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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., How visual social information informs movement is unclear. Here, the authors characterise the algorithm zebrafish use to transform visual inputs from neighbours into movement decisions during collective swimming behavior. The authors can also predict the neural circuits involved in transforming the visual input into movement decisions.

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.

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.

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., eLife digest 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.

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.

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.

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.

Compartmental models of neurons, introduced by Wilfrid Rall in 1964, have become important research tools for both theoretical and experimental neuroscientists to describe electrical (and sometimes also chemical) signalling in neurons. Usually built based on experimental data, they in turn help to interpret experiments, provide a quantitative description of neuronal function in contexts which are not yet directly accessible to experiment, and guide the development of theories of information processing in neurons and neural circuits. Detailed compartmental models, which incorporate anatomical reconstructions of the morphology of a neuron, biophysical descriptions of the kinetics and voltage dependence of its membrane conductances, as well as the membrane capacitance and intracellular resistivity, can predict the evolution in time and space of the membrane potential along the neuronal dendrites, soma and axon in response to arbitrary spatiotemporal patterns of synaptic input or current injection via intracellular electrodes. They can also form the basis for the construction of reduced, or simplified, neuronal models (reviewed by Herz et al. 2006).

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.