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Simultaneous image and pulse acquisition from freely swimming weakly electric fish

Grant number: 16/14009-8
Support Opportunities:Scholarships in Brazil - Scientific Initiation
Effective date (Start): September 01, 2016
Effective date (End): August 31, 2017
Field of knowledge:Biological Sciences - Biophysics - Biophysics of Processes and Systems
Principal Investigator:Reynaldo Daniel Pinto
Grantee:Andressa Caroline Freires de Oliveira
Host Institution: Instituto de Física de São Carlos (IFSC). Universidade de São Paulo (USP). São Carlos , SP, Brazil


Dyads of weakly electric fish, such as Gymnotus carapo, present a sex-independent territorial behavior even in artificial environments. Electrical communication seems to play an important role in the dominance contest. The establishment of dominance constitutes an interesting problem to be studied in neuroethology and it is possible to record the electrical signals by using non-invasive techniques. However, animals are mostly active in the dark and they are very sensitive to light, usually trying to hide themselves when light is present, posing some challenges to make movies of fish behavior during effective interaction. Nevertheless, changing a fish's position in the tank also changes the amplitude of the electrical signals induced in a three-dimensional array of electrodes: in principle, these changes contain movement information. Here we propose the development of an experimental apparatus to record simultaneously the electric pulses and position of a fish freely moving in a tank. Two cameras will be applied, during the day-light, to record fish position. Such data will be used to train a machine learning algorithm to extract, from electric pulses alone (obtained in the dark), the fish position at every given pulse. These methods will be improved and generalized to address the same problem when two fish are freely interacting. During the project the student will learn to built her own experimental apparatus, its electronic instrumentation, data acquisition, implementation of machine learning algorithms, and data analysis in neuroscience, all compatible with her background in computational physics. (AU)

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