Reflective multi-immersion microscope objectives inspired by the Schmidt telescope.

Imaging large, cleared samples requires microscope objectives that combine a large field of view (FOV) with a long working distance (WD) and a high numerical aperture (NA). Ideally, such objectives should be compatible with a wide range of immersion media, which is challenging to achieve with conventional lens-based objective designs. Here we introduce the multi-immersion 'Schmidt objective' consisting of a spherical mirror and an aspherical correction plate as a solution to this problem. We demonstrate that a multi-photon variant of the Schmidt objective is compatible with all homogeneous immersion media and achieves an NA of 1.08 at a refractive index of 1.56, 1.1-mm FOV and 11-mm WD. We highlight its versatility by imaging cleared samples in various media ranging from air and water to benzyl alcohol/benzyl benzoate, dibenzyl ether and ethyl cinnamate and by imaging of neuronal activity in larval zebrafish in vivo. In principle, the concept can be extended to any imaging modality, including wide-field, confocal and light-sheet microscopy.

A Visual Interface for Exploring Hypotheses about Neural Circuits.

One of the fundamental problems in neurobiological research is to understand how neural circuits generate behaviors in response to sensory stimuli. Elucidating such neural circuits requires anatomical and functional information about the neurons that are active during the processing of the sensory information and generation of the respective response, as well as an identification of the connections between these neurons. With modern imaging techniques, both morphological properties of individual neurons as well as functional information related to sensory processing, information integration and behavior can be obtained. Given the resulting information, neurobiologists are faced with the task of identifying the anatomical structures down to individual neurons that are linked to the studied behavior and the processing of the respective sensory stimuli. Here, we present a novel interactive tool that assists neurobiologists in the aforementioned task by allowing them to extract hypothetical neural circuits constrained by anatomical and functional data. Our approach is based on two types of structural data: brain regions that are anatomically or functionally defined, and morphologies of individual neurons. Both types of structural data are interlinked and augmented with additional information. The presented tool allows the expert user to identify neurons using Boolean queries. The interactive formulation of these queries is supported by linked views, using, among other things, two novel 2D abstractions of neural circuits. The approach was validated in two case studies investigating the neural basis of vision-based behavioral responses in zebrafish larvae. Despite this particular application, we believe that the presented tool will be of general interest for exploring hypotheses about neural circuits in other species, genera and taxa.

Precise visuomotor transformations underlying collective behavior in larval zebrafish.

Complex schooling behaviors result from local interactions among individuals. Yet, how sensory signals from neighbors are analyzed in the visuomotor stream of animals is poorly understood. Here, we studied aggregation behavior in larval zebrafish and found that over development larvae transition from overdispersed groups to tight shoals. Using a virtual reality assay, we characterized the algorithms fish use to transform visual inputs from neighbors into movement decisions. We found that young larvae turn away from virtual neighbors by integrating and averaging retina-wide visual occupancy within each eye, and by using a winner-take-all strategy for binocular integration. As fish mature, their responses expand to include attraction to virtual neighbors, which is based on similar algorithms of visual integration. Using model simulations, we show that the observed algorithms accurately predict group structure over development. These findings allow us to make testable predictions regarding the neuronal circuits underlying collective behavior in zebrafish.

Collective behavior emerges from genetically controlled simple behavioral motifs in zebrafish.

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.

Navigational strategies underlying temporal phototaxis in Drosophila 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 developed a novel closed-loop 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 during runs, 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.

Algorithms underlying flexible phototaxis in larval zebrafish.

To thrive, organisms must maintain physiological and environmental variables in suitable ranges. Given that these variables undergo constant fluctuations over varying time scales, how do biological control systems maintain control over these values? We explored this question in the context of phototactic behavior in larval zebrafish. We demonstrate that larval zebrafish use phototaxis to maintain environmental luminance at a set point, that the value of this set point fluctuates on a time scale of seconds when environmental luminance changes, and that it is determined by calculating the mean input across both sides of the visual field. These results expand on previous studies of flexible phototaxis in larval zebrafish; they suggest that larval zebrafish exert homeostatic control over the luminance of their surroundings, and that feedback from the surroundings drives allostatic changes to the luminance set point. As such, we describe a novel behavioral algorithm with which larval zebrafish exert control over a sensory variable.

Neural circuits for evidence accumulation and decision making in larval zebrafish.

To make appropriate decisions, animals need to accumulate sensory evidence. Simple integrator models can explain many aspects of such behavior, but how the underlying computations are mechanistically implemented in the brain remains poorly understood. Here we approach this problem by adapting the random-dot motion discrimination paradigm, classically used in primate studies, to larval zebrafish. Using their innate optomotor response as a measure of decision making, we find that larval zebrafish accumulate and remember motion evidence over many seconds and that the behavior is in close agreement with a bounded leaky integrator model. Through the use of brain-wide functional imaging, we identify three neuronal clusters in the anterior hindbrain that are well suited to execute the underlying computations. By relating the dynamics within these structures to individual behavioral choices, we propose a biophysically plausible circuit arrangement in which an evidence integrator competes against a dynamic decision threshold to activate a downstream motor command.

A bidirectional network for appetite control in larval zebrafish.

Medial and lateral hypothalamic loci are known to suppress and enhance appetite, respectively, but the dynamics and functional significance of their interaction have yet to be explored. Here we report that, in larval zebrafish, primarily serotonergic neurons of the ventromedial caudal hypothalamus (cH) become increasingly active during food deprivation, whereas activity in the lateral hypothalamus (LH) is reduced. Exposure to food sensory and consummatory cues reverses the activity patterns of these two nuclei, consistent with their representation of opposing internal hunger states. Baseline activity is restored as food-deprived animals return to satiety via voracious feeding. The antagonistic relationship and functional importance of cH and LH activity patterns were confirmed by targeted stimulation and ablation of cH neurons. Collectively, the data allow us to propose a model in which these hypothalamic nuclei regulate different phases of hunger and satiety and coordinate energy balance via antagonistic control of distinct behavioral outputs.

How soon after a meal do you start feeling hungry again? The answer depends on a complex set of processes within the brain that regulate appetite. A key player in these processes is the hypothalamus, a small structure at the base of the brain. The hypothalamus consists of many different subregions, some of which are responsible for increasing or decreasing hunger. Wee, Song et al. now show how two of these subregions interact to regulate appetite and feeding, by studying them in hungry zebrafish larvae. The brains of zebrafish have many features in common with the brains of mammals, but they are smaller and transparent, which makes them easier to study. Wee, Song et al. show that as larvae become hungry, an area called the caudal hypothalamus increases its activity. But when the larvae find food and start feeding, activity in this area falls sharply. It then remains low while the hungry larvae eat as much as possible. Eventually the larvae become full and start eating more slowly. As they do so, the activity of the caudal hypothalamus goes back to normal levels. While this is happening, activity in a different area called the lateral hypothalamus shows the opposite pattern. It has low activity in hungry larvae, which increases when food becomes available and feeding begins. When the larvae finally reduce their rate of feeding, the activity in the lateral hypothalamus drops back down. The authors posit that by inhibiting each other's activity, the caudal and lateral hypothalamus work together to ensure that animals search for food when necessary, but switch to feeding behavior when food becomes available. Serotonin ­ which is produced by the caudal hypothalamus ­ and drugs that act like it have been proposed to suppress appetite, but they have varied and complex effects on food intake and weight gain. By showing that activity in the caudal hypothalamus changes depending on whether food is present, the current findings may provide insights into this complexity. More generally, they show that mapping the circuits that regulate appetite and feeding in simple organisms could help us understand the same processes in humans.

Visual Projection Neurons Mediating Directed Courtship in Drosophila.

Many animals rely on vision to detect, locate, and track moving objects. In Drosophila courtship, males primarily use visual cues to orient toward and follow females and to select the ipsilateral wing for courtship song. Here, we show that the LC10 visual projection neurons convey essential visual information during courtship. Males with LC10 neurons silenced are unable to orient toward or maintain proximity to the female and do not predominantly use the ipsilateral wing when singing. LC10 neurons preferentially respond to small moving objects using an antagonistic motion-based center-surround mechanism. Unilateral activation of LC10 neurons recapitulates the orienting and ipsilateral wing extension normally elicited by females, and the potency with which LC10 induces wing extension is enhanced in a state of courtship arousal controlled by male-specific P1 neurons. These data suggest that LC10 is a major pathway relaying visual input to the courtship circuits in the male brain.

Asymmetry of Drosophila ON and OFF motion detectors enhances real-world velocity estimation.

The reliable estimation of motion across varied surroundings represents a survival-critical task for sighted animals. How neural circuits have adapted to the particular demands of natural environments, however, is not well understood. We explored this question in the visual system of Drosophila melanogaster. Here, as in many mammalian retinas, motion is computed in parallel streams for brightness increments (ON) and decrements (OFF). When genetically isolated, ON and OFF pathways proved equally capable of accurately matching walking responses to realistic motion. To our surprise, detailed characterization of their functional tuning properties through in vivo calcium imaging and electrophysiology revealed stark differences in temporal tuning between ON and OFF channels. We trained an in silico motion estimation model on natural scenes and discovered that our optimized detector exhibited differences similar to those of the biological system. Thus, functional ON-OFF asymmetries in fly visual circuitry may reflect ON-OFF asymmetries in natural environments.

Neural Mechanisms for Drosophila Contrast Vision.

Spatial 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.

Functional Specialization of Neural Input Elements to the Drosophila ON Motion Detector.

Detecting 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. 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. Electrophysiological characterization of Mi1 and Tm3 revealed different temporal filtering properties and proposed them to correspond to the delayed and direct input, respectively. 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 and highlight the importance of functional studies for neural circuit breaking.

A directional tuning map of Drosophila elementary motion detectors.

The extraction of directional motion information from changing retinal images is one of the earliest and most important processing steps in any visual system. In the fly optic lobe, two parallel processing streams have been anatomically described, leading from two first-order interneurons, L1 and L2, via T4 and T5 cells onto large, wide-field motion-sensitive interneurons of the lobula plate. Therefore, T4 and T5 cells are thought to have a pivotal role in motion processing; however, owing to their small size, it is difficult to obtain electrical recordings of T4 and T5 cells, leaving their visual response properties largely unknown. We circumvent this problem by means of optical recording from these cells in Drosophila, using the genetically encoded calcium indicator GCaMP5 (ref. 2). Here we find that specific subpopulations of T4 and T5 cells are directionally tuned to one of the four cardinal directions; that is, front-to-back, back-to-front, upwards and downwards. Depending on their preferred direction, T4 and T5 cells terminate in specific sublayers of the lobula plate. T4 and T5 functionally segregate with respect to contrast polarity: whereas T4 cells selectively respond to moving brightness increments (ON edges), T5 cells only respond to moving brightness decrements (OFF edges). When the output from T4 or T5 cells is blocked, the responses of postsynaptic lobula plate neurons to moving ON (T4 block) or OFF edges (T5 block) are selectively compromised. The same effects are seen in turning responses of tethered walking flies. Thus, starting with L1 and L2, the visual input is split into separate ON and OFF pathways, and motion along all four cardinal directions is computed separately within each pathway. The output of these eight different motion detectors is then sorted such that ON (T4) and OFF (T5) motion detectors with the same directional tuning converge in the same layer of the lobula plate, jointly providing the input to downstream circuits and motion-driven behaviours.

Object tracking in motion-blind flies.

Different visual features of an object, such as its position and direction of motion, are important elements for animal orientation, but the neural circuits extracting them are generally not well understood. We analyzed this problem in Drosophila, focusing on two well-studied behaviors known as optomotor response and fixation response. In the neural circuit controlling the optomotor response, columnar T4 and T5 cells are thought to be crucial. We found that blocking T4 and T5 cells resulted in a complete loss of the optomotor response. Nevertheless, these flies were still able to fixate a black bar, although at a reduced performance level. Further analysis revealed that flies in which T4 and T5 cells were blocked possess an intact position circuit that is implemented in parallel to the motion circuit; the optomotor response is exclusively controlled by the motion circuit, whereas the fixation response is supported by both the position and the motion circuit.

Automated optimization of a reduced layer 5 pyramidal cell model based on experimental data.

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.

Bio-inspired visual ego-rotation sensor for MAVs.

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.