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Description, Emergent social behavior in larval zebrafish is altered by targeted gene mutations., It is not understood how changes in the genetic makeup of individuals alter the behavior of groups of animals. Here, we find that, even at early larval stages, zebrafish regulate their proximity and alignment with each other. Two simple visual responses, one that measures relative visual field occupancy and one that accounts for global visual motion, suffice to account for the group behavior that emerges. Mutations in genes known to affect social behavior in humans perturb these simple reflexes in individual larval zebrafish and change their emergent collective behaviors in the predicted fashion. Model simulations show that changes in these two responses in individual mutant animals predict well the distinctive collective patterns that emerge in a group. Hence, group behaviors reflect in part genetically defined primitive sensorimotor “motifs,” which are evident even in young larvae.

Navigating across light gradients is essential for survival for many animals. However, we still have a poor understanding of the algorithms that underlie such behaviors. Here we develop a novel phototaxis assay for Drosophila larvae in which light intensity is always spatially uniform but updates depending on the location of the animal in the arena. Even though larvae can only rely on temporal cues in this closed-loop setup, we find that they are capable of finding preferred areas of low light intensity. Further detailed analysis of their behavior reveals that larvae turn more frequently and that heading angle changes increase when they experience brightness increments over extended periods of time. We suggest that temporal integration of brightness change during runs is an important – and so far largely unexplored – element of phototaxis.Summary statementUsing a novel closed-loop behavioral assay, we show that Drosophila larvae can navigate light gradients exclusively using temporal cues. Analyzing and modeling their behavior in detail, we propose that larvae achieve this by integrating brightness change during runs.

Flies are capable of extraordinary flight maneuvers at very high speeds largely due to their highly elaborate visual system. In this work we present a fly-inspired FPGA based sensor system able to visually sense rotations around different body axes, for use on board micro aerial vehicles (MAVs). Rotation sensing is performed analogously to the fly's VS cell network using zero-crossing detection. An additional key feature of our system is the ease of adding new functionalities akin to the different tasks attributed to the fly's lobula plate tangential cell network, such as object avoidance or collision detection. Our implementation consists of a modified eneo SC-MVC01 SmartCam module and a custom built circuit board, weighing less than 200 g and consuming less than 4 W while featuring 57,600 individual two-dimensional elementary motion detectors, a 185° field of view and a frame rate of 350 frames per second. This makes our sensor system compact in terms of size, weight and power requirements for easy incorporation into MAV platforms, while autonomously performing all sensing and processing on-board and in real time. published

SummaryDetecting the direction of visual movement is fundamental for every sighted animal in order to navigate, avoid predators, or detect conspecifics. Algorithmic models of correlation-type motion detectors describe the underlying computation remarkably well [1–3]. They consist of two spatially separated input lines that are asymmetrically filtered in time and then interact in a nonlinear way. However, the cellular implementation of this computation remains elusive. Recent connectomic data of the Drosophila optic lobe has suggested a neural circuit for the detection of moving bright edges (ON motion) with medulla cells Mi1 and Tm3 providing spatially offset input to direction-selective T4 cells, thereby forming the two input lines of a motion detector [4]. Electrophysiological characterization of Mi1 and Tm3 revealed different temporal filtering properties and proposed them to correspond to the delayed and direct input, respectively [5]. Here, we test this hypothesis by silencing either Mi1 or Tm3 cells and using electrophysiological recordings and behavioral responses of flies as a readout. We show that Mi1 is a necessary element of the ON pathway under all stimulus conditions. In contrast, Tm3 is specifically required only for the detection of fast ON motion in the preferred direction. We thereby provide first functional evidence that Mi1 and Tm3 are key elements of the ON pathway and uncover an unexpected functional specialization of these two cell types. Our results thus require an elaboration of the currently prevailing model for ON motion detection [6, 7] and highlight the importance of functional studies for neural circuit breaking.

SummarySpatial contrast, the difference in adjacent luminance values, provides information about objects, textures, and motion and supports diverse visual behaviors. Contrast computation is therefore an essential element of visual processing. The underlying mechanisms, however, are poorly understood. In human psychophysics, contrast illusions are means to explore such computations, but humans offer limited experimental access. Via behavioral experiments in Drosophila, we find that flies are also susceptible to contrast illusions. Using genetic silencing techniques, electrophysiology, and modeling, we systematically dissect the mechanisms and neuronal correlates underlying the behavior. Our results indicate that spatial contrast computation involves lateral inhibition within the same pathway that computes motion of luminance increments (ON pathway). Yet motion-blind flies, in which we silenced downstream motion-sensitive neurons needed for optomotor behavior, have fully intact contrast responses. In conclusion, spatial contrast and motion cues are first computed by overlapping neuronal circuits which subsequently feed into parallel visual processing streams.

The construction of compartmental models of neurons involves tuning a set of parameters to make the model neuron behave as realistically as possible. While the parameter space of single-compartment models or other simple models can be exhaustively searched, the introduction of dendritic geometry causes the number of parameters to balloon. As parameter tuning is a daunting and time-consuming task when performed manually, reliable methods for automatically optimizing compartmental models are desperately needed, as only optimized models can capture the behavior of real neurons. Here we present a three-step strategy to automatically build reduced models of layer 5 pyramidal neurons that closely reproduce experimental data. First, we reduce the pattern of dendritic branches of a detailed model to a set of equivalent primary dendrites. Second, the ion channel densities are estimated using a multi-objective optimization strategy to fit the voltage trace recorded under two conditions - with and without the apical dendrite occluded by pinching. Finally, we tune dendritic calcium channel parameters to model the initiation of dendritic calcium spikes and the coupling between soma and dendrite. More generally, this new method can be applied to construct families of models of different neuron types, with applications ranging from the study of information processing in single neurons to realistic simulations of large-scale network dynamics. published