Maximally informative foraging by Caenorhabditis elegans

History dependence in insect flight decisions during odor tracking

Natural decision-making often involves extended decision sequences in response to variable stimuli with complex structure. As an example, many animals follow odor plumes to locate food sources or mates, but turbulence breaks up the advected odor signal into intermittent filaments and puffs. This scenario provides an opportunity to ask how animals use sparse, instantaneous, and stochastic signal encounters to generate goal-oriented behavioral sequences. Here we examined the trajectories of flying fruit flies (Drosophila melanogaster) and mosquitoes (Aedes aegypti) navigating in controlled plumes of attractive odorants. While it is known that mean odor-triggered flight responses are dominated by upwind turns, individual responses are highly variable. We asked whether deviations from mean responses depended on specific features of odor encounters, and found that odor-triggered turns were slightly but significantly modulated by two features of odor encounters. First, encounters with higher concentrations triggered stronger upwind turns. Second, encounters occurring later in a sequence triggered weaker upwind turns. To contextualize the latter history dependence theoretically, we examined trajectories simulated from three normative tracking strategies. We found that neither a purely reactive strategy nor a strategy in which the tracker learned the plume centerline over time captured the observed history dependence. In contrast, "infotaxis", in which flight decisions maximized expected information gain about source location, exhibited a history dependence aligned in sign with the data, though much larger in magnitude. These findings suggest that while true plume tracking is dominated by a reactive odor response it might also involve a history-dependent modulation of responses consistent with the accumulation of information about a source over multi-encounter timescales. This suggests that short-term memory processes modulating decision sequences may play a role in natural plume tracking.

Exploratory search during directed navigation in C. elegans and Drosophila larva

Many organisms—from bacteria to nematodes to insect larvae—navigate their environments by biasing random movements. In these organisms, navigation in isotropic environments can be characterized as an essentially diffusive and undirected process. In stimulus gradients, movement decisions are biased to drive directed navigation toward favorable environments. How does directed navigation in a gradient modulate random exploration either parallel or orthogonal to the gradient? Here, we introduce methods originally used for analyzing protein folding trajectories to study the trajectories of the nematode Caenorhabditis elegans and the Drosophila larva in isotropic environments, as well as in thermal and chemical gradients. We find that the statistics of random exploration in any direction are little affected by directed movement along a stimulus gradient. A key constraint on the behavioral strategies of these organisms appears to be the preservation of their capacity to continuously explore their environments in all directions even while moving toward favorable conditions.

Hierarchical compression of Caenorhabditis elegans locomotion reveals phenotypic differences in the organization of behaviour

Regularities in animal behaviour offer insights into the underlying organizational and functional principles of nervous systems and automated tracking provides the opportunity to extract features of behaviour directly from large-scale video data. Yet how to effectively analyse such behavioural data remains an open question. Here, we explore whether a minimum description length principle can be exploited to identify meaningful behaviours and phenotypes. We apply a dictionary compression algorithm to behavioural sequences from the nematode worm Caenorhabditis elegans freely crawling on an agar plate both with and without food and during chemotaxis. We find that the motifs identified by the compression algorithm are rare but relevant for comparisons between worms in different environments, suggesting that hierarchical compression can be a useful step in behaviour analysis. We also use compressibility as a new quantitative phenotype and find that the behaviour of wild-isolated strains of C. elegans is more compressible than that of the laboratory strain N2 as well as the majority of mutant strains examined. Importantly, in distinction to more conventional phenotypes such as overall motor activity or aggregation behaviour, the increased compressibility of wild isolates is not explained by the loss of function of the gene npr-1, which suggests that erratic locomotion is a laboratory-derived trait with a novel genetic basis. Because hierarchical compression can be applied to any sequence, we anticipate that compressibility can offer insights into the organization of behaviour in other animals including humans.

Quantifying behavior to solve sensorimotor transformations: advances from worms and flies

The development of new computational tools has recently opened up the study of natural behaviors at a precision that was previously unachievable. These tools permit a highly quantitative analysis of behavioral dynamics at timescales that are well matched to the timescales of neural activity. Here we examine how combining these methods with established techniques for estimating an animal's sensory experience presents exciting new opportunities for dissecting the sensorimotor transformations performed by the nervous system. We focus this review primarily on examples from Caenorhabditis elegans and Drosophila melanogaster-for these model systems, computational approaches to characterize behavior, in combination with unparalleled genetic tools for neural activation, silencing, and recording, have already proven instrumental for illuminating underlying neural mechanisms.

On texture, form, and fixational eye movements

Recent studies show that small movements of the eye that occur during fixation are controlled in the brain by similar neural mechanisms as large eye movements. Information theory has been successful in explaining many properties of large eye movements. Could it also help us understand the smaller eye movements that are much more difficult to study experimentally? Here I describe new predictions for how small amplitude fixational eye movements should be modulated by visual context in order to improve visual perception. In particular, the amplitude of fixational eye movements is predicted to differ when localizing edges defined by changes in texture or luminance.

Lower vertebrate and invertebrate models of Alzheimer's disease - A review

Alzheimer's disease is a common neurodegenerative disorder which is characterized by the presence of beta- amyloid protein and neurofibrillary tangles (NFTs) in the brain. Till now, various higher vertebrate models have been in use to study the pathophysiology of this disease. But, these models possess some limitations like ethical restrictions, high cost, difficult maintenance of large quantity and lesser reproducibility. Besides, various lower chordate animals like Danio rerio, Drosophila melanogaster, Caenorhabditis elegans and Ciona intestinalis have been proved to be an important model for the in vivo determination of targets of drugs with least limitations. In this article, we reviewed different studies conducted on theses models for the better understanding of the pathophysiology of AD and their subsequent application as a potential tool in the preclinical evaluation of new drugs.

Nonstationary dynamics of encounters: Mean valuable territory covered by a random searcher

Inspired by recent experiments on the organism Caenorhabditis elegans we present a stochastic problem to capture the adaptive dynamics of search in living beings, which involves the exploration-exploitation dilemma between remaining in a previously preferred area and relocating to new places. We assess the question of search efficiency by introducing a new magnitude, the mean valuable territory covered by a Browinan searcher, for the case where each site in the domain becomes valuable only after a random time controlled by a nonhomogeneous rate which expands from the origin outwards. We explore analytically this magnitude for domains of dimensions 1, 2, and 3 and discuss the theoretical and applied (biological) interest of our approach. As the main results here, we (i) report the existence of some universal scaling properties for the mean valuable territory covered as a function of time and (ii) reveal the emergence of an optimal diffusivity which appears only for domains in two and higher dimensions.

Stochastic feeding dynamics arise from the need for information and energy

Animals regulate their food intake in response to the available level of food. Recent observations of feeding dynamics in small animals showed feeding patterns of bursts and pauses, but their function is unknown. Here, we present a data-driven decision-theoretical model of feeding in Caenorhabditis elegans Our central assumption is that food intake serves a dual purpose: to gather information about the external food level and to ingest food when the conditions are good. The model recapitulates experimentally observed feeding patterns. It naturally implements trade-offs between speed versus accuracy and exploration versus exploitation in responding to a dynamic environment. We find that the model predicts three distinct regimes in responding to a dynamical environment, with a transition region where animals respond stochastically to periodic signals. This stochastic response accounts for previously unexplained experimental data.

Locomotion Behavior Is Affected by the GαS Pathway and the Two-Pore-Domain K+ Channel TWK-7 Interacting in GABAergic Motor Neurons in Caenorhabditis elegans

Adjusting the efficiency of movement in response to environmental cues is an essential integrative characteristic of adaptive locomotion behavior across species. However, the modulatory molecules and the pathways involved are largely unknown. Recently, we demonstrated that in Caenorhabditis elegans, a loss-of-function of the two-pore-domain potassium (K₂P) channel TWK-7 causes a fast, coordinated, and persistent forward crawling behavior in which five central aspects of stimulated locomotion-velocity, direction, wave parameters, duration, and straightness-are affected. Here, we isolated the reduction-of-function allele cau1 of the C. elegans gene kin-2 in a forward genetic screen and showed that it phenocopies the locomotor activity and locomotion behavior of twk-7(null) animals. Kin-2 encodes the negative regulatory subunit of protein kinase A (KIN-1/PKA). Consistently, we found that other gain-of-function mutants of the Gα_S-KIN-1/PKA pathway resemble kin-2(cau1) and twk-7(null) in locomotion phenotype. Using the powerful genetics of the C. elegans system in combination with cell type-specific approaches and detailed locomotion analyses, we identified TWK-7 as a putative downstream target of the Gα_S-KIN-1/PKA pathway at the level of the γ-aminobutyric acid (GABA)ergic D-type motor neurons. Due to this epistatic interaction, we suggest that KIN-1/PKA and TWK-7 may share a common pathway that is probably involved in the modulation of both locomotor activity and locomotion behavior during forward crawling.

Farming and public goods production in Caenorhabditis elegans populations

The ecological and evolutionary dynamics of populations are shaped by the strategies they use to produce and use resources. However, our understanding of the interplay between the genetic, behavioral, and environmental factors driving these strategies is limited. Here, we report on a Caenorhabditis elegans-Escherichia coli (worm-bacteria) experimental system in which the worm-foraging behavior leads to a redistribution of the bacterial food source, resulting in a growth advantage for both organisms, similar to that achieved via farming. We show experimentally and theoretically that the increased resource growth represents a public good that can benefit all other consumers, regardless of whether or not they are producers. Mutant worms that cannot farm bacteria benefit from farming by other worms in direct proportion to the fraction of farmers in the worm population. The farming behavior can therefore be exploited if it is associated with either energetic or survival costs. However, when the individuals compete for resources with their own type, these costs can result in an increased population density. Altogether, our findings reveal a previously unrecognized mechanism of public good production resulting from the foraging behavior of C. elegans, which has important population-level consequences. This powerful system may provide broad insight into exploration-exploitation tradeoffs, the resultant ecoevolutionary dynamics, and the underlying genetic and neurobehavioral driving forces of multispecies interactions.

Variability in individual activity bursts improves ant foraging success

Using experimental and computational methods, we study the role of behavioural variability in activity bursts (or temporal activity patterns) for individual and collective regulation of foraging in A. senilis ants. First, foraging experiments were carried out under special conditions (low densities of ants and food and absence of external cues or stimuli) where individual-based strategies are most prevalent. By using marked individuals and recording all foraging trajectories, we were then able to precisely quantify behavioural variability among individuals. Our main conclusions are that (i) variability of ant trajectories (turning angles, speed, etc.) is low compared with variability of temporal activity profiles, and (ii) this variability seems to be driven by plasticity of individual behaviour through time, rather than the presence of fixed behavioural stereotypes or specialists within the group. The statistical measures obtained from these experimental foraging patterns are then used to build a general agent-based model (ABM) which includes the most relevant properties of ant foraging under natural conditions, including recruitment through pheromone communication. Using the ABM, we are able to provide computational evidence that the characteristics of individual variability observed in our experiments can provide a functional advantage (in terms of foraging success) to the group; thus, we propose the biological basis underpinning our observations. Altogether, our study reveals the potential utility of experiments under simplified (laboratory) conditions for understanding information-gathering in biological systems.

Internal states drive nutrient homeostasis by modulating exploration-exploitation trade-off

Internal states can profoundly alter the behavior of animals. A quantitative understanding of the behavioral changes upon metabolic challenges is key to a mechanistic dissection of how animals maintain nutritional homeostasis. We used an automated video tracking setup to characterize how amino acid and reproductive states interact to shape exploitation and exploration decisions taken by adult Drosophila melanogaster. We find that these two states have specific effects on the decisions to stop at and leave proteinaceous food patches. Furthermore, the internal nutrient state defines the exploration-exploitation trade-off: nutrient-deprived flies focus on specific patches while satiated flies explore more globally. Finally, we show that olfaction mediates the efficient recognition of yeast as an appropriate protein source in mated females and that octopamine is specifically required to mediate homeostatic postmating responses without affecting internal nutrient sensing. Internal states therefore modulate specific aspects of exploitation and exploration to change nutrient selection.

DOI:http://dx.doi.org/10.7554/eLife.19920.001

When making decisions, animals, including humans, do not always choose the same option. One reason for this is that their “internal state” changes the value of different options. This is particularly evident when deciding what type of food to eat. Depending on which nutrients the animal needs, it will choose to eat different foods.

Amino acids are key nutrients that affect health, lifespan and reproduction. Female fruit flies that have recently mated, for example, eat more amino acids in order to obtain the raw materials required to produce eggs. Despite the importance of amino acids, little was known about how animal behavior changes in response to a lack of this nutrient.

Corrales-Carvajal et al. used a video tracking system to measure the time that fruit flies – some of which had a need for amino acids – spent feeding on patches of yeast (which are rich in amino acids) versus patches of sucrose. Recently mated females – and virgins that had been fed a diet lacking in amino acids – consumed more yeast than sucrose, whereas virgin females that were not amino acid deficient showed the opposite pattern. To bias the fly toward eating the right food for their needs, several aspects of the fly’s behavior changed, including the number and length of individual feeding bouts. These different behaviors did not all change at the same time.

The pattern of exploration taken by the flies also depended on their need for amino acids. Amino acid deficient flies spent most of their time near known yeast patches. By contrast, fully fed flies adopted a riskier foraging strategy, moving away from known sources of food to explore their environment more widely. In common with humans, the flies relied upon their sense of smell to efficiently identify different types of food.

Overall, the results presented by Corrales-Carvajal et al. provide us with a detailed understanding about how changes to the internal state of the fly affect its behavior. The next step will be to use the powerful genetic tools available for studying fruit flies to reveal the neural circuits and molecular mechanisms that help animals find the types of food that they need.

DOI:http://dx.doi.org/10.7554/eLife.19920.002

Theoretical perspectives on active sensing

A key component of interacting with the world is how to direct ones' sensors so as to extract task-relevant information - a process referred to as active sensing. In this review, we present a framework for active sensing that forms a closed loop between an ideal observer, that extracts task-relevant information from a sequence of observations, and an ideal planner which specifies the actions that lead to the most informative observations. We discuss active sensing as an approximation to exploration in the wider framework of reinforcement learning, and conversely, discuss several sensory, perceptual, and motor processes as approximations to active sensing. Based on this framework, we introduce a taxonomy of sensing strategies, identify hallmarks of active sensing, and discuss recent advances in formalizing and quantifying active sensing.

Foraging success under uncertainty: search tradeoffs and optimal space use

Understanding the structural complexity and the main drivers of animal search behaviour is pivotal to foraging ecology. Yet, the role of uncertainty as a generative mechanism of movement patterns is poorly understood. Novel insights from search theory suggest that organisms should collect and assess new information from the environment by producing complex exploratory strategies. Based on an extension of the first passage time theory, and using simple equations and simulations, we unveil the elementary heuristics behind search behaviour. In particular, we show that normal diffusion is not enough for determining optimal exploratory behaviour but anomalous diffusion is required. Searching organisms go through two critical sequential phases (approach and detection) and experience fundamental search tradeoffs that may limit their encounter rates. Using experimental data, we show that biological search includes elements not fully considered in contemporary physical search theory. In particular, the need to consider search movement as a non-stationary process that brings the organism from one informational state to another. For example, the transition from remaining in an area to departing from it may occur through an exploratory state where cognitive search is challenged. Therefore, a more comprehensive view of foraging ecology requires including current perspectives about movement under uncertainty.

Natural search algorithms as a bridge between organisms, evolution, and ecology

The ability to navigate is a hallmark of living systems, from single cells to higher animals. Searching for targets, such as food or mates in particular, is one of the fundamental navigational tasks many organisms must execute to survive and reproduce. Here, we argue that a recent surge of studies of the proximate mechanisms that underlie search behavior offers a new opportunity to integrate the biophysics and neuroscience of sensory systems with ecological and evolutionary processes, closing a feedback loop that promises exciting new avenues of scientific exploration at the frontier of systems biology.

Sensorimotor Transformations Underlying Variability in Song Intensity during Drosophila Courtship

Diverse animal species, from insects to humans, utilize acoustic signals for communication. Studies of the neural basis for song or speech production have focused almost exclusively on the generation of spectral and temporal patterns, but animals can also adjust acoustic signal intensity when communicating. For example, humans naturally regulate the loudness of speech in accord with a visual estimate of receiver distance. The underlying mechanisms for this ability remain uncharacterized in any system. Here, we show that Drosophila males modulate courtship song amplitude with female distance, and we investigate each stage of the sensorimotor transformation underlying this behavior, from the detection of particular visual stimulus features and the timescales of sensory processing to the modulation of neural and muscle activity that generates song. Our results demonstrate an unanticipated level of control in insect acoustic communication and uncover novel computations and mechanisms underlying the regulation of acoustic signal intensity.

C. elegans

Random search is a behavioral strategy used by organisms from bacteria to humans to locate food that is randomly distributed and undetectable at a distance. We investigated this behavior in the nematode Caenorhabditis elegans, an organism with a small, well-described nervous system. Here we formulate a mathematical model of random search abstracted from the C. elegans connectome and fit to a large-scale kinematic analysis of C. elegans behavior at submicron resolution. The model predicts behavioral effects of neuronal ablations and genetic perturbations, as well as unexpected aspects of wild type behavior. The predictive success of the model indicates that random search in C. elegans can be understood in terms of a neuronal flip-flop circuit involving reciprocal inhibition between two populations of stochastic neurons. Our findings establish a unified theoretical framework for understanding C. elegans locomotion and a testable neuronal model of random search that can be applied to other organisms.

An animal’s ability to rapidly and efficiently locate new sources of food in its environment can mean the difference between life and death. As a result, animals have evolved foraging strategies that are adapted to the distribution and detectability of food sources. Organisms ranging from bacteria to humans use one such strategy, called random search, to locate food that cannot be detected at a distance and that is randomly distributed in their surroundings. The biological mechanisms that underpin random search are relatively well understood in single-cell organisms such as bacteria, but this information tells us little about the mechanisms that are used by animals, which use their nervous system to control their foraging behavior.

Roberts et al. have now investigated the biological basis for random search behavior in a tiny roundworm called Caenorhabditis elegans. This worm forages for pockets of bacteria in decaying plant matter and has a simple and well-understood nervous system. Roberts et al. used information on how the cells in this worm’s nervous system connect together into so-called “neural circuits” to generate a mathematical model of random searching.

The model revealed that the worm’s neural circuitry for random searching can be understood in terms of two groups of neuron-like components that switch randomly between “ON” and “OFF” states. While one group promotes forward movement, the other promotes backward movement, which is associated with a change in search direction. These two groups inhibit each other so that only one group usually is active at a given time. By adjusting this model to reproduce the behavioral records of real worms searching for food, Roberts et al. could predict the key neuronal connections involved. These predictions were then confirmed by taking electrical recordings from neurons. The model could also account for the unexpected behavioral effects that are seen when a neuron in one of these groups was destroyed or altered by a genetic mutation.

These findings thus reveal a biological mechanism for random search behavior in worms that might operate in other animals as well. The findings might also provide future insight into the neural circuits involved in sleep and wakefulness in mammals, which is organized in a similar way.

The Psychology and Neuroscience of Curiosity

Curiosity is a basic element of our cognition, but its biological function, mechanisms, and neural underpinning remain poorly understood. It is nonetheless a motivator for learning, influential in decision-making, and crucial for healthy development. One factor limiting our understanding of it is the lack of a widely agreed upon delineation of what is and is not curiosity. Another factor is the dearth of standardized laboratory tasks that manipulate curiosity in the lab. Despite these barriers, recent years have seen a major growth of interest in both the neuroscience and psychology of curiosity. In this Perspective, we advocate for the importance of the field, provide a selective overview of its current state, and describe tasks that are used to study curiosity and information-seeking. We propose that, rather than worry about defining curiosity, it is more helpful to consider the motivations for information-seeking behavior and to study it in its ethological context.