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The looks of an odour - Visualising neural odour response patterns in real time

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Background: Calcium imaging in insects reveals the neural response to odours, both at the receptor level on the antenna and in the antennal lobe, the first stage of olfactory information processing in the brain. Changes of intracellular calcium concentration in response to odour presentations can be observed by employing calciumsensitive, fluorescent dyes. The response pattern across all recorded units is characteristic for the odour. Method: Previously, extraction of odour response patterns from calcium imaging movies was performed offline, after the experiment. We developed software to extract and to visualise odour response patterns in real time. An adaptive algorithm in combination with an implementation for the graphics processing unit enables fast processing of movie streams. Relying on correlations between pixels in the temporal domain, the calcium imaging movie can be segmented into regions that correspond to the neural units. Results: We applied our software to calcium imaging data recorded from the antennal lobe of the honeybee Apis mellifera and from the antenna of the fruit fly Drosophila melanogaster. Evaluation on reference data showed results comparable to those obtained by previous offline methods while computation time was significantly lower. Demonstrating practical applicability, we employed the software in a real-time experiment, performing segmentation of glomeruli - the functional units of the honeybee antennal lobe - and visualisation of glomerular activity patterns. Conclusions: Real-time visualisation of odour response patterns expands the experimental repertoire targeted at understanding information processing in the honeybee antennal lobe. In interactive experiments, glomeruli can be selected for manipulation based on their present or past activity, or based on their anatomical position. Apart from supporting neurobiology, the software allows for utilising the insect antenna as a chemosensor, e.g. to detect or to classify odours.




Strauch, M., Müthing, C., Broeg, M. P., Szyszka, P., Münch, D., Laudes, T., … Merhof, D. (2013). The looks of an odour - Visualising neural odour response patterns in real time. BMC Bioinformatics, 14.

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