated in the last decade due to the increasing availability and capability of high-performance computing (HPC), enabling the simulation of neuronal structures at unprecedented levels of detail (Reimann et al, 2013), while expanding the simulations into multi-layer or even multiareal contexts (Potjans and Diesmann, 2012; Nakagawa et al, 2013) and reproducing complex stimulation and recording experimental protocols (Shushruth et al, 2012). It is our belief that this lack of support for the full simulation workflow greatly reduces the productivity of the field (Wilson, 2006) and constitutes a major challenge for future development of brain simulation infrastructure
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
