Abstract
Two trends have been unfolding in computational neuroscience during the last decade. First, a shift of focus to increasingly complex and heterogeneous neural network models, with a concomitant increase in the level of collaboration within the field (whether direct or in the form of building on top of existing tools and results). Second, a general trend in science toward more open communication, both internally, with other potential scientific collaborators, and externally, with the wider public. This multi-faceted development toward more integrative approaches and more intense communication within and outside of the field poses major new challenges for modelers, as currently there is a severe lack of tools to help with automatic communication and sharing of all aspects of a simulation workflow to the rest of the community. To address this important gap in the current computational modeling software infrastructure, here we introduce Arkheia. Arkheia is a web-based open science platform for computational models in systems neuroscience. It provides an automatic, 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 users. Arkheia is written in a modular fashion with a focus on future development of the platform. The platform is designed in an open manner, with a clearly defined and separated API for database access, so that any project can write its own backend translating its data into the Arkheia database format. Arkheia is not a centralized platform, but allows any user (or group of users) to set up their own repository, either for public access by the general population, or locally for internal use. Overall, Arkheia provides users with an automatic means to communicate information about not only their models but also individual simulation results and the entire experimental context in an approachable graphical manner, thus facilitating the user's ability to collaborate in the field and outreach to a wider audience.
Highlights
For most of its history, computational neuroscience has focused on relatively homogeneous models, targeting one or at most a handful of features of neural processing at a time
Unlike Arkheia, Open Source Brain (OSB) does not offer an explicit formalized presentation of the stimulation, results, experimental protocols, and their parametric context, which we argue are key for further development of collaborative tools in computational neuroscience
We hope that some of the ongoing efforts in the wider community will generate welldesigned and popular specification standards for some of the aspects of neural simulations covered by Arkheia, such as standardization of experimental protocols, higher level model specifications or stimulus definitions, which we would eagerly seek to incorporate into Arkheia (Hucka et al, 2003). To this end Arkheia represents both a sketch of how such specifications could look and an example of how they could be used to facilitate communication and comparison across different models, motivating the development of these technologies, which we believe are key to the future computational neuroscience software infrastructure
Summary
For most of its history, computational neuroscience has focused on relatively homogeneous models, targeting one or at most a handful of features of neural processing at a time. For parts of the project that are converted to NeuroML one can invoke the Geppetto (www.geppetto.org) Java interface within the web-browser allowing the user to inspect the model in detail via a GUI One limitation of this approach is that NeuroML has been designed for detailed morphological neural models, and large-scale point neuron simulations, common in systems neuroscience, are not as well supported. Unlike Arkheia, OSB does not offer an explicit formalized presentation of the stimulation, results, experimental protocols, and their parametric context, which we argue are key for further development of collaborative tools in computational neuroscience
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