Temporal analysis of stochastic turning behavior of swimming C. elegans

Geometric phase predicts locomotion performance in undulating living systems across scales

Self-propelling organisms locomote via generation of patterns of self-deformation. Despite the diversity of body plans, internal actuation schemes and environments in limbless vertebrates and invertebrates, such organisms often use similar traveling waves of axial body bending for movement. Delineating how self-deformation parameters lead to locomotor performance (e.g. speed, energy, turning capabilities) remains challenging. We show that a geometric framework, replacing laborious calculation with a diagrammatic scheme, is well-suited to discovery and comparison of effective patterns of wave dynamics in diverse living systems. We focus on a regime of undulatory locomotion, that of highly damped environments, which is applicable not only to small organisms in viscous fluids, but also larger animals in frictional fluids (sand) and on frictional ground. We find that the traveling wave dynamics used by mm-scale nematode worms and cm-scale desert dwelling snakes and lizards can be described by time series of weights associated with two principal modes. The approximately circular closed path trajectories of mode weights in a self-deformation space enclose near-maximal surface integral (geometric phase) for organisms spanning two decades in body length. We hypothesize that such trajectories are targets of control (which we refer to as "serpenoid templates"). Further, the geometric approach reveals how seemingly complex behaviors such as turning in worms and sidewinding snakes can be described as modulations of templates. Thus, the use of differential geometry in the locomotion of living systems generates a common description of locomotion across taxa and provides hypotheses for neuromechanical control schemes at lower levels of organization.

Inter-turn intervals in Paramecium caudatum display an exponential distribution

In navigating to a better location, mobile organisms in diverse taxa change directions of travel occasionally, including bacteria, archaea, single-celled eukaryotes, and small nematode worms such as Caenorhabditis elegans. In perhaps the most common form of goal-orientated movement, the rate of such turns is adjusted in all these taxa to ascend (or descend) a chemical gradient. Basically, the rate of turns is reduced when the movement results in better conditions. In the bacterium Escherichia coli and in C. elegans, the turns are generated by random-rate processes, in which the probability of a turn occurring is constant at every moment. This is evidenced by a distribution of inter-turn intervals that has an exponential distribution. For the first time, we examined the distribution of inter-turn intervals in the single-celled eukaryote, Paramecium caudatum, in a class exercise for first-year university students. We found clear evidence for an exponential distribution of inter-turn intervals, implying a random-rate process in generating turns in Paramecium. The exercise also shows that university laboratory classes can be used to generate scientific data to address research questions whose answers are as yet unknown.

Fluctuating landscapes and heavy tails in animal behavior

Animal behavior is shaped by a myriad of mechanisms acting on a wide range of scales, which hampers quantitative reasoning and the identification of general principles. Here, we combine data analysis and theory to investigate the relationship between behavioral plasticity and heavy-tailed statistics often observed in animal behavior. Specifically, we first leverage high-resolution recordings of C. elegans locomotion to show that stochastic transitions among long-lived behaviors exhibit heavy-tailed first passage time distributions and correlation functions. Such heavy tails can be explained by slow adaptation of behavior over time. This particular result motivates our second step of introducing a general model where we separate fast dynamics on a quasi-stationary multi-well potential, from non-ergodic, slowly varying modes. We then show that heavy tails generically emerge in such a model, and we provide a theoretical derivation of the resulting functional form, which can become a power law with exponents that depend on the strength of the fluctuations. Finally, we provide direct support for the generality of our findings by testing them in a C. elegans mutant where adaptation is suppressed and heavy tails thus disappear, and recordings of larval zebrafish swimming behavior where heavy tails are again prevalent.

Oscillators and servomechanisms in navigation and orientation

I summarize my recent theorizing on orientation and navigation across life. Organisms use navigational servomechanisms working with oscillators to get to goals. Navigational servomechanisms track errors from the best direction of travel and initiate action to correct the error. They work with endogenously generated action patterns, oscillations produced by oscillators, to adjust the course of travel. The theme applies to all scales of life from micrometers to thousands of kilometers. Servomechanisms and oscillators also characterize some other domains of cognition.

Fluctuating landscapes and heavy tails in animal behavior

Animal behavior is shaped by a myriad of mechanisms acting on a wide range of scales. This immense variability hampers quantitative reasoning and renders the identification of universal principles elusive. Through data analysis and theory, we here show that slow non-ergodic drives generally give rise to heavy-tailed statistics in behaving animals. We leverage high-resolution recordings of C. elegans locomotion to extract a self-consistent reduced order model for an inferred reaction coordinate, bridging from sub-second chaotic dynamics to long-lived stochastic transitions among metastable states. The slow mode dynamics exhibits heavy-tailed first passage time distributions and correlation functions, and we show that such heavy tails can be explained by dynamics on a time-dependent potential landscape. Inspired by these results, we introduce a generic model in which we separate faster mixing modes that evolve on a quasi-stationary potential, from slower non-ergodic modes that drive the potential landscape, and reflect slowly varying internal states. We show that, even for simple potential landscapes, heavy tails emerge when barrier heights fluctuate slowly and strongly enough. In particular, the distribution of first passage times and the correlation function can asymptote to a power law, with related exponents that depend on the strength and nature of the fluctuations. We support our theoretical findings through direct numerical simulations.

A stochastic explanation for observed local-to-global foraging states in Caenorhabditis elegans

Abrupt changes in behavior can often be associated with changes in underlying behavioral states. When placed off food, the foraging behavior of C. elegans can be described as a change between an initial local-search behavior characterized by a high rate of reorientations, followed by a global-search behavior characterized by sparse reorientations. This is commonly observed in individual worms, but when numerous worms are characterized, only about half appear to exhibit this behavior. We propose an alternative model that predicts both abrupt and continuous changes to reorientation that does not rely on behavioral states. This model is inspired by molecular dynamics modeling that defines the foraging reorientation rate as a decaying parameter. By stochastically sampling from the probability distribution defined by this rate, both abrupt and gradual changes to reorientation rates can occur, matching experimentally observed results. Crucially, this model does not depend on behavioral states or information accumulation. Even though abrupt behavioral changes do occur, they may not necessarily be indicative of abrupt changes in behavioral states, especially when abrupt changes are not universally observed in the population.

Scanning behaviour in ants: an interplay between random-rate processes and oscillators

At the start of a journey home or to a foraging site, ants often stop, interrupting their forward movement, turn on the spot a number of times, and fixate in different directions. These scanning bouts are thought to provide visual information for choosing a path to travel. The temporal organization of such scanning bouts has implications about the neural organisation of navigational behaviour. We examined (1) the temporal distribution of the start of such scanning bouts and (2) the dynamics of saccadic body turns and fixations that compose a scanning bout in Australian desert ants, Melophorus bagoti, as they came out of a walled channel onto open field at the start of their homeward journey. Ants were caught when they neared their nest and displaced to different locations to start their journey home again. The observed parameters were mostly similar across familiar and unfamiliar locations. The turning angles of saccadic body turning to the right or left showed some stereotypy, with a peak just under 45°. The direction of such saccades appears to be determined by a slow oscillatory process as described in other insect species. In timing, however, both the distribution of inter-scanning-bout intervals and individual fixation durations showed exponential characteristics, the signature for a random-rate or Poisson process. Neurobiologically, therefore, there must be some process that switches behaviour (starting a scanning bout or ending a fixation) with equal probability at every moment in time. We discuss how chance events in the ant brain that occasionally reach a threshold for triggering such behaviours can generate the results.

From representations to servomechanisms to oscillators: my journey in the study of cognition

The study of comparative cognition bloomed in the 1970s and 1980s with a focus on representations in the heads of animals that undergird what animals can achieve. Even in action-packed domains such as navigation and spatial cognition, a focus on representations prevailed. In the 1990s, I suggested a conception of navigation in terms of navigational servomechanisms. A servomechanism can be said to aim for a goal, with deviations from the goal-directed path registering as an error. The error drives action to reduce the error in a negative-feedback loop. This loop, with the action reducing the very signal that drove action in the first place, is key to defining a servomechanism. Even though actions are crucial components of servomechanisms, my focus was on the representational component that encodes signals and evaluates errors. Recently, I modified and amplified this view in claiming that, in navigation, servomechanisms operate by modulating the performance of oscillators, endogenous units that produce periodic action. The pattern is found from bacteria travelling micrometres to sea turtles travelling thousands of kilometres. This pattern of servomechanisms working with oscillators is found in other realms of cognition and of life. I think that oscillators provide an effective way to organise an organism's own activities while servomechanisms provide an effective means to adjust to the organism's environment, including that of its own body.

Oscillators and servomechanisms in orientation and navigation, and sometimes in cognition

Navigational mechanisms have been characterized as servomechanisms. A navigational servomechanism specifies a goal state to strive for. Discrepancies between the perceived current state and the goal state specify error. Servomechanisms adjust the course of travel to reduce the error. I now add that navigational servomechanisms work with oscillators, periodic movements of effectors that drive locomotion. I illustrate this concept selectively over a vast range of scales of travel from micrometres in bacteria to thousands of kilometres in sea turtles. The servomechanisms differ in sophistication, with some interrupting forward motion occasionally or changing travel speed in kineses and others adjusting the direction of travel in taxes. I suggest that in other realms of life as well, especially in cognition, servomechanisms work with oscillators.

The Basis of Navigation Across Species

Animals navigate a wide range of distances, from a few millimeters to globe-spanning journeys of thousands of kilometers. Despite this array of navigational challenges, similar principles underlie these behaviors across species. Here, we focus on the navigational strategies and supporting mechanisms in four well-known systems: the large-scale migratory behaviors of sea turtles and lepidopterans as well as navigation on a smaller scale by rats and solitarily foraging ants. In lepidopterans, rats, and ants we also discuss the current understanding of the neural architecture which supports navigation. The orientation and navigational behaviors of these animals are defined in terms of behavioral error-reduction strategies reliant on multiple goal-directed servomechanisms. We conclude by proposing to incorporate an additional component into this system: the observation that servomechanisms operate on oscillatory systems of cycling behavior. These oscillators and servomechanisms comprise the basis for directed orientation and navigational behaviors. Expected final online publication date for the Annual Review of Psychology, Volume 73 is January 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Learning walks in an Australian desert ant, Melophorus bagoti

The central Australian ant Melophorus bagoti is the most thermophilic ant in Australia and forages solitarily in the summer months during the hottest period of the day. For successful navigation, desert ants of many species are known to integrate a path and learn landmark cues around the nest. Ants perform a series of exploratory walks around the nest before their first foraging trip, during which they are presumed to learn about their landmark panorama. Here, we studied 15 naive M. bagoti ants transitioning from indoor work to foraging outside the nest. In 3–4 consecutive days, they performed 3–7 exploratory walks before heading off to forage. Naive ants increased the area of exploration around the nest and the duration of trips over successive learning walks. In their first foraging walk, the majority of the ants followed a direction explored on their last learning walk. During learning walks, the ants stopped and performed stereotypical orientation behaviours called pirouettes. They performed complete body rotations with stopping phases as well as small circular walks without stops known as voltes. After just one learning walk, these desert ants could head in the home direction from locations 2 m from the nest, although not from locations 4 m from the nest. These results suggest gradual learning of the visual landmark panorama around the foragers’ nest. Our observations show that M. bagoti exhibit similar characteristics in their learning walks to other desert ants of the genera Ocymyrmex and Cataglyphis.

Summary: Before becoming foragers, Melophorus bagoti ants took 3–7 learning walks around their nest. They increased the duration and area explored over successive walks, stopping occasionally to scan the environment.

Computational geometric tools for quantitative comparison of locomotory behavior

A fundamental challenge for behavioral neuroscientists is to accurately quantify (dis)similarities in animal behavior without excluding inherent variability present between individuals. We explored two new applications of curve and shape alignment techniques to address this issue. As a proof-of-concept we applied these methods to compare normal or alarmed behavior in pairs of medaka (Oryzias latipes). The curve alignment method we call Behavioral Distortion Distance (BDD) revealed that alarmed fish display less predictable swimming over time, even if individuals incorporate the same action patterns like immobility, sudden changes in swimming trajectory, or changing their position in the water column. The Conformal Spatiotemporal Distance (CSD) technique on the other hand revealed that, in spite of the unpredictability, alarmed individuals exhibit lower variability in overall swim patterns, possibly accounting for the widely held notion of "stereotypy" in alarm responses. More generally, we propose that these new applications of established computational geometric techniques are useful in combination to represent, compare, and quantify complex behaviors consisting of common action patterns that differ in duration, sequence, or frequency.

A quantitative model of conserved macroscopic dynamics predicts future motor commands

In simple organisms such as Caenorhabditis elegans, whole brain imaging has been performed. Here, we use such recordings to model the nervous system. Our model uses neuronal activity to predict expected time of future motor commands up to 30 s prior to the event. These motor commands control locomotion. Predictions are valid for individuals not used in model construction. The model predicts dwell time statistics, sequences of motor commands and individual neuron activation. To develop this model, we extracted loops spanned by neuronal activity in phase space using novel methodology. The model uses only two variables: the identity of the loop and the phase along it. Current values of these macroscopic variables predict future neuronal activity. Remarkably, our model based on macroscopic variables succeeds despite consistent inter-individual differences in neuronal activation. Thus, our analytical framework reconciles consistent individual differences in neuronal activation with macroscopic dynamics that operate universally across individuals.

How can we go about trying to understand an object as complex as the brain? The traditional approach is to begin by studying its component parts, cells called neurons. Once we understand how individual neurons work, we can use computers to simulate the activity of networks of neurons. The result is a computer model of the brain. By comparing this model to data from real brains, we can try to make the model as similar to a real brain as possible.

But whose brain should we try to reproduce? The roundworm C. elegans, for example, has just 302 neurons in total. Advances in brain imaging mean it is now possible to identify each of these neurons and compare its activity across worms. But doing so reveals that the activity of any given neuron varies greatly between individuals. This is true even among genetically identical worms performing the same behavior.

Researchers trying to model the roundworm brain have attempted to model the average activity of each neuron across many worms. They hoped they could use these averages to predict the behavior of other worms from their neuronal activity. But this approach did not to work. Even in roundworms, the coordinated activity of many neurons is required to generate even simple behaviors. Averaging the activity of neurons across worms thus scrambles the information that encodes each behavior.

Brennan and Proekt have now overcome this problem by developing a more abstract model that treats the nervous system as a whole. The model takes into account changes in the activity of neurons, and in the worms’ behavior, over time. A model of this type built using one set of worms can predict the behavior of another set of worms. This approach may work because in evolution natural selection acts at the level of behaviors, and not at the level of individual neurons. The activity of individual neurons can thus vary between animals, even when those neurons encode the same behavior. This means it may also be possible to model the human brain without knowing the activity of each of its billions of neurons.

Comprehensive machine learning analysis of Hydra behavior reveals a stable basal behavioral repertoire

Animal behavior has been studied for centuries, but few efficient methods are available to automatically identify and classify it. Quantitative behavioral studies have been hindered by the subjective and imprecise nature of human observation, and the slow speed of annotating behavioral data. Here, we developed an automatic behavior analysis pipeline for the cnidarian Hydra vulgaris using machine learning. We imaged freely behaving Hydra, extracted motion and shape features from the videos, and constructed a dictionary of visual features to classify pre-defined behaviors. We also identified unannotated behaviors with unsupervised methods. Using this analysis pipeline, we quantified 6 basic behaviors and found surprisingly similar behavior statistics across animals within the same species, regardless of experimental conditions. Our analysis indicates that the fundamental behavioral repertoire of Hydra is stable. This robustness could reflect a homeostatic neural control of "housekeeping" behaviors which could have been already present in the earliest nervous systems.

Emergence of Lévy Walks from Second-Order Stochastic Optimization

In natural foraging, many organisms seem to perform two different types of motile search: directed search (taxis) and random search. The former is observed when the environment provides cues to guide motion towards a target. The latter involves no apparent memory or information processing and can be mathematically modeled by random walks. We show that both types of search can be generated by a common mechanism in which Lévy flights or Lévy walks emerge from a second-order gradient-based search with noisy observations. No explicit switching mechanism is required-instead, continuous transitions between the directed and random motions emerge depending on the Hessian matrix of the cost function. For a wide range of scenarios, the Lévy tail index is α=1, consistent with previous observations in foraging organisms. These results suggest that adopting a second-order optimization method can be a useful strategy to combine efficient features of directed and random search.

A global brain state underlies C. elegans sleep behavior

How the brain effectively switches between and maintains global states, such as sleep and wakefulness, is not yet understood. We used brainwide functional imaging at single-cell resolution to show that during the developmental stage of lethargus, the Caenorhabditis elegans brain is predisposed to global quiescence, characterized by systemic down-regulation of neuronal activity. Only a few specific neurons are exempt from this effect. In the absence of external arousing cues, this quiescent brain state arises by the convergence of neuronal activities toward a fixed-point attractor embedded in an otherwise dynamic neural state space. We observed efficient spontaneous and sensory-evoked exits from quiescence. Our data support the hypothesis that during global states such as sleep, neuronal networks are drawn to a baseline mode and can be effectively reactivated by signaling from arousing circuits.

An Aversive Response to Osmotic Upshift in Caenorhabditis elegans

Environmental osmolarity presents a common type of sensory stimulus to animals. While behavioral responses to osmotic changes are important for maintaining a stable intracellular osmolarity, the underlying mechanisms are not fully understood. In the natural habitat of Caenorhabditis elegans, changes in environmental osmolarity are commonplace. It is known that the nematode acutely avoids shocks of extremely high osmolarity. Here, we show that C. elegans also generates gradually increased aversion of mild upshifts in environmental osmolarity. Different from an acute avoidance of osmotic shocks that depends on the function of a transient receptor potential vanilloid channel, the slow aversion to osmotic upshifts requires the cGMP-gated sensory channel subunit TAX-2. TAX-2 acts in several sensory neurons that are exposed to body fluid to generate the aversive response through a motor network that underlies navigation. Osmotic upshifts activate the body cavity sensory neuron URX, which is known to induce aversion upon activation. Together, our results characterize the molecular and cellular mechanisms underlying a novel sensorimotor response to osmotic stimuli and reveal that C. elegans engages different behaviors and the underlying mechanisms to regulate responses to extracellular osmolarity.

Resolving coiled shapes reveals new reorientation behaviors in C. elegans

We exploit the reduced space of C. elegans postures to develop a novel tracking algorithm which captures both simple shapes and also self-occluding coils, an important, yet unexplored, component of 2D worm behavior. We apply our algorithm to show that visually complex, coiled sequences are a superposition of two simpler patterns: the body wave dynamics and a head-curvature pulse. We demonstrate the precise Ω-turn dynamics of an escape response and uncover a surprising new dichotomy in spontaneous, large-amplitude coils; deep reorientations occur not only through classical Ω-shaped postures but also through larger postural excitations which we label here as δ-turns. We find that omega and delta turns occur independently, suggesting a distinct triggering mechanism, and are the serpentine analog of a random left-right step. Finally, we show that omega and delta turns occur with approximately equal rates and adapt to food-free conditions on a similar timescale, a simple strategy to avoid navigational bias.

DOI:http://dx.doi.org/10.7554/eLife.17227.001

We all instinctively recognize behavior: it’s what organisms do, whether they are single cells searching for food, or birds singing to mark their territory. If we want to understand behavior, however, we have to be able to characterize such actions as precisely and completely as their underlying molecular and cellular mechanisms.

For the millimeter-sized roundworm C. elegans, video tracking and analysis has produced a compact characterization of naturally occurring worm postures. Simply put: every body posture of the worm is a different mix of four fundamental postures called ‘eigenworms’. The worm’s snake-like motion is then a series of combinations of these projections, which can be analyzed to provide an automatic and measureable read-out of the worm’s behavior.

There is, however, an important caveat: when the worm makes a ‘loop’, and crosses over itself, such posture analysis is inapplicable. That is unfortunate: some of the worm’s most interesting behavior involves looping. One example is the “omega turn”, named after the shape of the Greek letter Ω. This sharp turn is used by the worm to steer away from harm, and more generally to abruptly reorient during the search for food and for mates.

Broekmans et al. have now created an algorithm, based on eigenworms, which can analyze worm images that encompass both looped and normal shapes. The result is a complete ‘behavioral microscope’ that shows how C. elegans moves in 2D. Focusing this microscope in particular on the omega turn, Broekmans et al. found that such turns are not, as has been previously described, a single behavior. Instead, they are two separate behaviors that represent the worm’s equivalent of a left-right step.

Together with previous posture analysis the work presented by Broekmans et al. allows for the full and precise measurement of the body shapes of C. elegans in 2D. This, combined with remarkable recent progress in global brain and gene expression imaging, should help to uncover new mechanisms that ultimately produce and control a worm’s behavior.

DOI:http://dx.doi.org/10.7554/eLife.17227.002

Characterization of the crawling activity of Caenorhabditis elegans using a Hidden Markov model

The locomotion behavior of Caenorhabditis elegans has been studied extensively to understand the respective roles of neural control and biomechanics as well as the interaction between them. Constructing a mathematical model is helpful to understand the locomotion behavior in various surrounding conditions that are difficult to realize in experiments. In this study, we built three hidden Markov models (HMMs) for the crawling behavior of C. elegans in a controlled environment with no chemical treatment and in a formaldehyde-treated environment (0.1 and 0.5 ppm). The organism's crawling activity was recorded using a digital camcorder for 20 min at a rate of 24 frames per second. All shape patterns were quantified by branch length similarity (BLS) entropy and classified into four groups using the self-organizing map (SOM). Comparison of the simulated behavior generated by HMMs and the actual crawling behavior demonstrated that the HMM coupled with the SOM was successful in characterizing the crawling behavior. In addition, we briefly discussed the possibility of using the HMM together with BLS entropy to develop bio-monitoring systems to determine water quality.

High-throughput, motility-based sorter for microswimmers such as C. elegans

Animal motility varies with genotype, disease, aging, and environmental conditions. In many studies, it is desirable to carry out high throughput motility-based sorting to isolate rare animals for, among other things, forward genetic screens to identify genetic pathways that regulate phenotypes of interest. Many commonly used screening processes are labor-intensive, lack sensitivity, and require extensive investigator training. Here, we describe a sensitive, high throughput, automated, motility-based method for sorting nematodes. Our method is implemented in a simple microfluidic device capable of sorting thousands of animals per hour per module, and is amenable to parallelism. The device successfully enriches for known C. elegans motility mutants. Furthermore, using this device, we isolate low-abundance mutants capable of suppressing the somnogenic effects of the flp-13 gene, which regulates C. elegans sleep. By performing genetic complementation tests, we demonstrate that our motility-based sorting device efficiently isolates mutants for the same gene identified by tedious visual inspection of behavior on an agar surface. Therefore, our motility-based sorter is capable of performing high throughput gene discovery approaches to investigate fundamental biological processes.

CeleST: computer vision software for quantitative analysis of C. elegans swim behavior reveals novel features of locomotion

In the effort to define genes and specific neuronal circuits that control behavior and plasticity, the capacity for high-precision automated analysis of behavior is essential. We report on comprehensive computer vision software for analysis of swimming locomotion of C. elegans, a simple animal model initially developed to facilitate elaboration of genetic influences on behavior. C. elegans swim test software CeleST tracks swimming of multiple animals, measures 10 novel parameters of swim behavior that can fully report dynamic changes in posture and speed, and generates data in several analysis formats, complete with statistics. Our measures of swim locomotion utilize a deformable model approach and a novel mathematical analysis of curvature maps that enable even irregular patterns and dynamic changes to be scored without need for thresholding or dropping outlier swimmers from study. Operation of CeleST is mostly automated and only requires minimal investigator interventions, such as the selection of videotaped swim trials and choice of data output format. Data can be analyzed from the level of the single animal to populations of thousands. We document how the CeleST program reveals unexpected preferences for specific swim “gaits” in wild-type C. elegans, uncovers previously unknown mutant phenotypes, efficiently tracks changes in aging populations, and distinguishes “graceful” from poor aging. The sensitivity, dynamic range, and comprehensive nature of CeleST measures elevate swim locomotion analysis to a new level of ease, economy, and detail that enables behavioral plasticity resulting from genetic, cellular, or experience manipulation to be analyzed in ways not previously possible.

Mechanistic analysis of the search behaviour of Caenorhabditis elegans

A central question in movement research is how animals use information and movement to promote encounter success. Current random search theory identifies reorientation patterns as key to the compromise between optimizing encounters for both nearby and faraway targets, but how the balance between intrinsic motor programmes and previous environmental experience determines the occurrence of these reorientation behaviours remains unknown. We used high-resolution tracking and imaging data to describe the complete motor behaviour of Caenorhabditis elegans when placed in a novel environment (one in which food is absent). Movement in C. elegans is structured around different reorientation behaviours, and we measured how these contributed to changing search strategies as worms became familiar with their new environment. This behavioural transition shows that different reorientation behaviours are governed by two processes: (i) an environmentally informed 'extrinsic' strategy that is influenced by recent experience and that controls for area-restricted search behaviour, and (ii) a time-independent, 'intrinsic' strategy that reduces spatial oversampling and improves random encounter success. Our results show how movement strategies arise from a balance between intrinsic and extrinsic mechanisms, that search behaviour in C. elegans is initially determined by expectations developed from previous environmental experiences, and which reorientation behaviours are modified as information is acquired from new environments.

The geometry of locomotive behavioral states in C. elegans

We develop a new hidden Markov model-based method to analyze C elegans locomotive behavior and use this method to quantitatively characterize behavioral states. In agreement with previous work, we find states corresponding to roaming, dwelling, and quiescence. However, we also find evidence for a continuum of intermediate states. We suggest that roaming, dwelling, and quiescence may best be thought of as extremes which, mixed in any proportion, define the locomotive repertoire of C elegans foraging and feeding behavior.

The microarchitecture of C. elegans behavior during lethargus: homeostatic bout dynamics, a typical body posture, and regulation by a central neuron

STUDY OBJECTIVES

The nematode C. elegans develops through four larval stages before it reaches adulthood. At the transition between stages and before it sheds its cuticle, it exhibits a sleep-like behavior during a stage termed lethargus. The objectives of this study were to characterize in detail behavioral patterns and physiological activity of a command interneuron during lethargus.

MEASUREMENTS AND RESULTS

We found that lethargus behavior was composed of bouts of quiescence and motion. The duration of individual bouts ranged from 2 to 100 seconds, and their dynamics exhibited local homeostasis: the duration of bouts of quiescence positively correlated with the duration of bouts of motion that immediately preceded them in a cAMP-dependent manner. In addition, we identified a characteristic body posture during lethargus: the average curvature along the body of L4 lethargus larvae was lower than that of L4 larvae prior to lethargus, and the positions of body bends were distributed non-uniformly along the bodies of quiescent animals. Finally, we found that the AVA interneurons, a pair of backward command neurons, mediated locomotion patterns during L4 lethargus in similar fashion to their function in L4 larvae prior to lethargus. Interestingly, in both developmental stages backward locomotion was initiated and terminated asymmetrically with respect to AVA intraneuronal calcium concentration.

CONCLUSIONS

The complex behavioral patterns during lethargus can be dissected to quantifiable elements, which exhibit rich temporal dynamics and are actively regulated by the nervous system. Our findings support the identification of lethargus as a sleep-like state.

CITATION

Iwanir S; Tramm N; Nagy S; Wright C; Ish D; Biron D. The microarchitecture of C. elegans behavior during lethargus: homeostatic bout dynamics, a typical body posture, and regulation by a central neuron. SLEEP 2013;36(3):385-395.

Discriminating external and internal causes for heading changes in freely flying Drosophila

As animals move through the world in search of resources, they change course in reaction to both external sensory cues and internally-generated programs. Elucidating the functional logic of complex search algorithms is challenging because the observable actions of the animal cannot be unambiguously assigned to externally- or internally-triggered events. We present a technique that addresses this challenge by assessing quantitatively the contribution of external stimuli and internal processes. We apply this technique to the analysis of rapid turns (“saccades”) of freely flying Drosophila melanogaster. We show that a single scalar feature computed from the visual stimulus experienced by the animal is sufficient to explain a majority (93%) of the turning decisions. We automatically estimate this scalar value from the observable trajectory, without any assumption regarding the sensory processing. A posteriori, we show that the estimated feature field is consistent with previous results measured in other experimental conditions. The remaining turning decisions, not explained by this feature of the visual input, may be attributed to a combination of deterministic processes based on unobservable internal states and purely stochastic behavior. We cannot distinguish these contributions using external observations alone, but we are able to provide a quantitative bound of their relative importance with respect to stimulus-triggered decisions. Our results suggest that comparatively few saccades in free-flying conditions are a result of an intrinsic spontaneous process, contrary to previous suggestions. We discuss how this technique could be generalized for use in other systems and employed as a tool for classifying effects into sensory, decision, and motor categories when used to analyze data from genetic behavioral screens.

Researchers have spent considerable effort studying how specific sensory stimuli elicit behavioral responses and how other behaviors may arise independent of external inputs in conditions of sensory deprivation. Yet an animal in its natural context, such as searching for food or mates, turns both in response to external stimuli and intrinsic, possibly stochastic, decisions. We show how to estimate the contribution of vision and internal causes on the observable behavior of freely flying Drosophila. We developed a dimensionality reduction scheme that finds a one-dimensional feature of the visual stimulus that best predicts turning decisions. This visual feature extraction is consistent with previous literature on visually elicited fly turning and predicts a large majority of turns in the tested environment. The rarity of stimulus-independent events suggests that fly behavior is more deterministic than previously suggested and that, more generally, animal search strategies may be dominated by responses to stimuli with only modest contributions from internal causes.

Optimal intermittence in search strategies under speed-selective target detection

Random search theory has been previously explored for both continuous and intermittent scanning modes with full target detection capacity. Here we present a new class of random search problems in which a single searcher performs flights of random velocities, the detection probability when it passes over a target location being conditioned to the searcher speed. As a result, target detection involves an N-passage process for which the mean search time is here analytically obtained through a renewal approximation. We apply the idea of speed-selective detection to random animal foraging since a fast movement is known to significantly degrade perception abilities in many animals. We show that speed-selective detection naturally introduces an optimal level of behavioral intermittence in order to solve the compromise between fast relocations and target detection capability.

Functional organization of a neural network for aversive olfactory learning in Caenorhabditis elegans

Many animals use their olfactory systems to learn to avoid dangers, but how neural circuits encode naive and learned olfactory preferences, and switch between those preferences, is poorly understood. Here, we map an olfactory network, from sensory input to motor output, which regulates the learned olfactory aversion of Caenorhabditis elegans for the smell of pathogenic bacteria. Naive animals prefer smells of pathogens but animals trained with pathogens lose this attraction. We find that two different neural circuits subserve these preferences, with one required for the naive preference and the other specifically for the learned preference. Calcium imaging and behavioral analysis reveal that the naive preference reflects the direct transduction of the activity of olfactory sensory neurons into motor response, whereas the learned preference involves modulations to signal transduction to downstream neurons to alter motor response. Thus, two different neural circuits regulate a behavioral switch between naive and learned olfactory preferences.

From modes to movement in the behavior of Caenorhabditis elegans

Organisms move through the world by changing their shape, and here we explore the mapping from shape space to movements in the nematode Caenorhabditis elegans as it crawls on an agar plate. We characterize the statistics of the trajectories through the correlation functions of the orientation angular velocity, orientation angle and the mean-squared displacement, and we find that the loss of orientational memory has significant contributions from both abrupt, large amplitude turning events and the continuous dynamics between these events. Further, we discover long-time persistence of orientational memory in the intervals between abrupt turns. Building on recent work demonstrating that C. elegans movements are restricted to a low-dimensional shape space, we construct a map from the dynamics in this shape space to the trajectory of the worm along the agar. We use this connection to illustrate that changes in the continuous dynamics reveal subtle differences in movement strategy that occur among mutants defective in two classes of dopamine receptors.

Genome-wide analysis of light- and temperature-entrained circadian transcripts in Caenorhabditis elegans

Transcriptional profiling experiments identify light- and temperature-entrained circadian transcripts in C. elegans.

Most organisms have an endogenous circadian clock that is synchronized to environmental signals such as light and temperature. Although circadian rhythms have been described in the nematode Caenorhabditis elegans at the behavioral level, these rhythms appear to be relatively non-robust. Moreover, in contrast to other animal models, no circadian transcriptional rhythms have been identified. Thus, whether this organism contains a bona fide circadian clock remains an open question. Here we use genome-wide expression profiling experiments to identify light- and temperature-entrained oscillating transcripts in C. elegans. These transcripts exhibit rhythmic expression with temperature-compensated 24-h periods. In addition, their expression is sustained under constant conditions, suggesting that they are under circadian regulation. Light and temperature cycles strongly drive gene expression and appear to entrain largely nonoverlapping gene sets. We show that mutations in a cyclic nucleotide-gated channel required for sensory transduction abolish both light- and temperature-entrained gene expression, implying that environmental cues act cell nonautonomously to entrain circadian rhythms. Together, these findings demonstrate circadian-regulated transcriptional rhythms in C. elegans and suggest that further analyses in this organism will provide new information about the evolution and function of this biological clock.

Daily (circadian) rhythms in behavior and physiology allow organisms to adapt to periodic cues such as light and temperature associated with the rotation of the earth. Subsets of molecular components of the internal clock that drive these rhythms, as well as effector genes for behavioral outputs, also exhibit rhythmic expression in many organisms. While circadian rhythms in behavior have previously been described in the nematode Caenorhabditis elegans, no transcriptional rhythms or clock genes have been identified, leaving open the question of the nature of the clock in this organism. Here, we identify light- and temperature-entrained cycling genes in C. elegans via genome-wide transcriptional profiling. Transcripts showing circadian regulation (including expression with a 24-h period maintained upon removal of the entraining stimulus) and temperature compensation were identified. Light and temperature appear to entrain independent sets of genes. We also identify large sets of light- or temperature-driven genes. Mutations in a channel gene previously implicated in sensory transduction in a small set of sensory neurons abolish entrainment of gene expression by environmental signals. This work demonstrates the presence of circadian transcriptional rhythms in C. elegans, and provides the foundation for future investigations into the underlying mechanisms.

Specific roles for DEG/ENaC and TRP channels in touch and thermosensation in C. elegans nociceptors

Polymodal nociceptors detect noxious stimuli including harsh touch, toxic chemicals, and extremes of heat and cold. The molecular mechanisms by which nociceptors are able to sense multiple qualitatively distinct stimuli are not well-understood. We show here that the C. elegans PVD neurons are mulitidendritic nociceptors that respond to harsh touch as well as cold temperatures. The harsh touch modality specifically requires the DEG/ENaC proteins MEC-10 and DEGT-1, which represent putative components of a harsh touch mechanotransduction complex. By contrast, responses to cold require the TRPA-1 channel and are MEC-10- and DEGT-1-independent. Heterologous expression of C. elegans TRPA-1 can confer cold responsiveness to other C. elegans neurons or to mammalian cells, indicating that TRPA-1 is itself a cold sensor. These results show that C. elegans nociceptors respond to thermal and mechanical stimuli using distinct sets of molecules, and identify DEG/ENaC channels as potential receptors for mechanical pain.

Caenorhabditis elegans: a model system for systems neuroscience

The nematode Caenorhabditis elegans is an excellent model organism for a systems-level understanding of neural circuits and behavior. Advances in the quantitative analyses of behavior and neuronal activity, and the development of new technologies to precisely control and monitor the workings of interconnected circuits, now allow investigations into the molecular, cellular, and systems-level strategies that transform sensory inputs into precise behavioral outcomes.