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| Home > Publications database > Visual exploration and generation of connectivity in neural networks: bridging the gap between empirical data and theoretical model definition. |
| Home > Publications database > Visual exploration and generation of connectivity in neural networks: bridging the gap between empirical data and theoretical model definition. |
| Poster (After Call) | FZJ-2017-07800 |
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2017
Please use a persistent id in citations: http://hdl.handle.net/2128/15991
Abstract: The study of connectivity is central in the diverse disciplines of neuroscience. On one hand, the structured definition of network connectivity is an essential step in network simulations. On the other hand, we can derive connectivity information from experimental data and various theoretical models at multiple scales. However, the connectivity information in these two contexts is represented differently. This results in a language gap limiting the flow of knowledge learned at different levels of abstraction. In this work, we present a first step in the creation of a shared visual language to bridge this gap between model based and empirical neuroscience, allowing us to work towards a single integrated representation of the brain.We have developed a visual and source-agnostic interactive interface to generate connectivity in neural networks at various scales. Based on NeuroScheme [1] and the Connection Set Algebra (CSA)[2], we can generate connectivity and use it in simulator-specific scripts to later perform simulations of the dynamics of the network. Our approach allows us to interactively create, explore and visualize connectivity even for large scale networks where probability based connections are used to describe the synapse generation. Here we show initial results of the tool applied to Potjan's and Diesmann microcircuit model as an initial use case for describing and exploring the connectivity.With this approach, we offer the neuroscientific community a generic tool for the easy generation and exploration of connectivity. The lack of dependency on a specific simulator makes this tool a good starting point for validation of complex neural network models using many simulation and emulation platforms, particularly when coupled. Our future applications involve incorporating this tool to complete workflows consisting of raw data processing, interactive exploration, creation and visualization of abstract connectivity models, simulation, analysis and validation.
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