Uncovering algorithms in the fly visual system [electronic resource]
Keywords: Neurobiology.

One important goal of the nervous system is to allow an animal to formulate an appropriate response to external stimuli. Doing so often requires both prediction of future events and comparison of expected outcomes to observed outcomes. The detection of visual motion can subserve both of these goals. Determining the speed and direction of a moving object—be it prey, predator, or a conspecific—allows an animal to predict where this target will likely be in the near future. Such a calculation can be essential to allowing the animal to eat, to avoid being eaten, or to successfully reproduce. Because of these many important uses for motion information, directional calculations are an early and essential task of the visual systems across animal phyla. Both psychophysical and physiological study has been used to try to understand what motion computations are performed by different visual systems and how these computations are implemented within neural circuits. Flies specifically and insects more generally have been used as model systems for the study of motion vision for over 60 years, with many theories about motion vision being advanced through intensive behavioral characterization and recordings from large motion-selective neurons accessible to electrophysiology. Over the past several years, the development of genetic tools has enabled direct identification of many of the neurons that play key roles in the processing of visual input to generate direction-selective neuronal responses and motion-guided visual behaviors. These recent studies have shown that motion-processing in the fly is split into separate pathways for moving light edges and moving dark edges at the level of second-order neurons (Joesch, Schnell, et al., 2010; Silies et al., 2013) and have shown that several third-order neurons have structural spatial offsets and temporal response properties appropriate to serve in the generation of direction-selective responses (Takemura, Bharioke, et al., 2013; Behnia et al., 2014; Shinomiya et al., 2014). Most significantly, these genetic tools have allowed for the identification of the first-direction selective neurons in the fly brain: T4 and T5 (Maisak et al., 2013). Prior to this work, the T4 and T5 neurons had been demonstrated to be direction- selective, edge-polarity selective, and orientation-selective, but the exact contrast comparisons and algorithm that they implemented to lead to such selectivity was unknown (Maisak et al., 2013; Fisher, Silies, and Clandinin, 2015). We set out to identify the algorithm that is implemented in the T4 and T5 neurons that leads to their stimulus selectivity. In this study, both functional segmentation (Chapter 2) and genetic tools are used isolate the visually-evoked activity of the first neurons in the fly visual system to exhibit direction selectivity (Chapter 3). The responses of single units are then used to fit a stimulus-response model that elucidates the functional computations accounting for the direction-selective responses of these neurons. This model places specific constraints on the contribution of synaptic inputs to these direction-selective neurons. Finally, as part of an ongoing effort to assign specific neurons to particular processing steps leading to the visual algorithm implemented by T4 and T5, preliminary experiments that aim at understanding the unique contributions of different early visual inputs are described (Chapter 4).