Scallops possess a visual system comprising up to 200 eyes, each containing a concave mirror rather than a lens to focus light. The hierarchical organization of the multilayered mirror is controlled for image formation, from the component guanine crystals at the nanoscale to the complex three-dimensional morphology at the millimeter level. The layered structure of the mirror is tuned to reflect the wavelengths of light penetrating the scallop's habitat and is tiled with a mosaic of square guanine crystals, which reduces optical aberrations. The mirror forms images on a double-layered retina used for separately imaging the peripheral and central fields of view. The tiled, off-axis mirror of the scallop eye bears a striking resemblance to the segmented mirrors of reflecting telescopes.

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The quality of visual information that is available to an animal is limited by the size of its eyes. Differences in eye size can be observed even between closely related individuals, yet we understand little about how this affects vision. Insects are good models for exploring the effects of size on visual systems because many insect species exhibit size polymorphism. Previous work has been limited by difficulties in determining the 3D structure of eyes. We have developed a novel method based on x-ray microtomography to measure the 3D structure of insect eyes and to calculate predictions of their visual capabilities. We used our method to investigate visual allometry in the bumblebee Bombus terrestris and found that size affects specific aspects of vision, including binocular overlap, optical sensitivity, and dorsofrontal visual resolution. This reveals that differential scaling between eye areas provides flexibility that improves the visual capabilities of larger bumblebees.

Attention allows animals to respond selectively to competing stimuli, enabling some stimuli to evoke a behavioral response while others are ignored. How the brain does this remains mysterious, although it is increasingly evident that even animals with the smallest brains display this capacity. For example, insects respond selectively to salient visual stimuli, but it is unknown where such selectivity occurs in the insect brain, or whether neural correlates of attention might predict the visual choices made by an insect. Here, we investigate neural correlates of visual attention in behaving honeybees (Apis mellifera). Using a closed-loop paradigm that allows tethered, walking bees to actively control visual objects in a virtual reality arena, we show that behavioral fixation increases neuronal responses to flickering, frequency-tagged stimuli. Attention-like effects were reduced in the optic lobes during replay of the same visual sequences, when bees were not able to control the visual displays. When bees were presented with competing frequency-tagged visual stimuli, selectivity in the medulla (an optic ganglion) preceded behavioral selection of a stimulus, suggesting that modulation of early visual processing centers precedes eventual behavioral choices made by these insects.invertebrate | vision | electrophysiology | local field potential A ttention allows animals to respond selectively to competing stimuli (1, 2). Stimulus-selective responses in the human brain can be endogenously driven, and this volitional form of attention has been referred to as a "top-down" process, to distinguish it from salience-driven or "bottom-up" attention (3). Although even insects display bottom-up attention (4-10), it is unclear whether attention-like selection in the insect brain might also precede or predict behavioral choices. The case for top-down attention is especially compelling for honeybees, which have welldemonstrated visual discrimination and cognitive capabilities (11)(12)(13)(14). To effectively relate attention processes to behavior, however, requires sophisticated behavioral tracking or recording brain activity from behaving insects selecting distinct objects (15). Previous psychophysical studies in insects have measured whole body movements using tethered, closed-loop flight paradigms (4-8, 15). However, most studies of visual perception and memory in the bee have involved free flight (11, 13, 14; but see ref. 16). To address the neural mechanisms subserving these behaviors, researchers have traditionally recorded brain activity from immobilized bees performing elemental associative learning (e.g., refs. 17 and 18). Animal immobilization, however, is not ideal for gaining a better understanding of the relationship between the complex cognitive behaviors seen in freely moving bees and the underlying neural activity (13,14). To this end, we developed a closed-loop paradigm for walking honey bees (19), allowing them to select and fixate visual cues by rotating an air-supported ball. To simultaneously examine attentio...

The quality of visual information that is available to an animal is limited by the size of its eyes. Differences in eye size can be observed even between closely related individuals but we understand little about how this affects visual quality. Insects are good models for exploring the effects of size on visual systems because many species exhibit size polymorphism, which modifies both the size and shape of their eyes. Previous work in this area has been limited, however, due to the challenge of determining the 3D structure of eyes. To address this, we have developed a novel method based on x-ray tomography to measure the 3D structure of insect eyes and calculate their visual capabilities. We investigated visual allometry in the bumblebee Bombus terrestris and found that size affects specific aspects of visual quality including binocular overlap, optical sensitivity across the field of view, and visual resolution in the dorsofrontal visual field. This holistic study on eye allometry reveals that differential scaling between different eye areas provides substantial flexibility for larger bumblebees to have improved visual capabilities. List of abbreviations:az. -Azimuth CC -Crystalline cone el. -Elevation EV -Eye volume FOV -Field of view ITW -Inter-tegula width IO -Inter-ommatidial microCT -micro-computed tomography NV -Normal vector