Abstract
Modeling large-scale spiking neural networks showing realistic biological behavior in their dynamics is a complex and tedious task. Since these networks consist of millions of interconnected neurons, their simulation produces an immense amount of data. In recent years it has become possible to simulate even larger networks. However, solutions to assist researchers in understanding the simulation's complex emergent behavior by means of visualization are still lacking. While developing tools to partially fill this gap, we encountered the challenge to integrate these tools easily into the neuroscientists' daily workflow. To understand what makes this so challenging, we looked into the workflows of our collaborators and analyzed how they use the visualizations to solve their daily problems. We identified two major issues: first, the analysis process can rapidly change focus which requires to switch the visualization tool that assists in the current problem domain. Second, because of the heterogeneous data that results from simulations, researchers want to relate data to investigate these effectively. Since a monolithic application model, processing and visualizing all data modalities and reflecting all combinations of possible workflows in a holistic way, is most likely impossible to develop and to maintain, a software architecture that offers specialized visualization tools that run simultaneously and can be linked together to reflect the current workflow, is a more feasible approach. To this end, we have developed a software architecture that allows neuroscientists to integrate visualization tools more closely into the modeling tasks. In addition, it forms the basis for semantic linking of different visualizations to reflect the current workflow. In this paper, we present this architecture and substantiate the usefulness of our approach by common use cases we encountered in our collaborative work.
Highlights
In recent years, advances in simulation technology and computing power have made simulation of large-scale spiking neural networks feasible
In this paper we have presented overall objectives for modeling neural system simulations and inspected a resulting workflow
We have demonstrated its applicability along a use case and showed where interactive visualizations can assist in modeling neural simulations
Summary
Advances in simulation technology and computing power have made simulation of large-scale spiking neural networks feasible These simulations produce an immense amount of data that needs to be analyzed by researchers in order to validate the simulated models. CMV systems have successfully been used to uncover complex relationships in data by enabling users to relate different data modalities and scales (cf Ryu et al, 2003), thereby assisting researchers in context switches, comparative tasks, and supplementary analysis techniques. To relate these different data modalities, coordination between views, especially linking, is required. This approach requires, on the one hand, coherent access to data so that all views display the same data model, and, on the other, a synchronization mechanism for shared data entities across views (i.e., selections)
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