Common strategies for light adaptation in the peripheral visual systems of fly and dragonfly

Multifaceted luminance gain control beyond photoreceptors in Drosophila

Animals navigating in natural environments must handle vast changes in their sensory input. Visual systems, for example, handle changes in luminance at many timescales, from slow changes across the day to rapid changes during active behavior. To maintain luminance-invariant perception, visual systems must adapt their sensitivity to changing luminance at different timescales. We demonstrate that luminance gain control in photoreceptors alone is insufficient to explain luminance invariance at both fast and slow timescales and reveal the algorithms that adjust gain past photoreceptors in the fly eye. We combined imaging and behavioral experiments with computational modeling to show that downstream of photoreceptors, circuitry taking input from the single luminance-sensitive neuron type L3 implements gain control at fast and slow timescales. This computation is bidirectional in that it prevents the underestimation of contrasts in low luminance and overestimation in high luminance. An algorithmic model disentangles these multifaceted contributions and shows that the bidirectional gain control occurs at both timescales. The model implements a nonlinear interaction of luminance and contrast to achieve gain correction at fast timescales and a dark-sensitive channel to improve the detection of dim stimuli at slow timescales. Together, our work demonstrates how a single neuronal channel performs diverse computations to implement gain control at multiple timescales that are together important for navigation in natural environments.

Understanding insect colour constancy

Colour constancy is the ability to recognize the colour of objects despite spectral changes in the natural illumination. As such, this phenomenon is important for most organisms with good colour vision, and it has been intensively studied in humans and primates. Colour constancy is also documented for several species of insects, which is not surprising given the ecological importance of colour vision. But how do insects, with their small brains, solve the complex problem of colour vision and colour constancy? In an interspecies approach, this review reports on behavioural studies on colour constancy in bees, butterflies, moths and humans, corresponding computational models and possible neurophysiological correlates. This article is part of the theme issue 'Understanding colour vision: molecular, physiological, neuronal and behavioural studies in arthropods'.

Divisive normalization is an efficient code for multivariate Pareto-distributed environments

Divisive normalization is a canonical computation in the brain, observed across neural systems, that is often considered to be an implementation of the efficient coding principle. We provide a theoretical result that makes the conditions under which divisive normalization is an efficient code analytically precise: We show that, in a low-noise regime, encoding an n-dimensional stimulus via divisive normalization is efficient if and only if its prevalence in the environment is described by a multivariate Pareto distribution. We generalize this multivariate analog of histogram equalization to allow for arbitrary metabolic costs of the representation, and show how different assumptions on costs are associated with different shapes of the distributions that divisive normalization efficiently encodes. Our result suggests that divisive normalization may have evolved to efficiently represent stimuli with Pareto distributions. We demonstrate that this efficiently encoded distribution is consistent with stylized features of naturalistic stimulus distributions such as their characteristic conditional variance dependence, and we provide empirical evidence suggesting that it may capture the statistics of filter responses to naturalistic images. Our theoretical finding also yields empirically testable predictions across sensory domains on how the divisive normalization parameters should be tuned to features of the input distribution.

First-order visual interneurons distribute distinct contrast and luminance information across ON and OFF pathways to achieve stable behavior

The accurate processing of contrast is the basis for all visually guided behaviors. Visual scenes with rapidly changing illumination challenge contrast computation because photoreceptor adaptation is not fast enough to compensate for such changes. Yet, human perception of contrast is stable even when the visual environment is quickly changing, suggesting rapid post receptor luminance gain control. Similarly, in the fruit fly Drosophila, such gain control leads to luminance invariant behavior for moving OFF stimuli. Here, we show that behavioral responses to moving ON stimuli also utilize a luminance gain, and that ON-motion guided behavior depends on inputs from three first-order interneurons L1, L2, and L3. Each of these neurons encodes contrast and luminance differently and distributes information asymmetrically across both ON and OFF contrast-selective pathways. Behavioral responses to both ON and OFF stimuli rely on a luminance-based correction provided by L1 and L3, wherein L1 supports contrast computation linearly, and L3 non-linearly amplifies dim stimuli. Therefore, L1, L2, and L3 are not specific inputs to ON and OFF pathways but the lamina serves as a separate processing layer that distributes distinct luminance and contrast information across ON and OFF pathways to support behavior in varying conditions.

Disentangling of Local and Wide-Field Motion Adaptation

Motion adaptation has been attributed in flying insects a pivotal functional role in spatial vision based on optic flow. Ongoing motion enhances in the visual pathway the representation of spatial discontinuities, which manifest themselves as velocity discontinuities in the retinal optic flow pattern during translational locomotion. There is evidence for different spatial scales of motion adaptation at the different visual processing stages. Motion adaptation is supposed to take place, on the one hand, on a retinotopic basis at the level of local motion detecting neurons and, on the other hand, at the level of wide-field neurons pooling the output of many of these local motion detectors. So far, local and wide-field adaptation could not be analyzed separately, since conventional motion stimuli jointly affect both adaptive processes. Therefore, we designed a novel stimulus paradigm based on two types of motion stimuli that had the same overall strength but differed in that one led to local motion adaptation while the other did not. We recorded intracellularly the activity of a particular wide-field motion-sensitive neuron, the horizontal system equatorial cell (HSE) in blowflies. The experimental data were interpreted based on a computational model of the visual motion pathway, which included the spatially pooling HSE-cell. By comparing the difference between the recorded and modeled HSE-cell responses induced by the two types of motion adaptation, the major characteristics of local and wide-field adaptation could be pinpointed. Wide-field adaptation could be shown to strongly depend on the activation level of the cell and, thus, on the direction of motion. In contrast, the response gain is reduced by local motion adaptation to a similar extent independent of the direction of motion. This direction-independent adaptation differs fundamentally from the well-known adaptive adjustment of response gain according to the prevailing overall stimulus level that is considered essential for an efficient signal representation by neurons with a limited operating range. Direction-independent adaptation is discussed to result from the joint activity of local motion-sensitive neurons of different preferred directions and to lead to a representation of the local motion direction that is independent of the overall direction of global motion.

Stabilizing responses to sideslip disturbances in Drosophila melanogaster are modulated by the density of moving elements on the ground

Stabilizing responses to sideslip disturbances are a critical part of the flight control system in flies. While strongly mediated by mechanoreception, much of the final response results from the wide-field motion detection system associated with vision. In order to be effective, these responses must match the disturbance they are aimed to correct. To do this, flies must estimate the velocity of the disturbance, although it is not known how they accomplish this task when presented with natural images or dot fields. The recent finding, that motion parallax in dot fields can modulate stabilizing responses only if perceived below the fly, raises the question of whether other image statistics are also processed differently between eye regions. One such parameter is the density of elements moving in translational optic flow. Depending on the habitat, there might be strong differences in the density of elements providing information about self-motion above and below the fly, which in turn could act as selective pressures tuning the visual system to process this parameter on a regional basis. By presenting laterally moving dot fields of different densities we found that, in Drosophila melanogaster, the amplitude of the stabilizing response is significantly affected by the number of elements in the field of view. Flies countersteer strongly within a relatively low and narrow range of element densities. But this effect is exclusive to the ventral region of the eye, and dorsal stimuli elicit an unaltered and stereotypical response regardless of the density of elements in the flow. This highlights local specialization of the eye and suggests the lower region may play a more critical role in translational flight stabilization.

Adaptive temporal processing of odor stimuli

The olfactory system translates chemical signals into neuronal signals that inform behavioral decisions of the animal. Odors are cues for source identity, but if monitored long enough, they can also be used to localize the source. Odor representations should therefore be robust to changing conditions and flexible in order to drive an appropriate behavior. In this review, we aim at discussing the main computations that allow robust and flexible encoding of odor information in the olfactory neural pathway.

Seeing Natural Images through the Eye of a Fly with Remote Focusing Two-Photon Microscopy

Visual systems of many animals, including the fruit fly Drosophila, represent the surrounding space as 2D maps, formed by populations of neurons. Advanced genetic tools make the fly visual system especially well accessible. However, in typical in vivo preparations for two-photon calcium imaging, relatively few neurons can be recorded at the same time. Here, we present an extension to a conventional two-photon microscope, based on remote focusing, which enables real-time rotation of the imaging plane, and thus flexible alignment to cellular structures, without resolution or speed trade-off. We simultaneously record from over 100 neighboring cells spanning the 2D retinotopic map. We characterize its representation of moving natural images, which we find is comparable to noise predictions. Our method increases throughput 10-fold and allows us to visualize a significant fraction of the fly's visual field. Furthermore, our system can be applied in general for a more flexible investigation of neural circuits.

Resource-efficient bio-inspired visual processing on the hexapod walking robot HECTOR

Emulating the highly resource-efficient processing of visual motion information in the brain of flying insects, a bio-inspired controller for collision avoidance and navigation was implemented on a novel, integrated System-on-Chip-based hardware module. The hardware module is used to control visually-guided navigation behavior of the stick insect-like hexapod robot HECTOR. By leveraging highly parallelized bio-inspired algorithms to extract nearness information from visual motion in dynamically reconfigurable logic, HECTOR is able to navigate to predefined goal positions without colliding with obstacles. The system drastically outperforms CPU- and graphics card-based implementations in terms of speed and resource efficiency, making it suitable to be also placed on fast moving robots, such as flying drones.

Contrast sensitivity and visual acuity of Queensland fruit flies (Bactrocera tryoni)

This study examines the visual acuity of Queensland fruit flies (Bactrocera tryoni) by analysing their turning responses to an immersive visual stimulus consisting of a pattern of vertical stripes presented at various angular periods and rotational rates. The results infer that these flies possess an interommatidial angle of approximately [Formula: see text], and an ommatidial acceptance angle of approximately [Formula: see text]. This suggests that the visual acuity of Queensland fruit flies is substantially better than that of the classical vinegar fly (Drosophila melanogaster), and is comparable to those of the housefly (Musca domestica) and the honeybee (Apis mellifera). The contrast sensitivity of Queensland fruit flies is comparable to that of the housefly.

Luminance Information Is Required for the Accurate Estimation of Contrast in Rapidly Changing Visual Contexts

Visual perception scales with changes in the visual stimulus, or contrast, irrespective of background illumination. However, visual perception is challenged when adaptation is not fast enough to deal with sudden declines in overall illumination, for example, when gaze follows a moving object from bright sunlight into a shaded area. Here, we show that the visual system of the fly employs a solution by propagating a corrective luminance-sensitive signal. We use in vivo 2-photon imaging and behavioral analyses to demonstrate that distinct OFF-pathway inputs encode contrast and luminance. Predictions of contrast-sensitive neuronal responses show that contrast information alone cannot explain behavioral responses in sudden dim light. The luminance-sensitive pathway via the L3 neuron is required for visual processing in such rapidly changing light conditions, ensuring contrast constancy when pure contrast sensitivity underestimates a stimulus. Thus, retaining a peripheral feature, luminance, in visual processing is required for robust behavioral responses.

A genetic, genomic, and computational resource for exploring neural circuit function

The anatomy of many neural circuits is being characterized with increasing resolution, but their molecular properties remain mostly unknown. Here, we characterize gene expression patterns in distinct neural cell types of the Drosophila visual system using genetic lines to access individual cell types, the TAPIN-seq method to measure their transcriptomes, and a probabilistic method to interpret these measurements. We used these tools to build a resource of high-resolution transcriptomes for 100 driver lines covering 67 cell types, available at http://www.opticlobe.com. Combining these transcriptomes with recently reported connectomes helps characterize how information is transmitted and processed across a range of scales, from individual synapses to circuit pathways. We describe examples that include identifying neurotransmitters, including cases of apparent co-release, generating functional hypotheses based on receptor expression, as well as identifying strong commonalities between different cell types.

Distinct expression of potassium channels regulates visual response properties of lamina neurons in Drosophila melanogaster

The computational organization of sensory systems depends on the diversification of individual cell types with distinct signal-processing capabilities. The Drosophila visual system, for instance, splits information into channels with different temporal properties directly downstream of photoreceptors in the first-order interneurons of the OFF pathway, L2 and L3. However, the biophysical mechanisms that determine this specialization are largely unknown. Here, we show that the voltage-gated K_a channels Shaker and Shal contribute to the response properties of the major OFF pathway input L2. L3 calcium response kinetics postsynaptic to photoreceptors resemble the sustained calcium signals of photoreceptors, whereas L2 neurons decay transiently. Based on a cell-type-specific RNA-seq data set and endogenous protein tagging, we identified Shaker and Shal as the primary candidates to shape L2 responses. Using in vivo two-photon imaging of L2 calcium signals in combination with pharmacological and genetic perturbations of these K_a channels, we show that the wild-type Shaker and Shal function is to enhance L2 responses and cell-autonomously sharpen L2 kinetics. Our results reveal a role for K_a channels in determining the signal-processing characteristics of a specific cell type in the visual system.

The impact of artificial light at night on nocturnal insects: A review and synthesis

In recent decades, advances in lighting technology have precipitated exponential increases in night sky brightness worldwide, raising concerns in the scientific community about the impact of artificial light at night (ALAN) on crepuscular and nocturnal biodiversity. Long-term records show that insect abundance has declined significantly over this time, with worrying implications for terrestrial ecosystems. The majority of investigations into the vulnerability of nocturnal insects to artificial light have focused on the flight-to-light behavior exhibited by select insect families. However, ALAN can affect insects in other ways as well. This review proposes five categories of ALAN impact on nocturnal insects, highlighting past research and identifying key knowledge gaps. We conclude with a summary of relevant literature on bioluminescent fireflies, which emphasizes the unique vulnerability of terrestrial light-based communication systems to artificial illumination. Comprehensive understanding of the ecological impacts of ALAN on diverse nocturnal insect taxa will enable researchers to seek out methods whereby fireflies, moths, and other essential members of the nocturnal ecosystem can coexist with humans on an increasingly urbanized planet.

Flying Drosophilamelanogaster maintain arbitrary but stable headings relative to the angle of polarized light

Animals must use external cues to maintain a straight course over long distances. In this study, we investigated how the fruit fly Drosophila melanogaster selects and maintains a flight heading relative to the axis of linearly polarized light, a visual cue produced by the atmospheric scattering of sunlight. To track flies' headings over extended periods, we used a flight simulator that coupled the angular velocity of dorsally presented polarized light to the stroke amplitude difference of the animals' wings. In the simulator, most flies actively maintained a stable heading relative to the axis of polarized light for the duration of 15 min flights. We found that individuals selected arbitrary, unpredictable headings relative to the polarization axis, which demonstrates that D. melanogaster can perform proportional navigation using a polarized light pattern. When flies flew in two consecutive bouts separated by a 5 min gap, the two flight headings were correlated, suggesting individuals retain a memory of their chosen heading. We found that adding a polarized light pattern to a light intensity gradient enhanced flies' orientation ability, suggesting D. melanogaster use a combination of cues to navigate. For both polarized light and intensity cues, flies' capacity to maintain a stable heading gradually increased over several minutes from the onset of flight. Our findings are consistent with a model in which each individual initially orients haphazardly but then settles on a heading which is maintained via a self-reinforcing process. This may be a general dispersal strategy for animals with no target destination.

Local motion adaptation enhances the representation of spatial structure at EMD arrays

Neuronal representation and extraction of spatial information are essential for behavioral control. For flying insects, a plausible way to gain spatial information is to exploit distance-dependent optic flow that is generated during translational self-motion. Optic flow is computed by arrays of local motion detectors retinotopically arranged in the second neuropile layer of the insect visual system. These motion detectors have adaptive response characteristics, i.e. their responses to motion with a constant or only slowly changing velocity decrease, while their sensitivity to rapid velocity changes is maintained or even increases. We analyzed by a modeling approach how motion adaptation affects signal representation at the output of arrays of motion detectors during simulated flight in artificial and natural 3D environments. We focused on translational flight, because spatial information is only contained in the optic flow induced by translational locomotion. Indeed, flies, bees and other insects segregate their flight into relatively long intersaccadic translational flight sections interspersed with brief and rapid saccadic turns, presumably to maximize periods of translation (80% of the flight). With a novel adaptive model of the insect visual motion pathway we could show that the motion detector responses to background structures of cluttered environments are largely attenuated as a consequence of motion adaptation, while responses to foreground objects stay constant or even increase. This conclusion even holds under the dynamic flight conditions of insects.

Parallel Computations in Insect and Mammalian Visual Motion Processing

Sensory systems use receptors to extract information from the environment and neural circuits to perform subsequent computations. These computations may be described as algorithms composed of sequential mathematical operations. Comparing these operations across taxa reveals how different neural circuits have evolved to solve the same problem, even when using different mechanisms to implement the underlying math. In this review, we compare how insect and mammalian neural circuits have solved the problem of motion estimation, focusing on the fruit fly Drosophila and the mouse retina. Although the two systems implement computations with grossly different anatomy and molecular mechanisms, the underlying circuits transform light into motion signals with strikingly similar processing steps. These similarities run from photoreceptor gain control and spatiotemporal tuning to ON and OFF pathway structures, motion detection, and computed motion signals. The parallels between the two systems suggest that a limited set of algorithms for estimating motion satisfies both the needs of sighted creatures and the constraints imposed on them by metabolism, anatomy, and the structure and regularities of the visual world.

Photoreceptor signalling is sufficient to explain the detectability threshold of insect aerial pursuers

An essential biological task for many flying insects is the detection of small, moving targets, such as when pursuing prey or conspecifics. Neural pathways underlying such 'target-detecting' behaviours have been investigated for their sensitivity and tuning properties (size, velocity). However, which stage of neuronal processing limits target detection is not yet known. Here, we investigated several skilled, aerial pursuers (males of four insect species), measuring the target-detection limit (signal-to-noise ratio) of light-adapted photoreceptors. We recorded intracellular responses to moving targets of varying size, extended well below the nominal resolution of single ommatidia. We found that the signal detection limit (2× photoreceptor noise) matches physiological or behavioural target-detection thresholds observed in each species. Thus, across a diverse range of flying insects, individual photoreceptor responses to changes in light intensity establish the sensitivity of the feature detection pathway, indicating later stages of processing are dedicated to feature tuning, tracking and selection.

Ontogenetic adaptations in the visual systems of deep-sea crustaceans

For all visually competent organisms, the driving force behind the adaptation of photoreceptors involves obtaining the best balance of resolution to sensitivity in the prevailing light regime, as an increase in sensitivity often results in a decrease in resolution. A number of marine species have an additional problem to deal with, in that the juvenile stages live in relatively brightly lit shallow (100-200 m depth) waters, whereas the adult stages have daytime depths of more than 600 m, where little downwelling light remains. Here, I present the results of electrophysiological analyses of the temporal resolution and irradiance sensitivity of juvenile and adult stages of two species of ontogenetically migrating crustaceans (Gnathophausia ingens and Systellaspis debilis) that must deal with dramatically different light environments and temperatures during their life histories. The results demonstrate that there are significant effects of temperature on temporal resolution, which help to optimize the visual systems of the two life-history stages for their respective light environments.This article is part of the themed issue 'Vision in dim light'.

Microsaccadic sampling of moving image information provides Drosophila hyperacute vision

Small fly eyes should not see fine image details. Because flies exhibit saccadic visual behaviors and their compound eyes have relatively few ommatidia (sampling points), their photoreceptors would be expected to generate blurry and coarse retinal images of the world. Here we demonstrate that Drosophila see the world far better than predicted from the classic theories. By using electrophysiological, optical and behavioral assays, we found that R1-R6 photoreceptors’ encoding capacity in time is maximized to fast high-contrast bursts, which resemble their light input during saccadic behaviors. Whilst over space, R1-R6s resolve moving objects at saccadic speeds beyond the predicted motion-blur-limit. Our results show how refractory phototransduction and rapid photomechanical photoreceptor contractions jointly sharpen retinal images of moving objects in space-time, enabling hyperacute vision, and explain how such microsaccadic information sampling exceeds the compound eyes’ optical limits. These discoveries elucidate how acuity depends upon photoreceptor function and eye movements.

Fruit flies have five eyes: two large compound eyes which support vision, plus three smaller single lens eyes which are used for navigation. Each compound eye monitors 180° of space and consists of roughly 750 units, each containing eight light-sensitive cells called photoreceptors. This relatively wide spacing of photoreceptors is thought to limit the sharpness, or acuity, of vision in fruit flies. The area of the human retina (the light-sensitive surface at back of our eyes) that generates our sharpest vision contains photoreceptors that are 500 times more densely packed.

Despite their differing designs, human and fruit fly eyes work via the same general principles. If we, or a fruit fly, were to hold our gaze completely steady, the world would gradually fade from view as the eye adapted to the unchanging visual stimulus. To ensure this does not happen, animals continuously make rapid, automatic eye movements called microsaccades. These refresh the image on the retina and prevent it from fading. Yet it is not known why do they not also cause blurred vision.

Standard accounts of vision assume that the retina and the brain perform most of the information processing required, with photoreceptors simply detecting how much light enters the eye. However, Juusola, Dau, Song et al. now challenge this idea by showing that photoreceptors are specially adapted to detect the fluctuating patterns of light that enter the eye as a result of microsaccades. Moreover, fruit fly eyes resolve small moving objects far better than would be predicted based on the spacing of their photoreceptors.

The discovery that photoreceptors are well adapted to deal with eye movements changes our understanding of insect vision. The findings also disprove the 100-year-old dogma that the spacing of photoreceptors limits the sharpness of vision in compound eyes. Further studies are required to determine whether photoreceptors in the retinas of other animals, including humans, have similar properties.

Olfactory receptor neurons use gain control and complementary kinetics to encode intermittent odorant stimuli

Insects find food and mates by navigating odorant plumes that can be highly intermittent, with intensities and durations that vary rapidly over orders of magnitude. Much is known about olfactory responses to pulses and steps, but it remains unclear how olfactory receptor neurons (ORNs) detect the intensity and timing of natural stimuli, where the absence of scale in the signal makes detection a formidable olfactory task. By stimulating Drosophila ORNs in vivo with naturalistic and Gaussian stimuli, we show that ORNs adapt to stimulus mean and variance, and that adaptation and saturation contribute to naturalistic sensing. Mean-dependent gain control followed the Weber-Fechner relation and occurred primarily at odor transduction, while variance-dependent gain control occurred at both transduction and spiking. Transduction and spike generation possessed complementary kinetic properties, that together preserved the timing of odorant encounters in ORN spiking, regardless of intensity. Such scale-invariance could be critical during odor plume navigation.

Static and Dynamic Adaptation of Insect Photoreceptor Responses to Naturalistic Stimuli

We describe a new nonlinear dynamic model of insect phototransduction using a NLN (nonlinear, linear, nonlinear) block structure. The first nonlinear stage provides a single exponential decline in gain and mean following the start of light stimulation. The linear stage uses a two-parameter log-normal convolution model previously applied alone to insect photoreceptors. The final stage is a static quadratic function. The model fitted current and voltage responses of isolated single photoreceptors from three different insect species with reasonable fidelity when they were stimulated by naturalistic time series having wide bandwidth and contrast, over a light intensity range of >1:10⁴. Mean squared error values for receptor current and receptor potential varied over ~2–60%, with many values below 10%. Linear log-normal filter parameters did not vary strongly with species or light intensity. Initial gain reduction was only large for the highest light levels, while the time constant of gain and mean reduction decreased with light intensity. The final nonlinearity changed from positively to negatively quadratic with increasing light intensity, indicating a change from threshold, or expansion to saturating compression with greater signal strength. Photoreceptor information transmission was estimated by linear information capacity and signal entropy measurements of both experimental data and predicted outputs of the model for identical stimuli at each light level. Comparison of actual and predicted data indicated significant added noise during phototransduction, with information being progressively lost by nonlinear behavior with increasing light intensity.

Peripheral Processing Facilitates Optic Flow-Based Depth Perception

Flying insects, such as flies or bees, rely on consistent information regarding the depth structure of the environment when performing their flight maneuvers in cluttered natural environments. These behaviors include avoiding collisions, approaching targets or spatial navigation. Insects are thought to obtain depth information visually from the retinal image displacements ("optic flow") during translational ego-motion. Optic flow in the insect visual system is processed by a mechanism that can be modeled by correlation-type elementary motion detectors (EMDs). However, it is still an open question how spatial information can be extracted reliably from the responses of the highly contrast- and pattern-dependent EMD responses, especially if the vast range of light intensities encountered in natural environments is taken into account. This question will be addressed here by systematically modeling the peripheral visual system of flies, including various adaptive mechanisms. Different model variants of the peripheral visual system were stimulated with image sequences that mimic the panoramic visual input during translational ego-motion in various natural environments, and the resulting peripheral signals were fed into an array of EMDs. We characterized the influence of each peripheral computational unit on the representation of spatial information in the EMD responses. Our model simulations reveal that information about the overall light level needs to be eliminated from the EMD input as is accomplished under light-adapted conditions in the insect peripheral visual system. The response characteristics of large monopolar cells (LMCs) resemble that of a band-pass filter, which reduces the contrast dependency of EMDs strongly, effectively enhancing the representation of the nearness of objects and, especially, of their contours. We furthermore show that local brightness adaptation of photoreceptors allows for spatial vision under a wide range of dynamic light conditions.

Rapid Sensory Adaptation Redux: A Circuit Perspective

Adaptation is fundamental to life. All organisms adapt over timescales that span from evolution to generations and lifetimes to moment-by-moment interactions. The nervous system is particularly adept at rapidly adapting to change, and this in fact may be one of its fundamental principles of organization and function. Rapid forms of sensory adaptation have been well documented across all sensory modalities in a wide range of organisms, yet we do not have a comprehensive understanding of the adaptive cellular mechanisms that ultimately give rise to the corresponding percepts, due in part to the complexity of the circuitry. In this Perspective, we aim to build links between adaptation at multiple scales of neural circuitry by investigating the differential adaptation across brain regions and sub-regions and across specific cell types, for which the explosion of modern tools has just begun to enable. This investigation points to a set of challenges for the field to link functional observations to adaptive properties of the neural circuit that ultimately underlie percepts.

Current advances in invertebrate vision: insights from patch-clamp studies of photoreceptors in apposition eyes

Traditional electrophysiological research on invertebrate photoreceptors has been conducted in vivo, using intracellular recordings from intact compound eyes. The only exception used to be Drosophila melanogaster, which was exhaustively studied by both intracellular recording and patch-clamp methods. Recently, several patch-clamp studies have provided new information on the biophysical properties of photoreceptors of diverse insect species, having both apposition and neural superposition eyes, in the contexts of visual ecology, behavior, and ontogenesis. Here, I discuss these and other relevant results, emphasizing differences between fruit flies and other species, between photoreceptors of diurnal and nocturnal insects, properties of distinct functional types of photoreceptors, postembryonic developmental changes, and relationships between voltage-gated potassium channels and visual ecology.

Frequency-selective transmission of graded signals in large monopolar neurons of blowfly Calliphora vicina compound eye

The functional roles of voltage-gated K(+)(Kv) channels in visual system interneurons remain poorly studied. We have addressed this problem in the large monopolar cells (LMCs) of the blowfly Calliphora vicina, using intracellular recordings and mathematical modeling methods. Intracellular recordings were performed in two cellular compartments: the synaptic zone, which receives input from photoreceptors, and the axon, which provides graded potential output to the third-order visual neurons. Biophysical properties of Kv conductances in the physiological voltage range were examined in the dark with injections of current in the discontinuous current-clamp mode. Putative LMC types 1/2 and 3 (L1/2 and L3, respectively) had dissimilar Kv channelomes: L1/2 displayed a prominent inactivating Kv conductance in the axon, while L3 cells were characterized by a sustained delayed-rectifier Kv conductance. To study the propagation of voltage signals, the data were incorporated into the previously developed mathematical model. We demonstrate that the complex interaction between the passive membrane properties, Kv conductances, and the neuronal geometry leads to a resonance-like filtering of signals with peak frequencies of transmission near 15 and 40 Hz for L3 and L1/2, respectively. These results point to distinct physiological roles of different types of LMCs.

A higher order visual neuron tuned to the spatial amplitude spectra of natural scenes

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.

A neural mechanism for time-window separation resolves ambiguity of adaptive coding

The senses of animals are confronted with changing environments and different contexts. Neural adaptation is one important tool to adjust sensitivity to varying intensity ranges. For instance, in a quiet night outdoors, our hearing is more sensitive than when we are confronted with the plurality of sounds in a large city during the day. However, adaptation also removes available information on absolute sound levels and may thus cause ambiguity. Experimental data on the trade-off between benefits and loss through adaptation is scarce and very few mechanisms have been proposed to resolve it. We present an example where adaptation is beneficial for one task—namely, the reliable encoding of the pattern of an acoustic signal—but detrimental for another—the localization of the same acoustic stimulus. With a combination of neurophysiological data, modeling, and behavioral tests, we show that adaptation in the periphery of the auditory pathway of grasshoppers enables intensity-invariant coding of amplitude modulations, but at the same time, degrades information available for sound localization. We demonstrate how focusing the response of localization neurons to the onset of relevant signals separates processing of localization and pattern information temporally. In this way, the ambiguity of adaptive coding can be circumvented and both absolute and relative levels can be processed using the same set of peripheral neurons.

Neuronal data, computational modeling, and behavioral experiments reveal how the conflict between sensory adaptation and sound localization is resolved in the grasshopper auditory system, allowing processing of both absolute and relative sound levels.

Smell, vision, hearing—virtually all of our senses adapt their sensitivity to cope with the varying environment. Adaptation removes information about absolute stimulus intensity available to the brain, as this information is usually of little relevance for sensory representation. For some tasks, however, knowledge of absolute stimulus intensities is essential. How sensory pathways cope with this conflict remains an open question. We addressed this question in the grasshopper auditory system, in which comparison of absolute intensities of conspecific calls at both ears is crucial for mate localization. We recorded activity from three levels in the auditory pathway, showing that adaptation in the peripheral auditory system indeed removes information about absolute intensities. We discovered that strong negative feedback restricts coding of sound direction in the central auditory system to the very beginning of a stimulus, when peripheral adaptation has not yet acted. By using a computational model, we show that this central mechanism enables localization of the sound source over a wide range of stimulus intensities and that its time course is well matched to the time course of peripheral adaptation. In a final step, we confirmed predictions from our model in behavioral experiments on sound localization.

A bio-inspired analog silicon retina with Michaelis-Menten auto-adaptive pixels sensitive to small and large changes in light

In this paper, we present: (i) a novel analog silicon retina featuring auto-adaptive pixels that obey the Michaelis-Menten law, i.e. V=V(m) I(n)/I(n)+σ(n); (ii) a method of characterizing silicon retinas, which makes it possible to accurately assess the pixels' response to transient luminous changes in a ±3-decade range, as well as changes in the initial steady-state intensity in a 7-decade range. The novel pixel, called M(2)APix, which stands for Michaelis-Menten Auto-Adaptive Pixel, can auto-adapt in a 7-decade range and responds appropriately to step changes up to ±3 decades in size without causing any saturation of the Very Large Scale Integration (VLSI) transistors. Thanks to the intrinsic properties of the Michaelis-Menten equation, the pixel output always remains within a constant limited voltage range. The range of the Analog to Digital Converter (ADC) was therefore adjusted so as to obtain a Least Significant Bit (LSB) voltage of 2.35mV and an effective resolution of about 9 bits. The results presented here show that the M(2)APix produced a quasi-linear contrast response once it had adapted to the average luminosity. Differently to what occurs in its biological counterparts, neither the sensitivity to changes in light nor the contrast response of the M(2)APix depend on the mean luminosity (i.e. the ambient lighting conditions). Lastly, a full comparison between the M(2)APix and the Delbrück auto-adaptive pixel is provided.

Computation of object approach by a system of visual motion-sensitive neurons in the crab Neohelice

Similar to most visual animals, crabs perform proper avoidance responses to objects directly approaching them. The monostratified lobula giant neurons of type 1 (MLG1) of crabs constitute an ensemble of 14-16 bilateral pairs of motion-detecting neurons projecting from the lobula (third optic neuropile) to the midbrain, with receptive fields that are distributed over the extensive visual field of the animal's eye. Considering the crab Neohelice (previously Chasmagnathus) granulata, here we describe the response of these neurons to looming stimuli that simulate objects approaching the animal on a collision course. We found that the peak firing time of MLG1 acts as an angular threshold detector signaling, with a delay of δ = 35 ms, the time at which an object reaches a fixed angular threshold of 49°. Using in vivo intracellular recordings, we detected the existence of excitatory and inhibitory synaptic currents that shape the neural response. Other functional features identified in the MLG1 neurons were phasic responses at the beginning of the approach, a relation between the stimulus angular velocity and the excitation delay, and a mapping between membrane potential and firing frequency. Using this information, we propose a biophysical model of the mechanisms that regulate the encoding of looming stimuli. Furthermore, we found that the parameter encoded by the MLG1 firing frequency during the approach is the stimulus angular velocity. The proposed model fits the experimental results and predicts the neural response to a qualitatively different stimulus. Based on these and previous results, we propose that the MLG1 neuron system acts as a directional coding system for collision avoidance.

Cellular adaptation facilitates sparse and reliable coding in sensory pathways

Most neurons in peripheral sensory pathways initially respond vigorously when a preferred stimulus is presented, but adapt as stimulation continues. It is unclear how this phenomenon affects stimulus coding in the later stages of sensory processing. Here, we show that a temporally sparse and reliable stimulus representation develops naturally in sequential stages of a sensory network with adapting neurons. As a modeling framework we employ a mean-field approach together with an adaptive population density treatment, accompanied by numerical simulations of spiking neural networks. We find that cellular adaptation plays a critical role in the dynamic reduction of the trial-by-trial variability of cortical spike responses by transiently suppressing self-generated fast fluctuations in the cortical balanced network. This provides an explanation for a widespread cortical phenomenon by a simple mechanism. We further show that in the insect olfactory system cellular adaptation is sufficient to explain the emergence of the temporally sparse and reliable stimulus representation in the mushroom body. Our results reveal a generic, biophysically plausible mechanism that can explain the emergence of a temporally sparse and reliable stimulus representation within a sequential processing architecture.

Contributions of the 12 neuron classes in the fly lamina to motion vision

Motion detection is a fundamental neural computation performed by many sensory systems. In the fly, local motion computation is thought to occur within the first two layers of the visual system, the lamina and medulla. We constructed specific genetic driver lines for each of the 12 neuron classes in the lamina. We then depolarized and hyperpolarized each neuron type and quantified fly behavioral responses to a diverse set of motion stimuli. We found that only a small number of lamina output neurons are essential for motion detection, while most neurons serve to sculpt and enhance these feedforward pathways. Two classes of feedback neurons (C2 and C3), and lamina output neurons (L2 and L4), are required for normal detection of directional motion stimuli. Our results reveal a prominent role for feedback and lateral interactions in motion processing and demonstrate that motion-dependent behaviors rely on contributions from nearly all lamina neuron classes.

GABAergic lateral interactions tune the early stages of visual processing in Drosophila

Early stages of visual processing must capture complex, dynamic inputs. While peripheral neurons often implement efficient encoding by exploiting natural stimulus statistics, downstream neurons are specialized to extract behaviorally relevant features. How do these specializations arise? We use two-photon imaging in Drosophila to characterize a first-order interneuron, L2, that provides input to a pathway specialized for detecting moving dark edges. GABAergic interactions, mediated in part presynaptically, create an antagonistic and anisotropic center-surround receptive field. This receptive field is spatiotemporally coupled, applying differential temporal processing to large and small dark objects, achieving significant specialization. GABAergic circuits also mediate OFF responses and balance these with responses to ON stimuli. Remarkably, the functional properties of L2 are strikingly similar to those of bipolar cells, yet emerge through different molecular and circuit mechanisms. Thus, evolution appears to have converged on a common strategy for processing visual information at the first synapse.

Fixational eye movements in the earliest stage of metazoan evolution

All known photoreceptor cells adapt to constant light stimuli, fading the retinal image when exposed to an immobile visual scene. Counter strategies are therefore necessary to prevent blindness, and in mammals this is accomplished by fixational eye movements. Cubomedusae occupy a key position for understanding the evolution of complex visual systems and their eyes are assumedly subject to the same adaptive problems as the vertebrate eye, but lack motor control of their visual system. The morphology of the visual system of cubomedusae ensures a constant orientation of the eyes and a clear division of the visual field, but thereby also a constant retinal image when exposed to stationary visual scenes. Here we show that bell contractions used for swimming in the medusae refresh the retinal image in the upper lens eye of Tripedalia cystophora. This strongly suggests that strategies comparable to fixational eye movements have evolved at the earliest metazoan stage to compensate for the intrinsic property of the photoreceptors. Since the timing and amplitude of the rhopalial movements concur with the spatial and temporal resolution of the eye it circumvents the need for post processing in the central nervous system to remove image blur.

Visual motion speed determines a behavioral switch from forward flight to expansion avoidance in Drosophila

As an animal translates through the world, its eyes will experience a radiating pattern of optic flow in which there is a focus of expansion directly in front and a focus of contraction behind. For flying fruit flies, recent experiments indicate that flies actively steer away from patterns of expansion. Whereas such a reflex makes sense for avoiding obstacles, it presents a paradox of sorts because an insect could not navigate stably through a visual scene unless it tolerated flight towards a focus of expansion during episodes of forward translation. One possible solution to this paradox is that a fly's behavior might change such that it steers away from strong expansion, but actively steers towards weak expansion. In this study, we use a tethered flight arena to investigate the influence of stimulus strength on the magnitude and direction of turning responses to visual expansion in flies. These experiments indicate that the expansion-avoidance behavior is speed dependent. At slower speeds of expansion, flies exhibit an attraction to the focus of expansion, whereas the behavior transforms to expansion avoidance at higher speeds. Open-loop experiments indicate that this inversion of the expansion-avoidance response depends on whether or not the head is fixed to the thorax. The inversion of the expansion-avoidance response with stimulus strength has a clear manifestation under closed-loop conditions. Flies will actively orient towards a focus of expansion at low temporal frequency but steer away from it at high temporal frequency. The change in the response with temporal frequency does not require motion stimuli directly in front or behind the fly. Animals in which the stimulus was presented within 120 deg sectors on each side consistently steered towards expansion at low temporal frequency and steered towards contraction at high temporal frequency. A simple model based on an array of Hassenstein-Reichardt type elementary movement detectors suggests that the inversion of the expansion-avoidance reflex can explain the spatial distribution of straight flight segments and collision-avoidance saccades when flies fly freely within an open circular arena.

Image sensor system with bio-inspired efficient coding and adaptation

We designed and implemented an image sensor system equipped with three bio-inspired coding and adaptation strategies: logarithmic transform, local average subtraction, and feedback gain control. The system comprises a field-programmable gate array (FPGA), a resistive network, and active pixel sensors (APS), whose light intensity-voltage characteristics are controllable. The system employs multiple time-varying reset voltage signals for APS in order to realize multiple logarithmic intensity-voltage characteristics, which are controlled so that the entropy of the output image is maximized. The system also employs local average subtraction and gain control in order to obtain images with an appropriate contrast. The local average is calculated by the resistive network instantaneously. The designed system was successfully used to obtain appropriate images of objects that were subjected to large changes in illumination.

Stochastic, adaptive sampling of information by microvilli in fly photoreceptors

Background

In fly photoreceptors, light is focused onto a photosensitive waveguide, the rhabdomere, consisting of tens of thousands of microvilli. Each microvillus is capable of generating elementary responses, quantum bumps, in response to single photons using a stochastically operating phototransduction cascade. Whereas much is known about the cascade reactions, less is known about how the concerted action of the microvilli population encodes light changes into neural information and how the ultrastructure and biochemical machinery of photoreceptors of flies and other insects evolved in relation to the information sampling and processing they perform.

Results

We generated biophysically realistic fly photoreceptor models, which accurately simulate the encoding of visual information. By comparing stochastic simulations with single cell recordings from Drosophila photoreceptors, we show how adaptive sampling by 30,000 microvilli captures the temporal structure of natural contrast changes. Following each bump, individual microvilli are rendered briefly (∼100–200 ms) refractory, thereby reducing quantum efficiency with increasing intensity. The refractory period opposes saturation, dynamically and stochastically adjusting availability of microvilli (bump production rate: sample rate), whereas intracellular calcium and voltage adapt bump amplitude and waveform (sample size). These adapting sampling principles result in robust encoding of natural light changes, which both approximates perceptual contrast constancy and enhances novel events under different light conditions, and predict information processing across a range of species with different visual ecologies.

Conclusions

These results clarify why fly photoreceptors are structured the way they are and function as they do, linking sensory information to sensory evolution and revealing benefits of stochasticity for neural information processing.

Understanding auditory spectro-temporal receptive fields and their changes with input statistics by efficient coding principles

Spectro-temporal receptive fields (STRFs) have been widely used as linear approximations to the signal transform from sound spectrograms to neural responses along the auditory pathway. Their dependence on statistical attributes of the stimuli, such as sound intensity, is usually explained by nonlinear mechanisms and models. Here, we apply an efficient coding principle which has been successfully used to understand receptive fields in early stages of visual processing, in order to provide a computational understanding of the STRFs. According to this principle, STRFs result from an optimal tradeoff between maximizing the sensory information the brain receives, and minimizing the cost of the neural activities required to represent and transmit this information. Both terms depend on the statistical properties of the sensory inputs and the noise that corrupts them. The STRFs should therefore depend on the input power spectrum and the signal-to-noise ratio, which is assumed to increase with input intensity. We analytically derive the optimal STRFs when signal and noise are approximated as Gaussians. Under the constraint that they should be spectro-temporally local, the STRFs are predicted to adapt from being band-pass to low-pass filters as the input intensity reduces, or the input correlation becomes longer range in sound frequency or time. These predictions qualitatively match physiological observations. Our prediction as to how the STRFs should be determined by the input power spectrum could readily be tested, since this spectrum depends on the stimulus ensemble. The potentials and limitations of the efficient coding principle are discussed.