In animal husbandry, livestock industry and research facilities, anaesthetic agents are frequently used to moderate stressful intervention. For mammals and birds, procedures have been established to fine-tune anaesthesia according to the intervention. In ectothermic vertebrates, however, and despite changes in legislation and growing evidence on their cognitive abilities, the presently available information is insufficient to make similarly informed decisions. Here we suggest a straightforward way for rapidly filling this gap. By recording from a command neuron in the brain of fish whose crucial role requires it to integrate and process information from all sensory systems and to relay it to motor output pathways, the specific effects of candidate anaesthesia on central processing of sensory information can directly and efficiently be probed. Our approach allows a rapid and reliable way of deciding if and at which concentration a given anaesthetic affects the central nervous system and sensory processing. We employ our method to four anaesthetics commonly used in fish and demonstrate that our method quickly and with small numbers of animals provides the critical data for informed decisions on anaesthetic use.
Electrical synapses are formed by two unrelated gap junction protein families, the primordial innexins (invertebrates) or the connexins (vertebrates). Although molecularly different, innexin- and connexinbased electrical synapses are strikingly similar in their membrane topology. However, it remains unclear if this similarity extends also to more sophisticated functions such as long-term potentiation which is only known in connexin-based synapses. Here we show that this capacity is not unique to connexinbased synapses. Using a method that allowed us to quantitatively measure gap-junction conductance we provide the first and unequivocal evidence of long-term potentiation in an innexin-based electrical synapse. Our findings suggest that long-term potentiation is a property that has likely existed already in ancestral gap junctions. They therefore could provide a highly potent system to dissect shared molecular mechanisms of electrical synapse plasticity.
Based on the initial movement of falling prey, hunting archerfish select a C-start that turns them right to where their prey is going to land and lends the speed to arrive simultaneously with prey. Our companion study suggested that the information sampled in less than 100 ms also includes the initial height of falling prey. Here, we examine which cues the fish might be using to gauge height so quickly. First, we show that binocular cues are not required: C-starts that either could or could not have used binocular information were equally fast and precise. Next, we explored whether the fish were using simplifying assumptions about the absolute size of their prey or its distance from a structured background. However, experiments with unexpected changes from the standard conditions failed to cause any errors. We then tested the hypothesis that the fish might infer depth from accommodation or from cues related to blurring in the image of their falling prey. However, the fish also determined the height of ‘fake flies’ correctly, even though their image could never be focused and their combined size and degree of blurring should have misled the fish. Our findings are not compatible with the view that archerfish use a flexible combination of cues. They also do not support the view that height is gauged relative to structures in the vicinity of starting prey. We suggest that these fish use an elaborate analysis of looming to rapidly gauge initial height.
Archerfish dislodge aerial prey with water jets and use their predictive C-starts to secure it. Their C-starts turn the fish to the later point of impact and set the speed so that the fish arrive just in time. The starts are adjusted on the basis of information on speed, direction, timing and horizontal start position of prey movement – sampled during less than 100 ms after prey starts falling. Presently, it is unclear whether one essential parameter, the initial height of prey, can also be determined during this brief sampling time. Shooters and probably also observing bystanders already know target height – used to hit and to shape their jets – and would simply have to feed this information into their C-start circuitry. We challenged archerfish by launching initially invisible prey objects either from the expected height level, at which the fish were looking and at which they fired shots, or from more lateral positions and a lower or higher initial height. The arrangement was designed so that an analysis of the direction and the linear speed chosen by the starting fish could determine whether the C-start information is based on the expected height or on the actual height, which can be detected only after hidden prey has begun falling. Our findings demonstrate that the fish quickly estimate initial height during the initial falling phase of prey and do not simply use the expected height level to which they were cued.
The parallel occurrence in archerfish of fine-tuned and yet powerful predictive C-starts as well as of kinematically identical escape C-starts makes archerfish an interesting system to test hypotheses on the roles played by the Mauthner cells, a pair of giant reticulospinal neurons. In this study, we show that the archerfish Mauthner cell shares all hallmark physiological properties with that of goldfish. Visual and acoustic inputs are received by the ventral and lateral dendrite, respectively, and cause complex postsynaptic potentials (PSPs) even in surgically anaesthetised fish. PSP shape did not indicate major differences between the species, but simple light flashes caused larger PSPs in archerfish, often driving the cell to fire an action potential. Probing archerfish in the classical tests for feedback inhibition, established in the Mauthner-associated networks in goldfish, revealed no differences between the two species, including the indications for electrical and chemical synaptic components. Also, the established hallmark experiments on feedforward inhibition showed no differences between the goldfish and archerfish Mauthner system. Extending these experiments to visual stimuli also failed to detect any differences between the two species and suggested that acoustical and visual input cause feed-forward inhibition, the magnitude, time course and duration of which match that of the respective PSPs in both archerfish and goldfish. Our findings question simple views on the role of the Mauthner cell and suggest that the archerfish Mauthner cell should be a good system to explore the function of these giant neurons in more sophisticated C-start behaviours.
Archerfish use two powerful C-starts: one to escape threats, the other to secure prey that they have downed with a shot of water. The two C-starts are kinematically equivalent and variable in both phases, and the predictive C-starts – used in hunting – are adjusted in terms of the angle of turning and the final linear speed to where and when their prey will hit the water surface. Presently, nothing is known about the neural circuits that drive the archerfish C-starts. As the starting point for a neuroethological analysis, we first explored the presence and morphology of a pair of Mauthner cells, which are key cells in the teleost fast-start system. We show that archerfish have a typical Mauthner cell in each medullary hemisphere and that these send by far the largest axons down the spinal cord. Stimulation of the spinal cord caused short-latency all-or-none field potentials that could be detected even at the surface of the medulla and that had the Mauthner cell as its only source. The archerfish’s Mauthner cell is remarkably similar morphologically to that of equally sized goldfish, except that the archerfish’s ventral dendrite is slightly longer and its lateral dendrite thinner. Our data provide the necessary starting point for the dissection of the archerfish fast-start system and of any role potentially played by its Mauthner cell in the two C-start manoeuvres. Moreover, they do not support the recently expressed view that Mauthner cells should be reduced in animals with highly variable faststart manoeuvres.
Archerfish are renowned for dislodging aerial prey using well-aimed shots of water. Recently it has been shown that these fish can shape their aerial jets by adjusting the dynamics of their mouth opening and closing. This allows the fish to adjust their jet to target distance so that they can forcefully hit prey over considerable distances. Here, we suggest that archerfish use the same technique to also actively control jets under water. Fired from close range, the underwater jets are powerful enough to lift up buried food particles, which the fish then can pick up. We trained fish so that we could monitor their mouth opening and closing maneuvers during underwater shooting and compare them with those employed in aerial shooting. Our analysis suggests that the fish use the same dynamic mechanism to produce aerial and underwater jets and that they employ the same basic technique to adjust their jets in both conditions. When food is buried in substrate that consists of large particles, the fish use a brief pulse, but they use a longer one when the substrate is more fine-grained. These findings extend the notion that archerfish can flexibly shape their jets to be appropriate in different contexts and suggest that archerfish shooting might have been shaped both by constraints in aerial and underwater shooting.
The analysis of saccadic decision-making tasks with two or four alternatives has shown what appears to be a general hallmark of decision-making: adding more alternatives decreases speed and accuracy. In their everyday lives, however, animals often select among many more than two options and under heavy constraints on speed and accuracy. Here we analyse a rapid decision made by hunting archerfish. As in the classical saccadic tasks, the fish must first estimate sensory information: based on an estimate of horizontal speed, azimuthal direction and initial height of falling prey, the fish must quickly select a suitable fast-start to arrive at the right place at the right time. Our results suggest that the fast-start decisions of archerfish are surprisingly robust with respect to adding a further decision-relevant variable. We show that the fish can appropriately account for vertical speed as an independent further variable – but the need to do so does not affect speed or accuracy of the decisions. Our findings suggest novel ways by which rapid and yet complex decisions could be balanced against increasing complexity.
Several animals are renowned for their cognitive skills such as tool use, metacognition or social learning. However, it remains puzzling why some species excel whereas others – sometimes even closely related ones ¬– do not. Archerfish show a remarkable assembly of skills in the context of their unique hunting behavior in which they down aerial prey with shots of water. Hoping to find ecological factors behind these skills, we have, over the past years, regularly traveled to archerfish mangrove habitats in Thailand. One of the most consistent findings was the presence of other surface-feeding fish, particularly the similar-sized halfbeak Zenarchopterus buffonis, wherever we spotted groups of archerfish. We describe here that Zenarchopterus is superbly equipped with water-wave detectors, rapidly detects the impact of prey even in the dark, is active at all times, is usually more numerous than archerfish and supplements its capabilities with visual skills. Without sophisticated additions to their hunting technique archerfish would thus lose most of their downed prey to halfbeaks. We suggest that the evolution of several skills of archerfish may have thus been influenced not only by intraspecific competition but also by competition with other surface-feeding fish.
Neuronal cell cultures offer a crucial tool to mechanistically analyse regeneration in the nervous system. Despite the increasing importance of zebrafish (Danio rerio) as an in vivo model in neurobiological and biomedical research, in vitro approaches to the nervous system are lagging far behind and no method is currently available for establishing enriched neuronal cell cultures. Here we show that magnetic-activated cell sorting (MACS) can be used for the large-scale generation of neuronal restricted progenitor (NRP) cultures from embryonic zebrafish. Our findings provide a simple and semi-automated method that is likely to boost the use of neuronal cell cultures as a tool for the mechanistic dissection of key processes in neuronal regeneration and development.
Among tool-using animals, none are known to adaptively change the hydrodynamic properties of a free jet of water—a task considered difficult in human technology. Hunting archerfish can strike their targets with precisely aimed water jets, but they are also presently thought to be unable to actively control the hydrodynamics of their jets. By using specifically trained fish, we were able to monitor several aspects of jet production and propagation as the fish fired at targets over a much wider range of distances than previously explored. We show that jets that have to travel farther also live longer. Furthermore, the time needed until water assembles at the jet tip is not fixed. Rather, it is adjusted so that maximum focusing occurs just before impact. Surprisingly, the fish achieve this by modulating the dynamics of changes in the cross-section of their mouth opening, a mechanism that seems to not have been applied yet in human-built nozzles. The timing adjustments archerfish make in order to powerfully hit targets over an extended range strikingly parallel the situation in the “uniquely human” ability of powerful throwing. Based on the key role throwing played in human encephalization and cognitive evolution, skillfully “throwing” water should similarly have led to the correlated rapid evolution of cognitive skills in this animal.
In their unique hunting behaviour, archerfish use a complex motor decision to secure their prey: based solely on how dislodged prey initially falls, they select an adapted C-start manoeuvre that turns the fish right towards the point on the water surface where their prey will later land. Furthermore, they take off at a speed that is set so as to arrive in time. We show here that the C-start manoeuvre and not subsequent tail beating is necessary and sufficient for setting this adaptive level of speed. Furthermore, the C-start pattern is adjusted to independently determine both the turning angle and the take-off speed. The selection of both aspects requires no a priori information and is done based on information sampled from the onset of target motion until the C-start is launched. Fin strokes can occur right after the C-start manoeuvre but are not required to fine-tune take-off speed, but rather to maintain it. By probing the way in which the fish set their take-off speed in a wide range of conditions in which distance from the later catching point and time until impact varied widely and unpredictably, we found that the C-start manoeuvre is programmed based on pre-C-start estimates of distance and time until impact. Our study hence provides the first evidence for a C-start that is fine-tuned to produce an adaptive speed level.
This article presents a summary and critical review of what is known about the ‘grouped retina’, a peculiar type of retinal organization in fish in which groups of photoreceptor cell inner and outer segments are arranged in spatially separated bundles.
Hunting archerfish precisely adapt their predictive C-starts to the initial movement of dislodged prey so that turn angle and initial speed are matched to the place and time of the later point of catch. The high accuracy and the known target point of the starts allow a sensitive straightforward assay of how temperature affects the underlying circuits. Furthermore, archerfish face rapid temperature fluctuations in their mangrove biotopes which could compromise performance. Here we show that after a brief acclimation period the function of the C-starts is fully maintained over a range of operating temperatures: (i) Full responsiveness was maintained at all temperatures, (ii) at all temperatures the fish selected accurate turns and were able to do so over the full angular range, (iii) at all temperatures speed attained immediately after the end of the C-start was matched - with equal accuracy - to 'virtual speed', i.e. the ratio of remaining distance to the future landing point and remaining time. While precision was fully compensated, C-start latency was not and increased by about 4 ms per 1°C cooling. Also kinematic aspects of the C-start were only partly compensated. Above 26°C the duration of the two major phases of the C-start were temperature-compensated. At lower temperatures, however, durations increased similarly as latency. Given the accessibility of the underlying networks, the archerfish predictive start should be an excellent model to assay the degree of plasticity and functional stability of C-start motor patterns.
Archerfish are renowned for shooting down aerial prey with water jets, but nothing is known about the ways they spot prey items in their richly structured mangrove habitats. We trained archerfish to stably assign the categories 'target' and 'background' to objects solely on the basis of non-motion cues. Unlike many other hunters archerfish are able to discriminate a target from its background in the complete absence of either self-motion or relative motion parallax cues and without using stored information about the structure of the background. This allowed us to perform matched tests to compare the ways fish and humans scan stationary visual scenes. In humans, visual search is seen as a doorway to cortical mechanisms of how attention is allocated. Fish lack a cortex and we therefore wondered if archerfish would differ from humans in their ways they scan a stationary visual scene. Our matched tests failed to disclose any differences in the dependence of response time distributions, a most sensitive indicator of the search mechanism, on number and complexity of background objects. Median and range of response times depended linearly on the number of background objects and the corresponding effective processing time per item increased similarly - about fourfold - in both humans and fish when the task was harder. Archerfish, like humans, also systematically scanned the scenery, starting with the closest object. Taken together, benchmark visual search tasks failed to disclose any difference between archerfish - who lack a cortex - and humans.
Decision-making networks must be tuned according to the rules that govern which action will be rewarded for a given constellation of current sensory information. Somehow these rules must be implemented in the networks that translate the sensory cues to actions but the nature of this representation is enigmatic. Recent findings suggest that Mauthner-associated networks in some fish can govern surprisingly sophisticated and plastic decisions in which the rules of prey motion govern what speed and direction must be selected to be at the right point at the right time. With the key cellular players individually identifiable, fish can help us to discover the nature of how rules are represented in decision-making circuitry of the vertebrate brain.
Despite their diversity, vertebrate retinae are specialized to maximize either photon catch or visual acuity. Here, we describe a functional type that is optimized for neither purpose. In the retina of the elephantnose fish (Gnathonemus petersii), cone photoreceptors are grouped together within reflecting, photonic crystal–lined cups acting as macroreceptors, but rod photoreceptors are positioned behind these reflectors. This unusual arrangement matches rod and cone sensitivity for detecting color-mixed stimuli, whereas the photoreceptor grouping renders the fish insensitive to spatial noise; together, this enables more reliable flight reactions in the fish’s dim and turbid habitat as compared with fish lacking this retinal specialization.
With more than 30 000 species inhabiting nearly every aquatic environment on earth, fish provide a rich source for studying fundamental aspects of visual processing. Many fish show highly sophisticated visual behaviors that often allow the analysis of unique visual specializations. Furthermore, most fish can readily be trained. This was masterly used to dismount the claim that lower vertebrates and invertebrates could not see color. Interestingly, later Nobel laureate Karl von Frisch first succeeded to show this in fish, before his famous work on bees. In this article, we will illustrate a number of behavioral approaches that have been used successfully to tackle most aspects of fish vision.
... After a brief overview of complex decisions in primates and of decision-making in simple networks, I argue that simpler systems can combine complexity with accessibility at the cellular level. Indeed, examination of a network in fish may help in dissecting key mechanisms of complex and flexible decision-making in an established model of synaptic plasticity at the level of identified neurons.
Numerous animal navigators are not simply at the mercy of winds and currents but cope with drift to reach their goals. Here, we report how a fruit-catching Costa Rican fish combines an analysis of aerial motion with a novel way of compensating for drift to optimize its catching success. In the field, schools of this riverine fish never waited until a falling fruit actually landed in the stream. Rather, the fish responded to visual motion and started early to arrive on time at the spot where their food would land. To be successful with their early starts, the fish must cope with the strong relative drift that arises, because the fish, but not their airborne target, experience strong flow on their way toward the fruit’s landing point. Surprisingly, the fish solve this problem right at the beginning—by turning rapidly and taking an initial aim that is already optimally adapted to the prevailing drift, so as to lead them straight to their food. Fruit-catching fish thus provide a stunning case of how rapidly animals can generate drift-compensating trajectories in their everyday local lives.
The enormous progress made in functional magnetic resonance imaging technology allows us to watch our brains engage in complex cognitive and social tasks. However, our understanding of what actually is computed in the underlying cellular networks is hindered by the vast numbers of neurons involved. Here, we describe a vertebrate system, shaped for top speed, in which a complex and plastic decision is performed by surprisingly small circuitry that can be studied at cellular resolution.
Once their shots have successfully dislodged aerial prey, hunting archer fish monitor the initial values of their prey’s ballistic motion and elicit an adapted rapid turning maneuver. This allows these fish to head straight towards the later point of catch with a speed matched to the distance to be covered. To make the catch despite severe competition the fish must quickly and yet precisely match their turn and take-off speed to the initial values of prey motion. However, the initial variables vary over broad ranges and can be determined only after prey is dislodged. Therefore, the underlying neuronal circuitry must be able to drive a maneuver that combines a high degree of precision and flexibility at top speed. To narrow down which neuronal substrate underlies the performance we characterized the kinematics of archer fish predictive starts using digital high-speed video. Strikingly, the predictive starts show all hallmarks of Mauthner-driven teleost C-type fast-starts, which have previously not been noted in feeding strikes and were not expected to provide the high angular accuracy required. The high demands on flexibility and precision of the predictive starts do not compromise their performance. On the contrary, archer fish predictive starts are among the fastest C-starts known so far among teleost fish, with peak linear speed beyond 20·body·lengths·s–1, angular speed over 4500·deg.·s–1, maximum linear acceleration of up to 12 times gravitational acceleration and peak angular acceleration of more than 450·000·deg.·s–2. Moreover, they were not slower than archer fish escape C-starts, elicited in the same individuals. Rather, both escapes and predictive starts follow an identical temporal pattern and all kinematic variables of the two patterns overlap. This kinematic equivalence strongly suggests that archer fish recruit their C-start escape network of identified reticulospinal neurons, or elements of it, to drive their predictive starts. How the network drives such a rather complex behavior without compromising speed is a wide open question.
... The fish appear to learn in ways that most high-school students and perhaps even more their teachers would dream of. A remarkable capability to generalize allows them to readily engage demanding tasks they have not directly been exposed to before.
A recent study has shown that, unusually, both the sensory and motor capabilities of an electric fish are omnidirectional. This matching of motor and sensory spaces helps the fish to hunt prey efciently —particularly important given their energetically costly active sensory system.
Archer fish can shoot down insect prey with a sharp jet of water. Fish usually fire from positions that are not directly below their target so that a dislodged insect falls ballistically with a horizontal velocity component. Only 100·ms after the insect is on its path both the shooter and other school members can initiate a rapid turn and then head straight in the direction of the later point of impact of their falling prey. The quick turn and subsequent take- off are performed ‘open-loop’, based on the initial values of the falling insect’s motion. We report here that archer fish can not only take off in the direction of the later point of impact but also predict its distance. Distance information allows the fish to adjust their take-off speed so that they would arrive within a narrow time slot slightly (about 50·ms) after their prey’s impact, despite large differences in the size of the aligning turn and in the distance to be covered. Selecting a constant speed program with matched speed and catching the insect on the move minimizes frictional losses. The initial speed of starting fish is slightly but systematically too slow and is increased later so that the fish arrive 20·ms earlier than expected and often make the catch on a higher than take-off speed. The variability of later speed changes suggests a systematic ‘error’ in the take-off, as if the fish underestimated distance. However, this apparent deficiency seems well adapted to the fish catching their prey at a high speed: if later the fish had no possibility to correct an initial error then it is better to start slightly too slow in order to minimize the risk of overshooting the point of catch.
Studying for the first time the forces transferred to prey, we discovered that archerfish do not fire all-or-none shots but fine-tune their surprisingly costly shots to prey size. This tuning is strikingly lacking of plasticity and innately matched to a constant key property of archerfish feeding ecology: the universal scaling of adhesive forces of their various prey organisms.
In extremely rapid maneuvers, animals including man can launch ballistic motor patterns that cannot immediately be corrected [1–3]. Such patterns are difficult to direct at targets that move in three-dimensional space [2–4], and it is presently unknown how animals learn to acquire the precision required. Archer fish live in groups and are renowned for their ballistic hunting technique in which they knock down stationary aerial insect prey with a precisely aimed shot of water [5– 7]. Here we report that these fish can learn to release their shots so as to hit prey that moves rapidly at great height, a remarkable accomplishment in which the shooter must take both the target’s three-dimensional motion as well as that of its rising shot into account. To successfully perform in the three-dimensional task, training with horizontal motion suffices. Moreover, all archer fish of a group were able to learn the complex sensomotor skill from watching a performing group member, without having to practice. This instance of social learning in a fish is most remarkable as it could imply that observers can ‘‘change their viewpoint,’’ mapping the perceived shooting characteristics of a distant team member into angles and tar- get distances that they later must use to hit.
Why study hearing and vision in electric fish whose outstanding electromotor and electrosensory abilities enable them to exchange messages 'secretly' over a channel that is inaccessible to most other animals?
Many animals, including humans, can visually judge the absolute size of objects regardless of changes in viewing distance and thus despite the resulting dramatic differences in the size of the actual retinal images [1–5]. For animals that have to judge the size of aerial objects from underwater views, this can be a formidable problem; our calculations show that considerable and strongly viewpoint-dependent corrections are needed to compensate for the effects of light refraction. Archer fish face these optical difficulties because they have to shoot down aerial insects over a wide range of horizontal and vertical distances [6, 7]. We show here that these fish can learn to acquire size constancy with remarkable precision and are thus fully capable of taking complex viewpoint dependency into account. Moreover, we demonstrate that archer fish solve the problem not by interpolating within a set of stored views and distances but by learning the laws that connect apparent size with the fish’s relative position to the target. This enables the fish to readily judge the absolute sizes of objects from completely novel views.
Insects can estimate distance or time-to-contact of surrounding objects from locomotion-induced changes in their retinal position and/or size. Freely walking fruit flies (Drosophila melanogaster) use the received mixture of different distance cues to select the nearest objects for subsequent visits. Conventional methods of behavioral analysis fail to elucidate the underlying data extraction. Here we demonstrate first comprehensive solutions of this problem by substituting virtual for real objects; a tracker-controlled 360 deg panorama converts a fruit fly’s changing coordinates into object illusions that require the perception of specific cues to appear at preselected distances up to infinity. An application reveals the following: (1) en- route sampling of retinal-image changes accounts for distance discrimination within a surprising range of at least 8–80 body lengths (20–200 mm). Stereopsis and peering are not involved. (2) Distance from image translation in the expected direction (motion parallax) outweighs distance from image expansion, which accounts for impact-avoiding flight reactions to looming objects. (3) The ability to discriminate distances is robust to artificially delayed updating of image translation. Fruit flies appear to interrelate self-motion and its visual feedback within a surprisingly long time window of about 2 s.