Visual pattern recognition in Drosophila is invariant for retinal position

Contiguity in perception: origins in cellular associative computations

Our brains are good at detecting and learning associative structures; according to some linguistic theories, this capacity even constitutes a prerequisite for the development of syntax and compositionality in language and verbalized thought. I will argue that the search for associative motifs in input patterns is an evolutionary old brain function that enables contiguity in sensory perception and orientation in time and space. It has its origins in an elementary material property of cells that is particularly evident at chemical synapses: input-assigned calcium influx that activates calcium sensor proteins involved in memory storage. This machinery for the detection and learning of associative motifs generates knowledge about input relationships and integrates this knowledge into existing networks through updates in connectivity patterns.

Whole-brain annotation and multi-connectome cell typing quantifies circuit stereotypy in Drosophila

The fruit fly Drosophila melanogaster combines surprisingly sophisticated behaviour with a highly tractable nervous system. A large part of the fly's success as a model organism in modern neuroscience stems from the concentration of collaboratively generated molecular genetic and digital resources. As presented in our FlyWire companion paper 1 , this now includes the first full brain connectome of an adult animal. Here we report the systematic and hierarchical annotation of this ~130,000-neuron connectome including neuronal classes, cell types and developmental units (hemilineages). This enables any researcher to navigate this huge dataset and find systems and neurons of interest, linked to the literature through the Virtual Fly Brain database 2 . Crucially, this resource includes 4,552 cell types. 3,094 are rigorous consensus validations of cell types previously proposed in the hemibrain connectome 3 . In addition, we propose 1,458 new cell types, arising mostly from the fact that the FlyWire connectome spans the whole brain, whereas the hemibrain derives from a subvolume. Comparison of FlyWire and the hemibrain showed that cell type counts and strong connections were largely stable, but connection weights were surprisingly variable within and across animals. Further analysis defined simple heuristics for connectome interpretation: connections stronger than 10 unitary synapses or providing >1% of the input to a target cell are highly conserved. Some cell types showed increased variability across connectomes: the most common cell type in the mushroom body, required for learning and memory, is almost twice as numerous in FlyWire as the hemibrain. We find evidence for functional homeostasis through adjustments of the absolute amount of excitatory input while maintaining the excitation-inhibition ratio. Finally, and surprisingly, about one third of the cell types proposed in the hemibrain connectome could not yet be reliably identified in the FlyWire connectome. We therefore suggest that cell types should be defined to be robust to inter-individual variation, namely as groups of cells that are quantitatively more similar to cells in a different brain than to any other cell in the same brain. Joint analysis of the FlyWire and hemibrain connectomes demonstrates the viability and utility of this new definition. Our work defines a consensus cell type atlas for the fly brain and provides both an intellectual framework and open source toolchain for brain-scale comparative connectomics.

Classifying organisms and artefacts by their outline shapes

We often wish to classify objects by their shapes. Indeed, the study of shapes is an important part of many scientific fields, such as evolutionary biology, structural biology, image processing and archaeology. However, mathematical shape spaces are rather complicated and nonlinear. The most widely used methods of shape analysis, geometric morphometrics, treat the shapes as sets of points. Diffeomorphic methods consider the underlying curve rather than points, but have rarely been applied to real-world problems. Using a machine classifier, we tested the ability of several of these methods to describe and classify the shapes of a variety of organic and man-made objects. We find that one method, based on square-root velocity functions (SRVFs), outperforms all others, including a standard geometric morphometric method (eigenshapes), and that it is also superior to human experts using shape alone. When the SRVF approach is constrained to take account of homologous landmarks it can accurately classify objects of very different shapes. The SRVF method identifies a shortest path between shapes, and we show that this can be used to estimate the shapes of intermediate steps in evolutionary series. Diffeomorphic shape analysis methods, we conclude, now provide practical and effective solutions to many shape description and classification problems in the natural and human sciences.

Binocular mirror-symmetric microsaccadic sampling enables Drosophila hyperacute 3D vision

To move efficiently, animals must continuously work out their x,y,z positions with respect to real-world objects, and many animals have a pair of eyes to achieve this. How photoreceptors actively sample the eyes’ optical image disparity is not understood because this fundamental information-limiting step has not been investigated in vivo over the eyes’ whole sampling matrix. This integrative multiscale study will advance our current understanding of stereopsis from static image disparity comparison to a morphodynamic active sampling theory. It shows how photomechanical photoreceptor microsaccades enable Drosophila superresolution three-dimensional vision and proposes neural computations for accurately predicting these flies’ depth-perception dynamics, limits, and visual behaviors.

Neural mechanisms behind stereopsis, which requires simultaneous disparity inputs from two eyes, have remained mysterious. Here we show how ultrafast mirror-symmetric photomechanical contractions in the frontal forward-facing left and right eye photoreceptors give Drosophila superresolution three-dimensional (3D) vision. By interlinking multiscale in vivo assays with multiscale simulations, we reveal how these photoreceptor microsaccades—by verging, diverging, and narrowing the eyes’ overlapping receptive fields—channel depth information, as phasic binocular image motion disparity signals in time. We further show how peripherally, outside stereopsis, microsaccadic sampling tracks a flying fly’s optic flow field to better resolve the world in motion. These results change our understanding of how insect compound eyes work and suggest a general dynamic stereo-information sampling strategy for animals, robots, and sensors.

A neural algorithm for Drosophila linear and nonlinear decision-making

It has been evidenced that vision-based decision-making in Drosophila consists of both simple perceptual (linear) decision and value-based (non-linear) decision. This paper proposes a general computational spiking neural network (SNN) model to explore how different brain areas are connected contributing to Drosophila linear and nonlinear decision-making behavior. First, our SNN model could successfully describe all the experimental findings in fly visual reinforcement learning and action selection among multiple conflicting choices as well. Second, our computational modeling shows that dopaminergic neuron-GABAergic neuron-mushroom body (DA-GABA-MB) works in a recurrent loop providing a key circuit for gain and gating mechanism of nonlinear decision making. Compared with existing models, our model shows more biologically plausible on the network design and working mechanism, and could amplify the small differences between two conflicting cues more clearly. Finally, based on the proposed model, the UAV could quickly learn to make clear-cut decisions among multiple visual choices and flexible reversal learning resembling to real fly. Compared with linear and uniform decision-making methods, the DA-GABA-MB mechanism helps UAV complete the decision-making task with fewer steps.

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.

Information reduction in a reverberatory neuronal network through convergence to complex oscillatory firing patterns

Dynamics of a reverberating neural net is studied by means of computer simulation. The net, which is composed of 9 leaky integrate-and-fire (LIF) neurons arranged in a square lattice, is fully connected with interneuronal communication delay proportional to the corresponding distance. The network is initially stimulated with different stimuli and then goes freely. For each stimulus, in the course of free evolution, activity either dies out completely or the network converges to a periodic trajectory, which may be different for different stimuli. The latter is observed for a set of 285290 initial stimuli which constitutes 83% of all stimuli applied. After applying each stimulus from the set, 102 different periodic end-states are found. The conclusion is made, after analyzing the trajectories, that neuronal firing is the necessary prerequisite for merging different trajectories into a single one, which eventually transforms into a periodic regime. Observed phenomena of self-organization in the time domain are discussed as a possible model for processes taking place during perception. The repetitive firing in the periodic regimes could underpin memory formation.

Visual projection neurons in the Drosophila lobula link feature detection to distinct behavioral programs

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.

Visual Attention in Flies-Dopamine in the Mushroom Bodies Mediates the After-Effect of Cueing

Visual environments may simultaneously comprise stimuli of different significance. Often such stimuli require incompatible responses. Selective visual attention allows an animal to respond exclusively to the stimuli at a certain location in the visual field. In the process of establishing its focus of attention the animal can be influenced by external cues. Here we characterize the behavioral properties and neural mechanism of cueing in the fly Drosophila melanogaster. A cue can be attractive, repulsive or ineffective depending upon (e.g.) its visual properties and location in the visual field. Dopamine signaling in the brain is required to maintain the effect of cueing once the cue has disappeared. Raising or lowering dopamine at the synapse abolishes this after-effect. Specifically, dopamine is necessary and sufficient in the αβ-lobes of the mushroom bodies. Evidence is provided for an involvement of the αβ_posterior Kenyon cells.

Vision in Flies: Measuring the Attention Span

A visual stimulus at a particular location of the visual field may elicit a behavior while at the same time equally salient stimuli in other parts do not. This property of visual systems is known as selective visual attention (SVA). The animal is said to have a focus of attention (FoA) which it has shifted to a particular location. Visual attention normally involves an attention span at the location to which the FoA has been shifted. Here the attention span is measured in Drosophila. The fly is tethered and hence has its eyes fixed in space. It can shift its FoA internally. This shift is revealed using two simultaneous test stimuli with characteristic responses at their particular locations. In tethered flight a wild type fly keeps its FoA at a certain location for up to 4s. Flies with a mutation in the radish gene, that has been suggested to be involved in attention-like mechanisms, display a reduced attention span of only 1s.

Closed-Loop Behavioral Control Increases Coherence in the Fly Brain

A crucial function of the brain is to be able to distinguish whether or not changes in the environment are caused by one's own actions. Even the smallest brains appear to be capable of making this distinction, as has been shown by closed-loop behavioral experiments in flies controlling visual stimuli in virtual reality paradigms. We questioned whether activity in the fruit fly brain is different during such closed-loop behavior, compared with passive viewing of a stimulus. To address this question, we used a procedure to record local field potential (LFP) activity across the fly brain while flies were controlling a virtual object through their movement on an air-supported ball. The virtual object was flickered at a precise frequency (7 Hz), creating a frequency tag that allowed us to track brain responses to the object while animals were behaving. Following experiments under closed-loop control, we replayed the same stimulus to the fly in open loop, such that it could no longer control the stimulus. We found identical receptive fields and similar strength of frequency tags across the brain for the virtual object under closed loop and replay. However, when comparing central versus peripheral brain regions, we found that brain responses were differentially modulated depending on whether flies were in control or not. Additionally, coherence of LFP activity in the brain increased when flies were in control, compared with replay, even if motor behavior was similar. This suggests that processes associated with closed-loop control promote temporal coordination in the insect brain.

SIGNIFICANCE STATEMENT

We show that closed-loop control of a visual stimulus promotes temporal coordination across the Drosophila brain, compared with open-loop replay of the same visual sequences. This is significant because it suggests that, to understand goal-directed behavior or visual attention in flies, it may be most informative to sample neural activity from multiple regions across the brain simultaneously, and to examine temporal relationships (e.g., coherence) between these regions.

Using an abstract geometry in virtual reality to explore choice behaviour: visual flicker preferences in honeybees

Closed-loop paradigms provide an effective approach to studying visual choice behaviour and attention in small animals. Different flying and walking paradigms have been developed to investigate behavioural and neuronal responses to competing stimuli in insects such as bees and flies. However, the variety of stimulus choices that can be presented over one experiment is often limited. Current choice paradigms are mostly constrained as single binary choice scenarios that are influenced by the linear structure of classical conditioning paradigms. Here, we present a novel behavioural choice paradigm that allows animals to explore a closed geometry of interconnected binary choices by repeatedly selecting among competing objects, thereby revealing stimulus preferences in an historical context. We employed our novel paradigm to investigate visual flicker preferences in honeybees (Apis mellifera), and found significant preferences for 20-25Hz flicker and avoidance of higher (50-100Hz) and lower (2-4Hz) flicker frequencies. Similar results were found when bees were presented with three simultaneous choices instead of two, and when they were given the chance to select previously rejected choices. Our results show that honeybees can discriminate among different flicker frequencies, and that their visual preferences are persistent even under different experimental conditions. Interestingly, avoided stimuli were more attractive if they were novel, suggesting that novelty salience can override innate preferences. Our recursive virtual reality environment provides a new approach to studying visual discrimination and choice behaviour in behaving animals.

A conditioned visual orientation requires the ellipsoid body in Drosophila

Orientation, the spatial organization of animal behavior, is an essential faculty of animals. Bacteria and lower animals such as insects exhibit taxis, innate orientation behavior, directly toward or away from a directional cue. Organisms can also orient themselves at a specific angle relative to the cues. In this study, using Drosophila as a model system, we established a visual orientation conditioning paradigm based on a flight simulator in which a stationary flying fly could control the rotation of a visual object. By coupling aversive heat shocks to a fly's orientation toward one side of the visual object, we found that the fly could be conditioned to orientate toward the left or right side of the frontal visual object and retain this conditioned visual orientation. The lower and upper visual fields have different roles in conditioned visual orientation. Transfer experiments showed that conditioned visual orientation could generalize between visual targets of different sizes, compactness, or vertical positions, but not of contour orientation. Rut—Type I adenylyl cyclase and Dnc—phosphodiesterase were dispensable for visual orientation conditioning. Normal activity and scb signaling in R3/R4d neurons of the ellipsoid body were required for visual orientation conditioning. Our studies established a visual orientation conditioning paradigm and examined the behavioral properties and neural circuitry of visual orientation, an important component of the insect's spatial navigation.

Multiple redundant medulla projection neurons mediate color vision in Drosophila

The receptor mechanism for color vision has been extensively studied. In contrast, the circuit(s) that transform(s) photoreceptor signals into color percepts to guide behavior remain(s) poorly characterized. Using intersectional genetics to inactivate identified subsets of neurons, we have uncovered the first-order interneurons that are functionally required for hue discrimination in Drosophila. We developed a novel aversive operant conditioning assay for intensity-independent color discrimination (true color vision) in Drosophila. Single flying flies are magnetically tethered in an arena surrounded by blue and green LEDs (light-emitting diodes). The flies' optomotor response is used to determine the blue-green isoluminant intensity. Flies are then conditioned to discriminate between equiluminant blue or green stimuli. Wild-type flies are successfully trained in this paradigm when conditioned to avoid either blue or green. Functional color entrainment requires the function of the narrow-spectrum photoreceptors R8 and/or R7, and is within a limited range, intensity independent, suggesting that it is mediated by a color vision system. The medulla projection neurons, Tm5a/b/c and Tm20, receive direct inputs from R7 or R8 photoreceptors and indirect input from the broad-spectrum photoreceptors R1-R6 via the lamina neuron L3. Genetically inactivating these four classes of medulla projection neurons abolished color learning. However, inactivation of subsets of these neurons is insufficient to block color learning, suggesting that true color vision is mediated by multiple redundant pathways. We hypothesize that flies represent color along multiple axes at the first synapse in the fly visual system. The apparent redundancy in learned color discrimination sharply contrasts with innate ultraviolet (UV) spectral preference, which is dominated by a single pathway from the amacrine neuron Dm8 to the Tm5c projection neurons.

The relative roles of vision and chemosensation in mate recognition of Drosophila

Animals rely on sensory cues to classify objects in their environment and respond appropriately. However, the spatial structure of those sensory cues can greatly impact when, where, and how they are perceived. In this study, we examined the relative roles of visual and chemosensory cues in the mate recognition behavior of fruit flies (Drosophila melanogaster) by using a robotic fly dummy that was programmed to interact with individual males. By pairing male flies with dummies of various shapes, sizes, and speeds, or coated with different pheromones, we determined that visual and chemical cues play specific roles at different points in the courtship sequence. Vision is essential for determining whether to approach a moving object and initiate courtship, and males were more likely to begin chasing objects with the same approximate dimensions as another fly. However, whereas males were less likely to begin chasing larger dummies, once started, they would continue chasing for a similar length of time regardless of the dummy's shape. The presence of female pheromones on the moving dummy did not affect the probability that males would initiate a chase, but it did influence how long they would continue chasing. Male pheromone both inhibits chase initiation and shortens chase duration. Collectively, these results suggest that male Drosophila use different sensory cues to progress through the courtship sequence: visual cues are dominant when deciding whether to approach an object whereas chemosensory cues determine how long the male pursues its target.

Distinct acute zones for visual stimuli in different visual tasks in Drosophila

The fruit fly Drosophila melanogaster has a sophisticated visual system and exhibits complex visual behaviors. Visual responses, vision processing and higher cognitive processes in Drosophila have been studied extensively. However, little is known about whether the retinal location of visual stimuli can affect fruit fly performance in various visual tasks. We tested the response of wild-type Berlin flies to visual stimuli at several vertical locations. Three paradigms were used in our study: visual operant conditioning, visual object fixation and optomotor response. We observed an acute zone for visual feature memorization in the upper visual field when visual patterns were presented with a black background. However, when a white background was used, the acute zone was in the lower visual field. Similar to visual feature memorization, the best locations for visual object fixation and optomotor response to a single moving stripe were in the lower visual field with a white background and the upper visual field with a black background. The preferred location for the optomotor response to moving gratings was around the equator of the visual field. Our results suggest that different visual processing pathways are involved in different visual tasks and that there is a certain degree of overlap between the pathways for visual feature memorization, visual object fixation and optomotor response.

Fish in the matrix: motor learning in a virtual world

One of the large remaining challenges in the field of zebrafish neuroscience is the establishment of techniques and preparations that permit the recording and perturbation of neural activity in animals that can interact meaningfully with the environment. Since it is very difficult to do this in freely behaving zebrafish, I describe here two alternative approaches that meet this goal via tethered preparations. The first uses head-fixation in agarose in combination with online imaging and analysis of tail motion. In the second method, paralyzed fish are suspended with suction pipettes in mid-water and nerve root recordings serve as indicators for intended locomotion. In both cases, fish can be immersed into a virtual environment and allowed to interact with this virtual world via real or fictive tail motions. The specific examples given in this review focus primarily on the role of visual feedback~– but the general principles certainly extend to other modalities, including proprioception, hearing, balance, and somatosensation.

Vision in Drosophila: seeing the world through a model's eyes

The fruit fly, Drosophila melanogaster, has been used for decades as a genetic model for unraveling mechanisms of development and behavior. In order to efficiently assign gene functions to cellular and behavioral processes, early measures were often necessarily simple. Much of what is known of developmental pathways was based on disrupting highly regular structures, such as patterns of cells in the eye. Similarly, reliable visual behaviors such as phototaxis and motion responses provided a solid foundation for dissecting vision. Researchers have recently begun to examine how this model organism responds to more complex or naturalistic stimuli by designing novel paradigms that more closely mimic visual behavior in the wild. Alongside these advances, the development of brain-recording strategies allied with novel genetic tools has brought about a new era of Drosophila vision research where neuronal activity can be related to behavior in the natural world.

Multiple spectral inputs improve motion discrimination in the Drosophila visual system

Color and motion information are thought to be channeled through separate neural pathways, but it remains unclear whether and how these pathways interact to improve motion perception. In insects, such as Drosophila, it has long been believed that motion information is fed exclusively by one spectral class of photoreceptor, so-called R1 to R6 cells; whereas R7 and R8 photoreceptors, which exist in multiple spectral classes, subserve color vision. Here, we report that R7 and R8 also contribute to the motion pathway. By using electrophysiological, optical, and behavioral assays, we found that R7/R8 information converge with and shape the motion pathway output, explaining flies' broadly tuned optomotor behavior by its composite responses. Our results demonstrate that inputs from photoreceptors of different spectral sensitivities improve motion discrimination, increasing robustness of perception.

A model of ant navigation based on visual prediction

A model of visual navigation in ants is presented which is based on a simple network predicting the changes of a visual scene under translatory movements. The model contains two behavioral components: the acquisition of multiple snapshots in different orientations during a learning walk, and the selection of a movement direction by a scanning behavior where the ant searches through different headings. Both components fit with observations in experiments with desert ants. The model is in most aspects biologically plausible with respect to the equivalent neural networks, and it produces reliable homing behavior in a simulated environment with a complex random surface texture. The model is closely related to the algorithmic min-warping method for visual robot navigation which shows good homing performance in real-world environments.

TESTING OF INFORMATION CONDENSATION IN A MODEL REVERBERATING SPIKING NEURAL NETWORK

Information about external world is delivered to the brain in the form of structured in time spike trains. During further processing in higher areas, information is subjected to a certain condensation process, which results in formation of abstract conceptual images of external world, apparently, represented as certain uniform spiking activity partially independent on the input spike trains details. Possible physical mechanism of condensation at the level of individual neuron was discussed recently. In a reverberating spiking neural network, due to this mechanism the dynamics should settle down to the same uniform/ periodic activity in response to a set of various inputs. Since the same periodic activity may correspond to different input spike trains, we interpret this as possible candidate for information condensation mechanism in a network. Our purpose is to test this possibility in a network model consisting of five fully connected neurons, particularly, the influence of geometric size of the network, on its ability to condense information. Dynamics of 20 spiking neural networks of different geometric sizes are modelled by means of computer simulation. Each network was propelled into reverberating dynamics by applying various initial input spike trains. We run the dynamics until it becomes periodic. The Shannon's formula is used to calculate the amount of information in any input spike train and in any periodic state found. As a result, we obtain explicit estimate of the degree of information condensation in the networks, and conclude that it depends strongly on the net's geometric size.

Brain modularity in arthropods: individual neurons that support "what" but not "where" memories

Experiments with insects and crabs have demonstrated their remarkable capacity to learn and memorize complex visual features (Giurfa et al., 2001; Pedreira and Maldonado, 2003; Chittka and Niven, 2009). Such abilities are thought to require modular brain processing similar to that occurring in vertebrates (Menzel and Giurfa, 2001). Yet, physiological evidence for this type of functioning in the small brains of arthropods is still scarce (Liu et al., 1999, 2006; Menzel and Giurfa, 2001). In the crab Chasmagnathus granulatus, the learning rate as well as the long-term memory of a visual stimulus has been found to be reflected in the performance of identified lobula giant neurons (LGs) (Tomsic et al., 2003). The memory can only be evoked in the training context, indicating that animals store two components of the learned experience, one related to the visual stimulus and one related to the visual context (Tomsic et al., 1998; Hermitte et al., 1999). By performing intracellular recordings in the intact animal, we show that the ability of crabs to generalize the learned stimulus into new space positions and to distinguish it from a similar but unlearned stimulus, two of the main attributes of stimulus memory, is reflected by the performance of the LGs. Conversely, we found that LGs do not support the visual context memory component. Our results provide physiological evidence that the memory traces regarding "what" and "where" are stored separately in the arthropod brain.

Attracting the attention of a fly

Organisms with complex visual systems rarely respond to just the sum of all visual stimuli impinging on their eyes. Often, they restrict their responses to stimuli in a temporarily selected region of the visual field (selective visual attention). Here, we investigate visual attention in the fly Drosophila during tethered flight at a torque meter. Flies can actively shift their attention; however, their attention can be guided to a certain location by external cues. Using visual cues, we can direct the attention of the fly to one or the other of the two visual half-fields. The cue can precede the test stimulus by several seconds and may also be spatially separated from the test by at least 20° and yet attract attention. This kind of external guidance of attention is found only in the lower visual field.

A model study on the circuit mechanism underlying decision-making in Drosophila

Previous elegant experiments in a flight simulator showed that conditioned Drosophila is able to make a clear-cut decision to avoid potential danger. When confronted with conflicting visual cues, the relative saliency of two competing cues is found to be a sensory ruler for flies to judge which cue should be used for decision-making. Further genetic manipulations and immunohistological analysis revealed that the dopamine system and mushroom bodies are indispensable for such a clear-cut or nonlinear decision. The neural circuit mechanism, however, is far from being clear. In this paper, we adopt a computational modeling approach to investigate how different brain areas and the dopamine system work together to drive a fly to make a decision. By developing a systems-level neural network, a two-pathway circuit is proposed. Besides a direct pathway from a feature binding area to the motor center, another connects two areas via the mushroom body, a target of dopamine release. A raised dopamine level is hypothesized to be induced by complex choice tasks and to enhance lateral inhibition and steepen the units' response gain in the mushroom body. Simulations show that training helps to assign values to formerly neutral features. For a circuit model with a blocked mushroom body, the direct pathway passes all alternatives to the motor center without changing original values, giving rise to a simple choice characterized by a linear choice curve. With respect to an intact circuit, enhanced lateral inhibition dependent on dopamine critically promotes competition between alternatives, turning the linear- into nonlinear choice behavior. Results account well for experimental data, supporting the reasonableness of model working hypotheses. Several testable predictions are made for future studies.

Learning and memory in Drosophila: behavior, genetics, and neural systems

The rich behavioral repertoire that Drosophila use to navigate in their natural environment suggests that flies can use memories to inform decisions. Development of paradigms to examine memories that restrict behavioral choice was essential in furthering our understanding of the genetics and neural systems of memory formation in the fly. Olfactory, visual, and place memory paradigms have proven influential in determining principles for the mechanisms of memory formation. Several parts of the nervous system have been shown to be important for different types of memories, including the mushroom bodies and the central complex. Thus far, about 40 genes have been linked to normal olfactory short-term memory. A subset of these genes have also been tested for a role in visual and place memory. Some genes have a common function in memory formation, specificity of action comes from where in the nervous system these genes act. Alternatively, some genes have a more restricted role in different types of memories.

Attention in Drosophila

As bluntly summarized by a psychologist over a century ago, everyone knows what attention is [James (1890). The Principles of Psychology]. Attention describes our capacity to focus perception on one or a group of related stimuli while filtering out irrelevant stimuli. The ease we have in recognizing this astounding capacity in ourselves is matched by a surprising difficulty in identifying it in others, and this is especially the case for measuring attention in other animals. Identifying and measuring attention-like processes in simple animals such as flies requires, to some extent, even more rigor than asking the same question for our closer animal relatives, such as apes and monkeys. This is because flies have completely different brains than humans do, so to study attention in these creatures one must rely purely on operational or behavioral measures rather than comparative neuroanatomy. There is a long history of using sophisticated behavioral paradigms to study visual responses in Drosophila melanogaster, and these studies have often provided early evidence of attention-like processes in flies. More recently, these fly paradigms have been applied to measuring visual attention directly, and the combination of electrophysiology with these preparations has provided insight into how a fly might pay attention. Together with more efficient methods for measuring some aspects of attention, such as stimulus suppression, these approaches should begin to uncover how visual attention might work in a small brain.

Intrinsic activity in the fly brain gates visual information during behavioral choices

The small insect brain is often described as an input/output system that executes reflex-like behaviors. It can also initiate neural activity and behaviors intrinsically, seen as spontaneous behaviors, different arousal states and sleep. However, less is known about how intrinsic activity in neural circuits affects sensory information processing in the insect brain and variability in behavior. Here, by simultaneously monitoring Drosophila's behavioral choices and brain activity in a flight simulator system, we identify intrinsic activity that is associated with the act of selecting between visual stimuli. We recorded neural output (multiunit action potentials and local field potentials) in the left and right optic lobes of a tethered flying Drosophila, while its attempts to follow visual motion (yaw torque) were measured by a torque meter. We show that when facing competing motion stimuli on its left and right, Drosophila typically generate large torque responses that flip from side to side. The delayed onset (0.1–1 s) and spontaneous switch-like dynamics of these responses, and the fact that the flies sometimes oppose the stimuli by flying straight, make this behavior different from the classic steering reflexes. Drosophila, thus, seem to choose one stimulus at a time and attempt to rotate toward its direction. With this behavior, the neural output of the optic lobes alternates; being augmented on the side chosen for body rotation and suppressed on the opposite side, even though the visual input to the fly eyes stays the same. Thus, the flow of information from the fly eyes is gated intrinsically. Such modulation can be noise-induced or intentional; with one possibility being that the fly brain highlights chosen information while ignoring the irrelevant, similar to what we know to occur in higher animals.

Image-matching during ant navigation occurs through saccade-like body turns controlled by learned visual features

Visual memories of landmarks play a major role in guiding the habitual foraging routes of ants and bees, but how these memories engage visuo-motor control systems during guidance is poorly understood. We approach this problem through a study of image matching, a navigational strategy in which insects reach a familiar place by moving so that their current retinal image transforms to match a memorized snapshot of the scene viewed from that place. Analysis of how navigating wood ants correct their course when close to a goal reveals a significant part of the mechanism underlying this transformation. Ants followed a short route to an inconspicuous feeder positioned at a fixed distance from a vertical luminance edge. They responded to an unexpected jump of the edge by turning to face the new feeder position specified by the edge. Importantly, the initial speed of the turn increased linearly with the turn's amplitude. This correlation implies that the ants' turns are driven initially by their prior calculation of the angular difference between the current retinal position of the edge and its desired position in their memorized view. Similar turns keep ants to their path during unperturbed routes. The neural circuitry mediating image-matching is thus concerned not only with the storage of views, but also with making exact comparisons between the retinal positions of a visual feature in a memorized view and of the same feature in the current retinal image.

Short-term memories in Drosophila are governed by general and specific genetic systems

In a dynamic environment, there is an adaptive value in the ability of animals to acquire and express memories. That both simple and complex animals can learn is therefore not surprising. How animals have solved this problem genetically and anatomically probably lies somewhere in a range between a single molecular/anatomical mechanism that applies to all situations and a specialized mechanism for each learning situation. With an intermediate level of nervous system complexity, the fruit fly Drosophila has both general and specific resources to support different short-term memories. Some biochemical/cellular mechanisms are common between learning situations, indicating that flies do not have a dedicated system for each learning context. The opposite possible extreme does not apply to Drosophila either. Specialization in some biochemical and anatomical terms suggests that there is not a single learning mechanism that applies to all conditions. The distributed basis of learning in Drosophila implies that these systems were independently selected.

(In) sensitivity to spatial distortion in natural scenes

The perception of object structure in the natural environment is remarkably stable under large variation in image size and projection, especially given our insensitivity to spatial position outside the fovea. Sensitivity to periodic spatial distortions that were introduced into one quadrant of gray-scale natural images was measured in a 4AFC task. Observers were able to detect the presence of distortions in unfamiliar images even though they did not significantly affect the amplitude spectrum. Sensitivity depended on the spatial period of the distortion and on the image structure at the location of the distortion. The results suggest that the detection of distortion involves decisions made in the late stages of image perception and is based on an expectation of the typical structure of natural scenes.

Modeling schizophrenia in flies

Schizophrenia is a debilitating mental illness that affects 1% of the population worldwide. Although its molecular etiology remains unclear, recent advances in human psychiatric genetics have identified a large number of candidate genetic risk factors involved in schizophrenia. Modeling the disease in genetically tractable animals is thus a challenging but increasingly important task. In this review, I discuss the potential problems and perspectives associated with modeling schizophrenia in fruit flies, and briefly review the recent studies analyzing the molecular and cellular functions of Disrupted-In-Schizophrenia-1 (DISC1) in transgenic flies.

Do female fruit flies (Drosophila serrata) copy the mate choice of others?

Female mate-choice copying is a social learning phenomenon whereby a female's observation of a successful sexual interaction between a male and another female increases her likelihood of subsequently preferring that male. Although mate-choice copying has been documented in several vertebrate species, to our knowledge it has not yet been investigated in insects. Here, we investigated whether female mate-choice copying occurs in the fruit fly Drosophila serrata, a model system for the study of mate preferences and the sexual selection they generate. We used two complementary experiments in which focal females were given a choice between two males that differed in either their apparent (as determined visually by the focal female) or actual recent mating success. Mate-choice copying was evaluated by testing whether focal females mated more frequently with the 'preferred' male as opposed to the other male. In both experiments, however, we found no evidence for mate-choice copying. We discuss possible reasons for the apparent absence of mate-choice copying in this species.

Network adaptation improves temporal representation of naturalistic stimuli in Drosophila eye: II mechanisms

Retinal networks must adapt constantly to best present the ever changing visual world to the brain. Here we test the hypothesis that adaptation is a result of different mechanisms at several synaptic connections within the network. In a companion paper (Part I), we showed that adaptation in the photoreceptors (R1-R6) and large monopolar cells (LMC) of the Drosophila eye improves sensitivity to under-represented signals in seconds by enhancing both the amplitude and frequency distribution of LMCs' voltage responses to repeated naturalistic contrast series. In this paper, we show that such adaptation needs both the light-mediated conductance and feedback-mediated synaptic conductance. A faulty feedforward pathway in histamine receptor mutant flies speeds up the LMC output, mimicking extreme light adaptation. A faulty feedback pathway from L2 LMCs to photoreceptors slows down the LMC output, mimicking dark adaptation. These results underline the importance of network adaptation for efficient coding, and as a mechanism for selectively regulating the size and speed of signals in neurons. We suggest that concert action of many different mechanisms and neural connections are responsible for adaptation to visual stimuli. Further, our results demonstrate the need for detailed circuit reconstructions like that of the Drosophila lamina, to understand how networks process information.

Network adaptation improves temporal representation of naturalistic stimuli in Drosophila eye: I dynamics

Because of the limited processing capacity of eyes, retinal networks must adapt constantly to best present the ever changing visual world to the brain. However, we still know little about how adaptation in retinal networks shapes neural encoding of changing information. To study this question, we recorded voltage responses from photoreceptors (R1–R6) and their output neurons (LMCs) in the Drosophila eye to repeated patterns of contrast values, collected from natural scenes. By analyzing the continuous photoreceptor-to-LMC transformations of these graded-potential neurons, we show that the efficiency of coding is dynamically improved by adaptation. In particular, adaptation enhances both the frequency and amplitude distribution of LMC output by improving sensitivity to under-represented signals within seconds. Moreover, the signal-to-noise ratio of LMC output increases in the same time scale. We suggest that these coding properties can be used to study network adaptation using the genetic tools in Drosophila, as shown in a companion paper (Part II).

From form to function: the ways to know a neuron

The shape of a neuron, its morphological signature, dictates the neuron's function by establishing its synaptic partnerships. Here, we review various anatomical methods used to reveal neuron shape and the contributions these have made to our current understanding of neural function in the Drosophila brain, especially the optic lobe. These methods, including Golgi impregnation, genetic reporters, and electron microscopy (EM), necessarily incorporate biases of various sorts that are easy to overlook, but that filter the morphological signatures we see. Nonetheless, the application of these methods to the optic lobe has led to reassuringly congruent findings on the number and shapes of neurons and their connection patterns, indicating that morphological classes are actually genetic classes. Genetic methods using, especially, GAL4 drivers and associated reporters have largely superceded classical Golgi methods for cellular analyses and, moreover, allow the manipulation of neuronal activity, thus enabling us to establish a bridge between morphological studies and functional ones. While serial-EM reconstruction remains the only reliable, albeit labor-intensive, method to determine actual synaptic connections, genetic approaches in combination with EM or high-resolution light microscopic techniques are promising methods for the rapid determination of synaptic circuit function.

Heisenberg's roadmap guides our journey to the small cognitive world of Drosophila

Professor Martin Heisenberg is one of the pioneers in the exploration of neuroethology. With his inspiration and earnest help, we employed the fruitfly as a model system to investigate the underlying neural mechanism of cognitive behaviors. Here, we recalled the help from Martin in the early years and introduced some findings from our lab about visual cognition behaviors in Drosophila, such as decision making, selective attention, and experience-dependent visual pattern recognition. From the results so far, the circuit composed of mushroom bodies, central complex, and dopaminergic neurons may play an essential role in these behaviors.

Cracking neural circuits in a tiny brain: new approaches for understanding the neural circuitry of Drosophila

Genetic screens in Drosophila have identified many genes involved in neural development and function. However, until recently, it has been impossible to monitor neural signals in Drosophila central neurons, and it has been difficult to make specific perturbations to central neural circuits. This has changed in the past few years with the development of new tools for measuring and manipulating neural activity in the fly. Here we review how these new tools enable novel conceptual approaches to 'cracking circuits' in this important model organism. We discuss recent studies aimed at defining the cognitive demands on the fly brain, identifying the cellular components of specific neural circuits, mapping functional connectivity in those circuits and defining causal relationships between neural activity and behavior.

Selforganizing memory: active learning of landmarks used for navigation

We propose a memory architecture that is suited to solve a specific task, namely homing, that is finding a not directly visible home place by using visually accessible landmarks. We show that an agent equipped with such a memory structure can autonomously learn the situation and can later use its memory to accomplish homing behaviour. The architecture is based on neuronal structures and grows in a self-organized way depending on experience. The basic architecture consists of three parts, (i) a pre-processor, (ii) a simple, one-layered feed-forward network, called distributor net, and (iii) a full recurrently connected net for representing the situation models to be stored. Apart from Hebbian learning and a local version of the delta-rule, explorative learning is applied that is not based on passive detection of correlations, but is actively searching for interesting hypotheses. Hypotheses are spontaneously introduced and are verified or falsified depending on how well the network representing the hypothesis approaches an internal error of zero. The stability of this approach is successfully tested by removal of one landmark or shifting the position of one or several landmarks showing results comparable to those found in biological experiments. Furthermore, we applied noise in two ways. The trained network was either due to sensory noise or to noise applied to the bias weights describing the memory content. Finally, we tested to what extent learning of the weights is affected by noisy input given to the sensor data. The architecture proposed is discussed to have some at least superficial similarity to the mushroom bodies of insects.

Visual cues for the retrieval of landmark memories by navigating wood ants

Background

Even on short routes, ants can be guided by multiple visual memories. We investigate here the cues controlling memory retrieval as wood ants approach a one- or two-edged landmark to collect sucrose at a point along its base. In such tasks, ants store the desired retinal position of landmark edges at several points along their route. They guide subsequent trips by retrieving the appropriate memory and moving to bring the edges in the scene toward the stored positions.

Results

The apparent width of the landmark turns out to be a powerful cue for retrieving the desired retinal position of a landmark edge. Two other potential cues, the landmark's apparent height and the distance that the ant walks, have little effect on memory retrieval. A simple model encapsulates these conclusions and reproduces the ants' routes in several conditions. According to this model, the ant stores a look-up table. Each entry contains the apparent width of the landmark and the desired retinal position of vertical edges. The currently perceived width provides an index for retrieving the associated stored edge positions. The model accounts for the population behavior of ants and the idiosyncratic training routes of individual ants.

Discussion

Our results imply binding between the edge of a shape and its width and, further, imply that assessing the width of a shape does not depend on the presence of any particular local feature, such as a landmark edge. This property makes the ant's retrieval and guidance system relatively robust to edge occlusions.

Some recent advances in basic neuroscience research in China

Neuroscience as a distinct discipline or research programme has been a rather recent event in most Chinese universities and in the Chinese Academy of Sciences. However, the last few years have witnessed increased funding and an improved research environment for neuroscience, both of which facilitated an influx of Chinese neuroscientists trained abroad. In this review, we have highlighted some recent research advances made by neuroscientists in China. Based on our own expertise, this review is focused mainly on findings that have contributed to our understanding of the mechanisms underlying brain development, neural plasticity and cognitive processes, and neural degeneration.

Experience improves feature extraction in Drosophila

Previous exposure to a pattern in the visual scene can enhance subsequent recognition of that pattern in many species from honeybees to humans. However, whether previous experience with a visual feature of an object, such as color or shape, can also facilitate later recognition of that particular feature from multiple visual features is largely unknown. Visual feature extraction is the ability to select the key component from multiple visual features. Using a visual flight simulator, we designed a novel protocol for visual feature extraction to investigate the effects of previous experience on visual reinforcement learning in Drosophila. We found that, after conditioning with a visual feature of objects among combinatorial shape-color features, wild-type flies exhibited poor ability to extract the correct visual feature. However, the ability for visual feature extraction was greatly enhanced in flies trained previously with that visual feature alone. Moreover, we demonstrated that flies might possess the ability to extract the abstract category of "shape" but not a particular shape. Finally, this experience-dependent feature extraction is absent in flies with defective MBs, one of the central brain structures in Drosophila. Our results indicate that previous experience can enhance visual feature extraction in Drosophila and that MBs are required for this experience-dependent visual cognition.

The co-activation of snapshot memories in wood ants

Insects can guide themselves along a familiar route to a familiar place by retrieving and using visual snapshots that they have stored both along the route and at their destination and moving so that their current views match the target snapshots. To learn more about the matching process, we have investigated the interaction of snapshots by engineering a situation in which ants simultaneously retrieve two sets of memories.

Ants were trained from a fixed start position to feed in one site, after which the feeder was switched to a new one. It could take up to 30 trials after the switch before the ants headed directly to the new food site. We suppose that during this transition phase ants retrieve memories appropriate for both sites. We compared the ants' behaviour for two different sized separations between feeder sites. When the sites are relatively close together, the initial headings of the ants' paths rotated gradually from aiming directly at the first food site to aiming at the second food site,suggesting that ants' paths are controlled by the weighted average of two simultaneously activated snapshot attractors. By contrast, when the food sites were further apart, initial headings switched abruptly between the two sites– ants either headed for food site 1 or for food site 2. We show that these differences in transition behaviour can be simulated by the co-activation of snapshot attractors of restricted spatial extent, such that features encoded in a snapshot are only recognised if they occur within a limited retinal distance of the stored position of the feature.