Computational neurosceince is shifting towards more heterogeneous models of neuronal circuits, and employment of complex experimental protocols. This increase of complexity is not sufficiently addressed by existing tool chains. Mozaik is a workflow system for spiking neuronal network simulations written in Python that integrates model, experiment and stimulation specification, simulation execution, data storage, data analysis and visualization into a single automated workflow. This way Mozaik increases the productivity of running virtual experiments on complex neuronal networks. Download Mozaik on GitHub or read about it here.

The multi-faceted development in computational neuroscience towards more integrative approaches and more intense communication poses major new challenges for modelers, as currently there is a lack of tools that help with automatic communication of all aspects of a simulation workflow to the rest of the community. To address this important gap, we introduce Arkheia. Arkheia is a web-based open science platform for computational models in systems neuroscience. It provides an interactive, graphical presentation of simulation results, experimental protocols, and interactive exploration of parameter searches, in a web browser-based application. Arkheia is focused on automatic presentation of these resources with minimal manual input from user. It is designed in an open manner, with a clearly defined and separated API for database access, so that any project can translate its data into the Arkheia database format. Download Arkheia on GitHub, or read about it here.