Intracellular processing of motion information in a network of blowfly visual interneurons

In the past few decades, the lobula plate of the fly has emerged as one of the leading models for the neural processing of optic flow stimuli that give rise to visual orientation behaviors (for recent reviews see Borst and Haag, 2002; Egelhaaf et al., 2002; Egelhaaf et al., 2002; Borst and Haag, 2007). The relative simplicity and accessibility of this neural system allows researchers to characterize the neural mechanisms that are thought to link the visual stimuli and the resulting behavioral responses. In the lobula plate, a set of 60 motion sensitive lobula plate tangential cells (LPTCs) integrate visual motion information from an array of local motion detectors, which form a retinotopic map of the fly’s visual space in the lobula plate. The selective pooling of local, direction selective inputs, together with a network of unilateral and bilateral interactions between LPTCs, shape and tune the response properties of LPTCs to behaviorally relevant optic flow stimuli. Over the years, lobula plate researchers assembled a formidable array of measurement and perturbation techniques that are usually available only in in-vitro systems. Additionally, the lobula plate and its presynaptic circuitry have been the subject of extensive and detailed modeling which allows a deeper synthetic understanding of the empirical results, as well as a more efficient and detailed way to generate hypotheses. In this work I used a selection of these tools to explore the role of intracellular processing of visual motion information in lobula plate neurons and the significance of spatial segregation and aggregation of these cells’ inputs in the context of their sensory function. Previous work on a network of ten LPTCs of the vertical system (VS cells) resulted in a prediction that due to lateral, gap-junction coupling of neighboring VS cells in their axon-terminals, the receptive fields of these cells should be broader in the axonal region than in the dendritic regions. I tested and confirmed this prediction using in-vivo calcium imaging and intracellular recordings. Using single-electrode voltage clamp I was able to perturb the flow of information in these cells and isolate the source of input responsible for this broadening, confirming that the coupling indeed takes place in the axon terminal. The separation of feed-forward, synaptic input in the dendrites from lateral, gap-junction coupling in the axon-terminals allowed me to experimentally ask what is the function of the receptive field broadening. Relying on model predictions, I showed that this broadening results in a more stable and smooth representation of optic flow in the output region of the cells than in their input region, when the fly is presented with naturalistic, patchy and non-uniform stimuli. I then showed, using a simplified compartmental model that the separation of axonal gap-junctions from the dendritic synaptic input makes the gap-junction coupling more effective, and is thus necessary to ensure the functionality of the lateral interactions

Early visual encoding of Musca domestica

Fly vision has often been considered to be quite poor, both temporally and spatially, as it is limited by numerous different factors (i.e. number of sampling units, lens dimensions, photoreceptors’ slow integration time, ambient light level as well as flies’ own speed when in motion) (Mallock, 1894; Fermi and Richardt, 1963; Srinivasan and Bernard, 1975; Warrant and McIntyre, 1992; Land, 1997; Warrant, 1999). Some studies have challenged these views and found that flies have evolved to partially overcome these constraints (i.e. via acute zones, head/thorax and body movements) (van Hateren and Schilstra, 1999; Hornstein et al., 2000; Burton, Tatler and Laughlin, 2001; Burton and Laughlin, 2003). One recent example from Juusola et al. (2017) showed that Drosophila photoreceptors contract to light and these photomechanical contractions coupled with refractory sampling enable the fly to overcome motion blur even to objects smaller than their optical limit. Following on from this work, my aim was to test whether different aspects of a fast-flying housefly (Musca domestica) would also have enhanced spatial and temporal vision beyond our current understanding. If slow-flying Drosophila with its optically poorer vision has evolved to compensate for its limitations, then in theory we should see similar, or better, improvements in a faster flying fly such as Musca. Additionally, working with Musca created the opportunity to investigate any presence of sexual dimorphism, as males have "love spots", which Drosophila males lack (GonzalezBellido, Wardill and Juusola, 2011; Perry and Desplan, 2016). My work focussed on examining via in vivo intracellular recordings visual encoding of Musca photoreceptors (R1-R6) and what happens to that information when passed downstream to large monopolar cells (LMCs, L1-L3). In total, this examination resulted in three separate studies: (i) early temporal encoding during body saccades, (ii) R1- R6 and L1-L3 cells' response properties during light adaptation and its impact on underlying quantum bumps (QBs) and (iii) hyperacuity of photoreceptors and LMCs. I found that temporal encoding of Musca early vision was better than previously thought, especially in male flies. Additionally, both photoreceptors' and LMCs’ signalling performance to different stimulus statistics improved when brightening mean light levels. However, when looking at spatial encoding, both male and female photoreceptors were in general not able to resolve details finer than their optical limit i.e. they were not hyperacute. LMCs may have this ability but further investigations are required

Evolution in 3D

PhD ThesisThis thesis explores the mechanisms underlying motion vision in the praying mantis (Sphodromantis lineola) and how this visual predator perceives camouflaged prey. By recording the mantis optomotor response to wide-field motion I was able to define the mantis Dmax, the point where a pattern is displaced by such a distance that coherent motion is no longer perceived. This allowed me to investigate the spatial characteristics of the insect wide field motion processing pathway. The insect Dmax was found to be very similar to that observed in humans which suggests similar underlying motion processing mechanisms; whereby low spatial frequency local motion is being pooled over a larger visual area compared to higher spatial frequency motion. By recording the mantis tracking response to computer generated targets, I was able to investigate whether there are any benefits of background matching when prey are moving and whether pattern influences the predatory response of the mantis towards prey. I found that only prey with large pattern elements benefit from background matching during movement; and above all prey which remain un-patterned but match the mean luminance of the background receive the greatest survival advantage. Additionally, I examined the effects of background motion on the tracking response of the mantis towards moving prey. By using a computer generated target as prey, I investigated the benefits associated with matching background motion as a protective strategy to reduce the risk of detection by predators. I found the mantis was able to successfully track a moving target in the presence of background My results suggests that although there are no overall benefits for prey to match background motion, it is costly to move out of phase with the background motion. Finally, I examined the contrast sensitivity of the mantis wide-field and small target motion detection pathways. Using the mantis tracking response to small targets and the optomotor response to wide-field motion; I measured the distinct temporal and spatial signatures of each pathway. I found the mantis wide-field and small target movement detecting pathways are each tuned to a different set of spatial and temporal frequencies. The wide-field motion detecting pathway has a high sensitivity to a broad range of spatio-temporal frequencies making it sensitive to a broad range of velocities; whereas the small-target motion-detection pathway has a high sensitivity to a narrow set of spatio-temporal combinations with optimal sensitivity to targets with a low spatial frequencymotion

Internal structure of the fly elementary motion detector

Flies use visual motion information for flight control, stabilization and object tracking. However, information about local motion such as direction and velocity is not explicitly represented at the level of the retina but must be computed by subsequent motion detection circuitry. The output of these circuits can be recorded in large, direction-selective lobula plate tangential cells, that integrate over hundreds of elementary motion detectors. The computational structure of these detectors is best described by the Reichardt model, where the signals from two neighboring photoreceptors become multiplied after one of them has been delayed. However, the neural correlate of the Reichardt Detector, i.e. the identity, physiology and connectivity of the constituting cells, has escaped further characterization due to technical difficulties in recording from these small neurons. In this thesis, I investigated the internal structure of the fly motion detection circuit by a combination of electrophysiology, computer simulations and mathematical modeling. First, I studied the effect of the mean luminance on motion detection. I found that the response strength of lobula plate tangential cells strongly depends on stimulus contrast but barely changes as a function of mean luminance. Adaptation to a new mean luminance follows an exponential decay with a time constant of several hundred milliseconds. I next investigated the structural consequences of splitting the visual input into ON and OFF components, as recently discovered in the fruit fly. The original Reichardt Detector can be refined by incorporating these findings, giving rise to two alternative structures. The 4-Quadrant-Detector consists of four independent subunits of the Reichardt type, correlating ON with ON, OFF with OFF, ON with OFF and OFF with ON signals. In contrast, the 2-Quadrant-Detector consists of two subunits only, that correlate ON with ON and OFF with OFF signals. In order to distinguish between these two models, I first stimulated flies with apparent motion stimuli consisting of a sequence of two brightness steps at neighboring locations, while recording the motion detector output in lobula plate tangential cells of the blow fly. I found strongly direction-selective responses to ON-ON and OFF-OFF sequences, but also to ON-OFF and OFF-ON sequences. At first sight, these results seem to support the 4-Quadrant-Detector. However, I showed with simulations and an analytical treatment that the 2-Quadrant-Detector, when equipped with an appropriate preprocessing stage, is capable of reproducing such responses as well. Based on predictions from model simulations, I designed a new stimulus protocol consisting of a sequence of short brightness pulses instead of steps. For such stimuli, the 2-Quadrant-Detector does not produce significant responses to ON-OFF and OFF-ON sequences, in contrast to the 4-Quadrant-Detector. The corresponding recordings cannot be reconciled with the 4-Quadrant-Detector but are in good agreement with the 2-Quadrant-Detector. I therefore conclude that the internal structure of the y elementary motion detector consists of two non-interacting subunits for detecting ON and OFF motion, respectively. These results mark an important step in the ongoing dissection of the insect motion detection circuit by providing an updated model that better matches the structure and physiology of the corresponding neural hardware