Three approaches for data processing in neuroscience

Key concepts explored in FNIP-T3 event:

- How to perform neuroimaging big-data analysis.
- How to benchmark functional imaging methods.
- Image segmentation: which are the open challenges/solutions?
- Which are the current bottlenecks in real-time data processing for closed-loop experimental approaches?

Image processing methods are a family of algorithms developed to extract and analyze features of interest from digital images. Thanks to recent technological developments in the field of neuro-imaging, we can now record from tens of thousand neurons with subcellular precision at high speed for prolonged periods while the animal is engaged in some behavioral task.
The traditional manual data curation is therefore no longer affordable particularly in the context of closed-loop approaches in which the neurophysiological readout must be available during the data acquisition session in order to guide for example optogenetic manipulations or behavioral interventions. Several excellent tools designed for automatic or semi-automatic data processing, are available, and in the context of this FNIP-T3 event we will discuss about 3 popular approaches with 3 experts in the field.

Together we will learn about key features, advantages and weakness of each method, with the aim to provide participants with useful information and start a discussion in the FNIP community, promoting future improvements on this critical topic.

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