A detailed understanding of brain function requires monitoring, analysing and interpreting the activity of large networks of neurons whilst animals perform meaningful behaviours. In recent years, neuroscientists have come ever closer to achieving this goal, mainly by developing imaging techniques that allow measurements of large fractions of neurons with high spatial and temporal resolution.

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In addition, behavioural monitoring has become increasingly sophisticated. For example, high-speed video recordings allow the precise evaluation of animals’ movements whilst neuronal activity is acquired in parallel. In combination, these new experimental approaches offer novel insights into the neuronal programs that organise behaviour. However, these techniques are very data-intensive, and pose new analysis and data management challenges for neuroscientists.

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In this project, a generic data analysis pipeline was developed for Prof. Fritjof Helmchen at the Brain Research Institute, University of Zurich. This pipeline allows concurrent processing of neuronal and behavioural data from the acquisition stage, right through to the visualisation of results. One of the main design criteria, and a significant advantage over current ‘lab-internal’ solutions, is the scalability of the pipeline to ever-increasing amounts of data.

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In order to achieve this, distributed processing frameworks optimised for dealing with large volumes of data (e.g. Hadoop, Spark) were utilised. In addition, the pipeline is not restricted, and is able to deal with data acquired by different modalities and systems (e.g. microscopy, electrophysiology, video tracking). This was achieved by combining an internal data model well suited for multi-dimensional time-series data with a set of ‘plug-ins’ for existing data stores. Finally, a number of training sessions were delivered at the customer to ensure maximum benefit from the pipeline.