Nervous systems combine lower-level sensory signals to detect higher-order stimulus features critical to survival, such as the visual looming motion created by an imminent collision or approaching predator. Looming-sensitive neurons have been identified in diverse animal species. Different large-scale visual features such as looming often share local cues, which means loom-detecting neurons face the challenge of rejecting confounding stimuli. Here we report the discovery of an ultra-selective looming detecting neuron, lobula plate/lobula columnar, type II (LPLC2) in Drosophila, and show how its selectivity is established by radial motion opponency. In the fly visual system, directionally selective small-field neurons called T4 and T5 form a spatial map in the lobula plate, where they each terminate in one of four retinotopic layers, such that each layer responds to motion in a different cardinal direction. Single-cell anatomical analysis reveals that each arm of the LPLC2 cross-shaped primary dendrites ramifies in one of these layers and extends along that layer's preferred motion direction. In vivo calcium imaging demonstrates that, as their shape predicts, individual LPLC2 neurons respond strongly to outward motion emanating from the centre of the neuron's receptive field. Each dendritic arm also receives local inhibitory inputs directionally selective for inward motion opposing the excitation. This radial motion opponency generates a balance of excitation and inhibition that makes LPLC2 non-responsive to related patterns of motion such as contraction, wide-field rotation or luminance change. As a population, LPLC2 neurons densely cover visual space and terminate onto the giant fibre descending neurons, which drive the jump muscle motor neuron to trigger an escape take off. Our findings provide a mechanistic description of the selective feature detection that flies use to discern and escape looming threats.

Assigning behavioral functions to neural structures has long been a central goal in neuroscience and is a necessary first step toward a circuit-level understanding of how the brain generates behavior. Here, we map the neural substrates of locomotion and social behaviors for Drosophila melanogaster using automated machine-vision and machine-learning techniques. From videos of 400,000 flies, we quantified the behavioral effects of activating 2,204 genetically targeted populations of neurons. We combined a novel quantification of anatomy with our behavioral analysis to create brain-behavior correlation maps, which are shared as browsable web pages and interactive software. Based on these maps, we generated hypotheses of regions of the brain causally related to sensory processing, locomotor control, courtship, aggression, and sleep. Our maps directly specify genetic tools to target these regions, which we used to identify a small population of neurons with a role in the control of walking.

•We developed machine-vision methods to broadly and precisely quantify fly behavior•We measured effects of activating 2,204 genetically targeted neuronal populations•We created whole-brain maps of neural substrates of locomotor and social behaviors•We created resources for exploring our results and enabling further investigation

Machine-vision analyses of large behavior and neuroanatomy data reveal whole-brain maps of regions associated with numerous complex behaviors.

The perception of visual motion is critical for animal navigation, and flies are a prominent model system for exploring this neural computation. In Drosophila, the T4 cells of the medulla are directionally selective and necessary for ON motion behavioral responses. To examine the emergence of directional selectivity, we developed genetic driver lines for the neuron types with the most synapses onto T4 cells. Using calcium imaging, we found that these neuron types are not directionally selective and that selectivity arises in the T4 dendrites. By silencing each input neuron type, we identified which neurons are necessary for T4 directional selectivity and ON motion behavioral responses. We then determined the sign of the connections between these neurons and T4 cells using neuronal photoactivation. Our results indicate a computational architecture for motion detection that is a hybrid of classic theoretical models.

Visual motion sensing neurons in the fly also encode a range of behavior-related signals. These nonvisual inputs appear to be used to correct some of the challenges of visually guided locomotion.

The delivery of tracers into populations of neurons is essential to visualize their anatomy and analyze their function. In some model systems genetically-targeted expression of fluorescent proteins is the method of choice; however, these genetic tools are not available for most organisms and alternative labeling methods are very limited. Here we describe a new method for neuronal labelling by electrophoretic dye delivery from a suction electrode directly through the neuronal sheath of nerves and ganglia in insects. Polar tracer molecules were delivered into the locust auditory nerve without destroying its function, simultaneously staining peripheral sensory structures and central axonal projections. Local neuron populations could be labelled directly through the surface of the brain, and in-vivo optical imaging of sound-evoked activity was achieved through the electrophoretic delivery of calcium indicators. The method provides a new tool for studying how stimuli are processed in peripheral and central sensory pathways and is a significant advance for the study of nervous systems in non-model organisms.

Visual projection neurons (VPNs) provide an anatomical connection between early visual processing and higher brain regions. Here we characterize lobula columnar (LC) cells, a class of Drosophila VPNs that project to distinct central brain structures called optic glomeruli. We anatomically describe 22 different LC types and show that, for several types, optogenetic activation in freely moving flies evokes specific behaviors. The activation phenotypes of two LC types closely resemble natural avoidance behaviors triggered by a visual loom. In vivo two-photon calcium imaging reveals that these LC types respond to looming stimuli, while another type does not, but instead responds to the motion of a small object. Activation of LC neurons on only one side of the brain can result in attractive or aversive turning behaviors depending on the cell type. Our results indicate that LC neurons convey information on the presence and location of visual features relevant for specific behaviors.

Color is famous for not existing in the external world: our brains create the perception of color from the spatial and temporal patterns of the wavelength and intensity of light. For an intangible quality, we have detailed knowledge of its origins and consequences. Much is known about the organization and evolution of the first phases of color processing, the filtering of light in the eye and processing in the retina, and about the final phases, the roles of color in behavior and natural selection. To understand how color processing in the central brain has evolved, we need well-defined pathways or circuitry where we can gauge how color contributes to the computations involved in specific behaviors. Examples of such pathways or circuitry that are dedicated to processing color cues are rare, despite the separation of color and luminance pathways early in the visual system of many species, and despite the traditional definition of color as being independent of luminance. This minireview presents examples in which color vision contributes to behaviors dominated by other visual modalities, examples that are not part of the canon of color vision circuitry. The pathways and circuitry process a range of chromatic properties of objects and their illumination, and are taken from a variety of species. By considering how color processing complements luminance processing, rather than being independent of it, we gain an additional way to account for the diversity of color coding in the central brain, its consequences for specific behaviors and ultimately the evolution of color vision.

Animal species display enormous variation for innate behaviours, but little is known about how this diversity arose. Here, using an unbiased genetic approach, we map a courtship song difference between wild isolates of Drosophila simulans and Drosophila mauritiana to a 966 base pair region within the slowpoke (slo) locus, which encodes a calcium-activated potassium channel. Using the reciprocal hemizygosity test, we confirm that slo is the causal locus and resolve the causal mutation to the evolutionarily recent insertion of a retroelement in a slo intron within D. simulans. Targeted deletion of this retroelement reverts the song phenotype and alters slo splicing. Like many ion channel genes, slo is expressed widely in the nervous system and influences a variety of behaviours; slo-null males sing little song with severely disrupted features. By contrast, the natural variant of slo alters a specific component of courtship song, illustrating that regulatory evolution of a highly pleiotropic ion channel gene can cause modular changes in behaviour.

Animal sensory systems are optimally adapted to those features typically encountered in natural surrounds, thus allowing neurons with limited bandwidth to encode challengingly large input ranges. Natural scenes are not random, and peripheral visual systems in vertebrates and insects have evolved to respond efficiently to their typical spatial statistics. The mammalian visual cortex is also tuned to natural spatial statistics, but less is known about coding in higher order neurons in insects. To redress this we here record intracellularly from a higher order visual neuron in the hoverfly. We show that the cSIFE neuron, which is inhibited by stationary images, is maximally inhibited when the slope constant of the amplitude spectrum is close to the mean in natural scenes. The behavioural optomotor response is also strongest to images with naturalistic image statistics. Our results thus reveal a close coupling between the inherent statistics of natural scenes and higher order visual processing in insects.