Design, fabrication and testing of miniaturized neural recording platform for robotic applications in fly sensorimotor research

Abstract

Blowflies are exquisite fliers and have long been established as models for sensorimotor research. Their flight performance crucially depends on both visual and mechanoreceptive feedback. Our understanding of how the nervous system integrates signals of these sensory modalities, however, leaves two important issues unresolved. First: As most experiments are performed on restrained animals, how are neural signals integrated when the animal actually moves? Second: Are neural mechanisms underlying optic-flow-based self-motion estimation modulated by mechanosensory modalities when controlling motor behaviour? To address these questions, I have designed a mobile platform and miniaturized electrophysiological recording equipment. Here, I describe details of the platform’s mechanical and electronic components, test its performance, and demonstrate that it enables high quality recordings from an identified interneuron in the fly motion vision pathway, the H1-cell, which is believed to process optic-flow, generate during self-motion. I compare the recordings obtained with my novel platform to those gathered with much bigger setups, which are orders of magnitude heavier, and characterize the responses of the H1-cell under various stimulus conditions. My results are mostly in agreement with earlier work but include new findings on pattern size-dependent responses of the cell, which are in contrast to theoretical predictions based on previous behavioural and physiological studies. To investigate how the fly uses signals from different sensory modalities to control various optomotor behaviours and potentially vision-based collision avoidance, I have established a brain machine interface on a small mobile robot that uses the neural activity of the H1-cell to control the robot's motor system. In summary, I have created a platform for in vivo recordings of neural activity in flies on a small robot. This novel technology will enable further studies on multisensory integration to gain new insights into the design of fly sensorimotor pathways involved in course control and collision avoidance.