Maximally informative foraging by Caenorhabditis elegans

Accelerated hermaphrodite maturation on male pheromones suggests a general principle of coordination between larval behavior and development

Environment in general and social signals in particular could alter development. In Caenorhabditis elegans, male pheromones hasten development of hermaphrodite larvae. We show that this involves acceleration of growth and both somatic and germline development during the last larval stage (L4). Larvae exposed to male pheromones spend more time in L3 and less in the quiescent period between L3 and L4. This behavioral alteration improves provision in early L4, likely allowing for faster development. Larvae must be exposed to male pheromones in late L3 for behavioral and developmental effects to occur. Latter portions of other larval stages also contain periods of heightened sensitivity to environmental signals. Behavior during the early part of the larval stages is biased toward exploration, whereas later the emphasis shifts to food consumption. We argue that this organization allows assessment of the environment to identify the most suitable patch of resources, followed by acquisition of sufficient nutrition and salient information for the developmental events in the next larval stage. Evidence from other species indicates that such coordination of behavior and development may be a general feature of larval development.

A vast space of compact strategies for effective decisions

Inference-based decision-making, which underlies a broad range of behavioral tasks, is typically studied using a small number of handcrafted models. We instead enumerate a complete ensemble of strategies that could be used to effectively, but not necessarily optimally, solve a dynamic foraging task. Each strategy is expressed as a behavioral "program" that uses a limited number of internal states to specify actions conditioned on past observations. We show that the ensemble of strategies is enormous-comprising a quarter million programs with up to five internal states-but can nevertheless be understood in terms of algorithmic "mutations" that alter the structure of individual programs. We devise embedding algorithms that reveal how mutations away from a Bayesian-like strategy can diversify behavior while preserving performance, and we construct a compositional description to link low-dimensional changes in algorithmic structure with high-dimensional changes in behavior. Together, this work provides an alternative approach for understanding individual variability in behavior across animals and tasks.

Revealing neural dynamical structure of C. elegans with deep learning

Caenorhabditis elegans serves as a common model for investigating neural dynamics and functions of biological neural networks. Data-driven approaches have been employed in reconstructing neural dynamics. However, challenges remain regarding the curse of high-dimensionality and stochasticity in realistic systems. In this study, we develop a deep neural network (DNN) approach to reconstruct the neural dynamics of C. elegans and study neural mechanisms for locomotion. Our model identifies two limit cycles in the neural activity space: one underpins basic pirouette behavior, essential for navigation, and the other introduces extra Ω turns. The combination of two limit cycles elucidates predominant locomotion patterns in neural imaging data. The corresponding energy landscape explains the switching strategies between two limit cycles, quantitatively, and provides testable predictions on neural functions and circuit roles. Our work provides a general approach to study neural dynamics by combining imaging data and stochastic modeling.

Exploration-exploitation trade-off is regulated by metabolic state and taste value in Drosophila

Similar to other animals, the fly, Drosophila melanogaster, changes its foraging strategy from exploration to exploitation upon encountering a nutrient-rich food source. However, the impact of metabolic state or taste/nutrient value on exploration vs. exploitation decisions in flies is poorly understood. Here, we developed a one-source foraging assay that uses automated video tracking coupled with high-resolution measurements of food ingestion to investigate the behavioral variables flies use when foraging for food with different taste/caloric values and when in different metabolic states. We found that flies alter their foraging and ingestive behaviors based on their hunger state and the concentration of the sucrose solution. Interestingly, sugar-blind flies did not transition from exploration to exploitation upon finding a high-concentration sucrose solution, suggesting that taste sensory input, as opposed to post-ingestive nutrient feedback, plays a crucial role in determining the foraging decisions of flies. Using a Generalized Linear Model (GLM), we showed that hunger state and sugar volume ingested, but not the nutrient or taste value of the food, influence flies' radial distance to the food source, a strong indicator of exploitation. Our behavioral paradigm and theoretical framework offer a promising avenue for investigating the neural mechanisms underlying state and value-based foraging decisions in flies, setting the stage for systematically identifying the neuronal circuits that drive these behaviors.

Mitochondrial energy state controls AMPK-mediated foraging behavior in C. elegans

Organisms surveil and respond to their environment using behaviors entrained by metabolic cues that reflect food availability. Mitochondria act as metabolic hubs and at the center of mitochondrial energy production is the protonmotive force (PMF), an electrochemical gradient generated by metabolite consumption. The PMF serves as a central integrator of mitochondrial status, but its role in governing metabolic signaling is poorly understood. We used optogenetics to dissipate the PMF in Caenorhabditis elegans tissues to test its role in food-related behaviors. Our data demonstrate that PMF reduction in the intestine is sufficient to initiate locomotor responses to acute food deprivation. This behavioral adaptation requires the cellular energy regulator AMP-activated protein kinase (AMPK) in neurons, not in the intestine, and relies on mitochondrial dynamics and axonal trafficking. Our results highlight a role for intestinal PMF as an internal metabolic cue, and we identify a bottom-up signaling axis through which changes in the PMF trigger AMPK activity in neurons to promote foraging behavior.

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.

Network models to enhance the translational impact of cross-species studies

Neuroscience studies are often carried out in animal models for the purpose of understanding specific aspects of the human condition. However, the translation of findings across species remains a substantial challenge. Network science approaches can enhance the translational impact of cross-species studies by providing a means of mapping small-scale cellular processes identified in animal model studies to larger-scale inter-regional circuits observed in humans. In this Review, we highlight the contributions of network science approaches to the development of cross-species translational research in neuroscience. We lay the foundation for our discussion by exploring the objectives of cross-species translational models. We then discuss how the development of new tools that enable the acquisition of whole-brain data in animal models with cellular resolution provides unprecedented opportunity for cross-species applications of network science approaches for understanding large-scale brain networks. We describe how these tools may support the translation of findings across species and imaging modalities and highlight future opportunities. Our overarching goal is to illustrate how the application of network science tools across human and animal model studies could deepen insight into the neurobiology that underlies phenomena observed with non-invasive neuroimaging methods and could simultaneously further our ability to translate findings across species.

Periods of environmental sensitivity couple larval behavior and development

The typical life cycle in most animal phyla includes a larval period that bridges embryogenesis and adulthood1. Despite the great diversity of larval forms, all larvae grow, acquire adult morphology and function, while navigating their habitats to obtain resources necessary for development. How larval development is coordinated with behavior remains substantially unclear. Here, we describe features of the iterative organization of larval stages that serve to assess the environment and procure resources prior to costly developmental commitments. We found that male-excreted pheromones accelerate2-4 the onset of adulthood in C. elegans hermaphrodites by coordinately advancing multiple developmental events and growth during the last larval stage. The larvae are sensitive to the accelerating male pheromones only at the end of the penultimate larval stage, just before the acceleration begins. Other larval stages also contain windows of sensitivity to environmental inputs. Importantly, behaviors associated with search and consumption of food are distinct between early and late portions of larval stages. We infer that each larval stage in C. elegans is subdivided into two epochs: A) global assessment of the environment to identify the most suitable patch and B) consumption of sufficient food and acquisition of salient information for developmental events in the next stage. We predict that in larvae of other species behavior is also divided into distinct epochs optimized either for assessing the habitat or obtaining the resources. Thus, a major role of larval behavior is to coordinate the orderly progression of development in variable environments.

Continuous odor profile monitoring to study olfactory navigation in small animals

Olfactory navigation is observed across species and plays a crucial role in locating resources for survival. In the laboratory, understanding the behavioral strategies and neural circuits underlying odor-taxis requires a detailed understanding of the animal's sensory environment. For small model organisms like Caenorhabditis elegans and larval Drosophila melanogaster, controlling and measuring the odor environment experienced by the animal can be challenging, especially for airborne odors, which are subject to subtle effects from airflow, temperature variation, and from the odor's adhesion, adsorption, or reemission. Here, we present a method to control and measure airborne odor concentration in an arena compatible with an agar substrate. Our method allows continuous controlling and monitoring of the odor profile while imaging animal behavior. We construct stationary chemical landscapes in an odor flow chamber through spatially patterned odorized air. The odor concentration is measured with a spatially distributed array of digital gas sensors. Careful placement of the sensors allows the odor concentration across the arena to be continuously inferred in space and monitored through time. We use this approach to measure the odor concentration that each animal experiences as it undergoes chemotaxis behavior and report chemotaxis strategies for C. elegans and D. melanogaster larvae populations as they navigate spatial odor landscapes.

Multisite regulation integrates multimodal context in sensory circuits to control persistent behavioral states in C. elegans

Maintaining or shifting between behavioral states according to context is essential for animals to implement fitness-promoting strategies. How the integration of internal state, past experience and sensory inputs orchestrates persistent multidimensional behavioral changes remains poorly understood. Here, we show that C. elegans integrates environmental temperature and food availability over different timescales to engage in persistent dwelling, scanning, global or glocal search strategies matching thermoregulatory and feeding needs. Transition between states, in each case, involves regulating multiple processes including AFD or FLP tonic sensory neurons activity, neuropeptide expression and downstream circuit responsiveness. State-specific FLP-6 or FLP-5 neuropeptide signaling acts on a distributed set of inhibitory GPCR(s) to promote scanning or glocal search, respectively, bypassing dopamine and glutamate-dependent behavioral state control. Integration of multimodal context via multisite regulation in sensory circuits might represent a conserved regulatory logic for a flexible prioritization on the valence of multiple inputs when operating persistent behavioral state transitions.

Emerging technologies for behavioral research in changing environments

The first response exhibited by animals to changing environments is typically behavioral. Behavior is thus central to predicting, and mitigating, the impacts that natural and anthropogenic environmental changes will have on populations and, consequently, ecosystems. Yet the inherently multiscale nature of behavior, as well as the complexities associated with inferring how animals perceive their world, and make decisions, has constrained the scope of behavioral research. Major technological advances in electronics and in machine learning, however, provide increasingly powerful means to see, analyze, and interpret behavior in its natural complexity. We argue that these disruptive technologies will foster new approaches that will allow us to move beyond quantitative descriptions and reveal the underlying generative processes that give rise to behavior.

Deep reinforcement learning for the olfactory search POMDP: a quantitative benchmark

The olfactory search POMDP (partially observable Markov decision process) is a sequential decision-making problem designed to mimic the task faced by insects searching for a source of odor in turbulence, and its solutions have applications to sniffer robots. As exact solutions are out of reach, the challenge consists in finding the best possible approximate solutions while keeping the computational cost reasonable. We provide a quantitative benchmarking of a solver based on deep reinforcement learning against traditional POMDP approximate solvers. We show that deep reinforcement learning is a competitive alternative to standard methods, in particular to generate lightweight policies suitable for robots.

Continuous, long-term crawling behavior characterized by a robotic transport system

Detailed descriptions of behavior provide critical insight into the structure and function of nervous systems. In Drosophila larvae and many other systems, short behavioral experiments have been successful in characterizing rapid responses to a range of stimuli at the population level. However, the lack of long-term continuous observation makes it difficult to dissect comprehensive behavioral dynamics of individual animals and how behavior (and therefore the nervous system) develops over time. To allow for long-term continuous observations in individual fly larvae, we have engineered a robotic instrument that automatically tracks and transports larvae throughout an arena. The flexibility and reliability of its design enables controlled stimulus delivery and continuous measurement over developmental time scales, yielding an unprecedented level of detailed locomotion data. We utilize the new system’s capabilities to perform continuous observation of exploratory behavior over a duration of six hours with and without a thermal gradient present, and in a single larva for over 30 hours. Long-term free-roaming behavior and analogous short-term experiments show similar dynamics that take place at the beginning of each experiment. Finally, characterization of larval thermotaxis in individuals reveals a bimodal distribution in navigation efficiency, identifying distinct phenotypes that are obfuscated when only analyzing population averages.

Patient-specific variants of NFU1/NFU-1 disrupt cholinergic signaling in a model of multiple mitochondrial dysfunctions syndrome 1

Neuromuscular dysfunction is a common feature of mitochondrial diseases and frequently presents as ataxia, spasticity and/or dystonia, all of which can severely impact individuals with mitochondrial diseases. Dystonia is one of the most common symptoms of multiple mitochondrial dysfunctions syndrome 1 (MMDS1), a disease associated with mutations in the causative gene (NFU1) that impair iron-sulfur cluster biogenesis. We have generated Caenorhabditis elegans strains that recreated patient-specific point variants in the C. elegans ortholog (nfu-1) that result in allele-specific dysfunction. Each of these mutants, Gly147Arg and Gly166Cys, have altered acetylcholine signaling at neuromuscular junctions, but opposite effects on activity and motility. We found that the Gly147Arg variant was hypersensitive to acetylcholine and that knockdown of acetylcholine release rescued nearly all neuromuscular phenotypes of this variant. In contrast, we found that the Gly166Cys variant caused predominantly postsynaptic acetylcholine hypersensitivity due to an unclear mechanism. These results are important for understanding the neuromuscular conditions of MMDS1 patients and potential avenues for therapeutic intervention.

Visuospatial information foraging describes search behavior in learning latent environmental features

In the real world, making sequences of decisions to achieve goals often depends upon the ability to learn aspects of the environment that are not directly perceptible. Learning these so-called latent features requires seeking information about them. Prior efforts to study latent feature learning often used single decisions, used few features, and failed to distinguish between reward-seeking and information-seeking. To overcome this, we designed a task in which humans and monkeys made a series of choices to search for shapes hidden on a grid. On our task, the effects of reward and information outcomes from uncovering parts of shapes could be disentangled. Members of both species adeptly learned the shapes and preferred to select tiles expected to be informative earlier in trials than previously rewarding ones, searching a part of the grid until their outcomes dropped below the average information outcome-a pattern consistent with foraging behavior. In addition, how quickly humans learned the shapes was predicted by how well their choice sequences matched the foraging pattern, revealing an unexpected connection between foraging and learning. This adaptive search for information may underlie the ability in humans and monkeys to learn latent features to support goal-directed behavior in the long run.

Regulation and modulation of biogenic amine neurotransmission in Drosophila and Caenorhabditis elegans

Neurotransmitters are crucial for the relay of signals between neurons and their target. Monoamine neurotransmitters dopamine (DA), serotonin (5-HT), and histamine are found in both invertebrates and mammals and are known to control key physiological aspects in health and disease. Others, such as octopamine (OA) and tyramine (TA), are abundant in invertebrates. TA is expressed in both Caenorhabditis elegans and Drosophila melanogaster and plays important roles in the regulation of essential life functions in each organism. OA and TA are thought to act as the mammalian homologs of epinephrine and norepinephrine respectively, and when triggered, they act in response to the various stressors in the fight-or-flight response. 5-HT regulates a wide range of behaviors in C. elegans including egg-laying, male mating, locomotion, and pharyngeal pumping. 5-HT acts predominantly through its receptors, of which various classes have been described in both flies and worms. The adult brain of Drosophila is composed of approximately 80 serotonergic neurons, which are involved in modulation of circadian rhythm, feeding, aggression, and long-term memory formation. DA is a major monoamine neurotransmitter that mediates a variety of critical organismal functions and is essential for synaptic transmission in invertebrates as it is in mammals, in which it is also a precursor for the synthesis of adrenaline and noradrenaline. In C. elegans and Drosophila as in mammals, DA receptors play critical roles and are generally grouped into two classes, D1-like and D2-like based on their predicted coupling to downstream G proteins. Drosophila uses histamine as a neurotransmitter in photoreceptors as well as a small number of neurons in the CNS. C. elegans does not use histamine as a neurotransmitter. Here, we review the comprehensive set of known amine neurotransmitters found in invertebrates, and discuss their biological and modulatory functions using the vast literature on both Drosophila and C. elegans. We also suggest the potential interactions between aminergic neurotransmitters systems in the modulation of neurophysiological activity and behavior.

Automatically tracking feeding behavior in populations of foraging C. elegans

Caenorhabditis elegans feeds on bacteria and other small microorganisms which it ingests using its pharynx, a neuromuscular pump. Currently, measuring feeding behavior requires tracking a single animal, indirectly estimating food intake from population-level metrics, or using restrained animals. To enable large throughput feeding measurements of unrestrained, crawling worms on agarose plates at a single worm resolution, we developed an imaging protocol and a complementary image analysis tool called PharaGlow. We image up to 50 unrestrained crawling worms simultaneously and extract locomotion and feeding behaviors. We demonstrate the tool's robustness and high-throughput capabilities by measuring feeding in different use-case scenarios, such as through development, with genetic and chemical perturbations that result in faster and slower pumping, and in the presence or absence of food. Finally, we demonstrate that our tool is capable of long-term imaging by showing behavioral dynamics of mating animals and worms with different genetic backgrounds. The low-resolution fluorescence microscopes required are readily available in C. elegans laboratories, and in combination with our python-based analysis workflow makes this methodology easily accessible. PharaGlow therefore enables the observation and analysis of the temporal dynamics of feeding and locomotory behaviors with high-throughput and precision in a user-friendly system.

Searching for a source without gradients: how good is infotaxis and how to beat it

Infotaxis is a popular search algorithm designed to track a source of odour in a turbulent environment using information provided by odour detections. To exemplify its capabilities, the source-tracking task was framed as a partially observable Markov decision process consisting in finding, as fast as possible, a stationary target hidden in a two-dimensional grid using stochastic partial observations of the target location. Here, we provide an extended review of infotaxis, together with a toolkit for devising better strategies. We first characterize the performance of infotaxis in domains from one dimension to four dimensions. Our results show that, while being suboptimal, infotaxis is reliable (the probability of not reaching the source approaches zero), efficient (the mean search time scales as expected for the optimal strategy) and safe (the tail of the distribution of search times decays faster than any power law, though subexponentially). We then present three possible ways of beating infotaxis, all inspired by methods used in artificial intelligence: tree search, heuristic approximation of the value function, and deep reinforcement learning. The latter is able to find, without any prior human knowledge, the (near) optimal strategy. Altogether, our results provide evidence that the margin of improvement of infotaxis towards the optimal strategy gets smaller as the dimensionality increases.

Mechanisms of Variability Underlying Odor-Guided Locomotion

Changes in locomotion mediated by odors (odor-guided locomotion) are an important mechanism by which animals discover resources important to their survival. Odor-guided locomotion, like most other behaviors, is highly variable. Variability in behavior can arise at many nodes along the circuit that performs sensorimotor transformation. We review these sources of variability in the context of the Drosophila olfactory system. While these sources of variability are important, using a model for locomotion, we show that another important contributor to behavioral variability is the stochastic nature of decision-making during locomotion as well as the persistence of these decisions: Flies choose the speed and curvature stochastically from a distribution and locomote with the same speed and curvature for extended periods. This stochasticity in locomotion will result in variability in behavior even if there is no noise in sensorimotor transformation. Overall, the noise in sensorimotor transformation is amplified by mechanisms of locomotion making odor-guided locomotion in flies highly variable.

Evolution of innate behavioral strategies through competitive population dynamics

Many organism behaviors are innate or instinctual and have been “hard-coded” through evolution. Current approaches to understanding these behaviors model evolution as an optimization problem in which the traits of organisms are assumed to optimize an objective function representing evolutionary fitness. Here, we use a mechanistic birth-death dynamics approach to study the evolution of innate behavioral strategies in a simulated population of organisms. In particular, we performed agent-based stochastic simulations and mean-field analyses of organisms exploring random environments and competing with each other to find locations with plentiful resources. We find that when organism density is low, the mean-field model allows us to derive an effective objective function, predicting how the most competitive phenotypes depend on the exploration-exploitation trade-off between the scarcity of high-resource sites and the increase in birth rate those sites offer organisms. However, increasing organism density alters the most competitive behavioral strategies and precludes the derivation of a well-defined objective function. Moreover, there exists a range of densities for which the coexistence of many phenotypes persists for evolutionarily long times.

The innate, or instinctual, behavioral strategies that populations of organisms employ to navigate their environments and fend for survival are shaped over epochs of evolutionary selection, in contrast to individual behaviors that can change within an individual’s lifetime based on experience and sensory input. Understanding how the interplay between organism and their environment shapes which behavior strategies emerge as the most successful for a population’s survival is a major problem in mathematical biology. Often, evolution is modeled as an optimization process that selects for behaviors that optimize the “fitness” of organisms in their environment. However, the fundamental evolutionary events are stochastic birth and death events, and the most successful organisms that emerge under these dynamics are not always those predicted by fitness-based approaches. In this work, we use agent-based stochastic simulations and mean-field approximations of a mechanistic population dynamics model to investigate the evolution of a population’s innate foraging strategies. In particular, we investigate when an emergent fitness function can be derived and how competition between individuals for resources alters the most successful behavioral strategies and precludes the derivation of a simple fitness function that predicts the most successful behavioral strategies.

Knowledge in Sight: Toddlers Plan Efficient Epistemic Actions by Anticipating Learning Gains

Anticipating the learning consequences of actions is crucial to plan efficient information seeking. Such a capacity is needed for learners to determine which actions are most likely to result in learning. Here, we tested the early ontogeny of the human capacity to anticipate the amount of learning gained from seeing. In study 1, we tested infants' capacity to anticipate the availability of sight. Fourteen-month-old infants (N = 72) were invited to search for a toy hidden inside a container. The participants were faster to attempt at opening a shutter when this action allowed them to see inside the container. Moreover, this effect was specifically observed when seeing inside the container was potentially useful to the participants' goals. Thus, infants anticipated the availability of sight, and they calibrated their information-seeking behaviors accordingly. In studies 2 and 3, we tested toddlers' capacity to anticipate whether data would be cognitively useful for their goals. Two-and-a-half-year-olds (N = 72) had to locate a target character hidden among distractors. The participants flipped the characters more often, and were comparatively faster to initiate this action when it yielded access to visual data allowing them to locate the target. Thus, toddlers planned their information-seeking behaviors by anticipating the cognitive utility of sight. In contrast, toddlers did not calibrate their behaviors to the cognitive usefulness of auditory data. These results suggest that cognitive models of learning guide toddlers' search for information. The early developmental onset of the capacity to anticipate future learning gains is crucial for active learning.

Sensing complementary temporal features of odor signals enhances navigation of diverse turbulent plumes

We and others have shown that during odor plume navigation, walking Drosophila melanogaster bias their motion upwind in response to both the frequency of their encounters with the odor (Demir et al., 2020), and the intermittency of the odor signal, which we define to be the fraction of time the signal is above a detection threshold (Alvarez-Salvado et al., 2018). Here we combine and simplify previous mathematical models that recapitulated these data to investigate the benefits of sensing both of these temporal features, and how these benefits depend on the spatiotemporal statistics of the odor plume. Through agent-based simulations, we find that navigators that only use frequency or intermittency perform well in some environments - achieving maximal performance when gains are near those inferred from experiment - but fail in others. Robust performance across diverse environments requires both temporal modalities. However, we also find a steep tradeoff when using both sensors simultaneously, suggesting a strong benefit to modulating how much each sensor is weighted, rather than using both in a fixed combination across plumes. Finally, we show that the circuitry of the Drosophila olfactory periphery naturally enables simultaneous intermittency and frequency sensing, enhancing robust navigation through a diversity of odor environments. Together, our results suggest that the first stage of olfactory processing selects and encodes temporal features of odor signals critical to real-world navigation tasks.

The role of food odor in invertebrate foraging

Foraging for food is an integral part of animal survival. In small insects and invertebrates, multisensory information and optimized locomotion strategies are used to effectively forage in patchy and complex environments. Here, the importance of olfactory cues for effective invertebrate foraging is discussed in detail. We review how odors are used by foragers to move toward a likely food source and the recent models that describe this sensory-driven behavior. We argue that smell serves a second function by priming an organism for the efficient exploitation of food. By appraising food odors, invertebrates can establish preferences and better adapt to their ecological niches, thereby promoting survival. The smell of food pre-prepares the gastrointestinal system and primes feeding motor programs for more effective ingestion as well. Optimizing resource utilization affects longevity and reproduction as a result, leading to drastic changes in survival. We propose that models of foraging behavior should include odor priming, and illustrate this with a simple toy model based on the marginal value theorem. Lastly, we discuss the novel techniques and assays in invertebrate research that could investigate the interactions between odor sensing and food intake. Overall, the sense of smell is indispensable for efficient foraging and influences not only locomotion, but also organismal physiology, which should be reflected in behavioral modeling.

Modelling learning in C. elegans chemosensory and locomotive circuitry for T-maze navigation

Recently, a new type of Caenorhabditis elegans associative learning was reported, where nematodes learn to reach a target arm in an empty T‐maze, after they have successfully located reward (food) in the same side arm of a similar, baited, training maze. Here, we present a simplified mathematical model of C. elegans chemosensory and locomotive circuitry that replicates C. elegans navigation in a T‐maze and predicts the underlying mechanisms generating maze learning. Based on known neural circuitry, the model circuit responds to food‐released chemical cues by modulating motor neuron activity that drives simulated locomotion. We show that, through modulation of interneuron activity, such a circuit can mediate maze learning by acquiring a turning bias, even after a single training session. Simulated nematode maze navigation during training conditions in food‐baited mazes and during testing conditions in empty mazes is validated by comparing simulated behaviour with new experimental video data, extracted through the implementation of a custom‐made maze tracking algorithm. Our work provides a mathematical framework for investigating the neural mechanisms underlying this novel learning behaviour in C. elegans. Model results predict neuronal components involved in maze and spatial learning and identify target neurons and potential neural mechanisms for future experimental investigations into this learning behaviour.

A conserved neuropeptide system links head and body motor circuits to enable adaptive behavior

Neuromodulators promote adaptive behaviors that are often complex and involve concerted activity changes across circuits that are often not physically connected. It is not well understood how neuromodulatory systems accomplish these tasks. Here, we show that the Caenorhabditis elegans NLP-12 neuropeptide system shapes responses to food availability by modulating the activity of head and body wall motor neurons through alternate G-protein coupled receptor (GPCR) targets, CKR-1 and CKR-2. We show ckr-2 deletion reduces body bend depth during movement under basal conditions. We demonstrate CKR-1 is a functional NLP-12 receptor and define its expression in the nervous system. In contrast to basal locomotion, biased CKR-1 GPCR stimulation of head motor neurons promotes turning during local searching. Deletion of ckr-1 reduces head neuron activity and diminishes turning while specific ckr-1 overexpression or head neuron activation promote turning. Thus, our studies suggest locomotor responses to changing food availability are regulated through conditional NLP-12 stimulation of head or body wall motor circuits.

Behavioral control by depolarized and hyperpolarized states of an integrating neuron

Coordinated transitions between mutually exclusive motor states are central to behavioral decisions. During locomotion, the nematode Caenorhabditis elegans spontaneously cycles between forward runs, reversals, and turns with complex but predictable dynamics. Here, we provide insight into these dynamics by demonstrating how RIM interneurons, which are active during reversals, act in two modes to stabilize both forward runs and reversals. By systematically quantifying the roles of RIM outputs during spontaneous behavior, we show that RIM lengthens reversals when depolarized through glutamate and tyramine neurotransmitters and lengthens forward runs when hyperpolarized through its gap junctions. RIM is not merely silent upon hyperpolarization: RIM gap junctions actively reinforce a hyperpolarized state of the reversal circuit. Additionally, the combined outputs of chemical synapses and gap junctions from RIM regulate forward-to-reversal transitions. Our results indicate that multiple classes of RIM synapses create behavioral inertia during spontaneous locomotion.

Plasticity in gustatory and nociceptive neurons controls decision making in C. elegans salt navigation

A conventional understanding of perception assigns sensory organs the role of capturing the environment. Better sensors result in more accurate encoding of stimuli, allowing for cognitive processing downstream. Here we show that plasticity in sensory neurons mediates a behavioral switch in C. elegans between attraction to NaCl in naïve animals and avoidance of NaCl in preconditioned animals, called gustatory plasticity. Ca²⁺ imaging in ASE and ASH NaCl sensing neurons reveals multiple cell-autonomous and distributed circuit adaptation mechanisms. A computational model quantitatively accounts for observed behaviors and reveals roles for sensory neurons in the control and modulation of motor behaviors, decision making and navigational strategy. Sensory adaptation dynamically alters the encoding of the environment. Rather than encoding the stimulus directly, therefore, we propose that these C. elegans sensors dynamically encode a context-dependent value of the stimulus. Our results demonstrate how adaptive sensory computation can directly control an animal’s behavioral state.

Martijn Dekkers and Felix Salfelder et al. combine experimental approaches and mathematical modeling to determine the contribution of the two main NaCl sensory neurons (termed ASEL and ASER) and the nociceptive neurons (termed ASH) in C. elegans to the context-dependent switching between NaCl attraction and avoidance. Their results show that regulated sensitivity of these sensory neurons to NaCl allows the animal to dynamically modulate its behavioral response and suggest a role for sensory modulation in balancing exploration and exploitation during foraging.

Uncertainty drives deviations in normative foraging decision strategies

Nearly all animals forage to acquire energy for survival through efficient search and resource harvesting. Patch exploitation is a canonical foraging behaviour, but there is a need for more tractable and understandable mathematical models describing how foragers deal with uncertainty. To provide such a treatment, we develop a normative theory of patch foraging decisions, proposing mechanisms by which foraging behaviours emerge in the face of uncertainty. Our model foragers statistically and sequentially infer patch resource yields using Bayesian updating based on their resource encounter history. A decision to leave a patch is triggered when the certainty of the patch type or the estimated yield of the patch falls below a threshold. The time scale over which uncertainty in resource availability persists strongly impacts behavioural variables like patch residence times and decision rules determining patch departures. When patch depletion is slow, as in habitat selection, departures are characterized by a reduction of uncertainty, suggesting that the forager resides in a low-yielding patch. Uncertainty leads patch-exploiting foragers to overharvest (underharvest) patches with initially low (high) resource yields in comparison with predictions of the marginal value theorem. These results extend optimal foraging theory and motivate a variety of behavioural experiments investigating patch foraging behaviour.

Inversion of pheromone preference optimizes foraging in C. elegans

Foraging animals have to locate food sources that are usually patchily distributed and subject to competition. Deciding when to leave a food patch is challenging and requires the animal to integrate information about food availability with cues signaling the presence of other individuals (e.g., pheromones). To study how social information transmitted via pheromones can aid foraging decisions, we investigated the behavioral responses of the model animal Caenorhabditis elegans to food depletion and pheromone accumulation in food patches. We experimentally show that animals consuming a food patch leave it at different times and that the leaving time affects the animal preference for its pheromones. In particular, worms leaving early are attracted to their pheromones, while worms leaving later are repelled by them. We further demonstrate that the inversion from attraction to repulsion depends on associative learning and, by implementing a simple model, we highlight that it is an adaptive solution to optimize food intake during foraging.

Inferring temporal organization of postembryonic development from high-content behavioral tracking

Understanding temporal regulation of development remains an important challenge. Whereas average, species-typical timing of many developmental processes has been established, less is known about inter-individual variability and correlations in timing of specific events. We addressed these questions in the context of postembryonic development in Caenorhabditis elegans. Based on patterns of locomotor activity of freely moving animals, we inferred durations of four larval stages (L1-L4) in over 100 individuals. Analysis of these data supports several conclusions. Individuals have consistently faster or slower rates of development because durations of L1 through L3 stages are positively correlated. The last larval stage, the L4, is less variable than the earlier stages and its duration is largely independent of the rate of early larval development, implying existence of two distinct larval epochs. We describe characteristic patterns of variation and correlation, as well as the fact that stage durations tend to scale relative to total developmental time. This scaling relationship suggests that each larval stage is not limited by an absolute duration, but is instead terminated when a subset of events that must occur prior to adulthood have been completed. The approach described here offers a scalable platform that will facilitate the study of temporal regulation of postembryonic development.

Multi-camera field monitoring reveals costs of learning for parasitoid foraging behaviour

Dynamic conditions in nature have led to the evolution of behavioural traits that allow animals to use information on local circumstances and adjust their behaviour accordingly, for example through learning. Although learning can improve foraging efficiency, the learned information can become unreliable as the environment continues to change. This could lead to potential fitness costs when memories holding such unreliable information persist. Indeed, persistent unreliable memory was found to reduce the foraging efficiency of the parasitoid Cotesia glomerata under laboratory conditions.

Here, we evaluated the effect of such persistent unreliable memory on the foraging behaviour of C. glomerata in the field. This is a critical step in studies of foraging theory, since animal behaviour evolved under the complex conditions present in nature.

Existing methods provide little detail on how parasitoids interact with their environment in the field, therefore we developed a novel multi‐camera system that allowed us to trace parasitoid foraging behaviour in detail. With this multi‐camera system, we studied how persistent unreliable memory affected the foraging behaviour of C. glomerata when these memories led parasitoids to plants infested with non‐host caterpillars in a semi‐field set‐up.

Our results demonstrate that persistent unreliable memory can lead to maladaptive foraging behaviour in C. glomerata under field conditions and increased the likelihood of oviposition in the non‐host caterpillar Mamestra brassica. Furthermore, these time‐ and egg‐related costs can be context dependent, since they rely on the plant species used.

These results provide us with new insight on how animals use previously obtained information in naturally complex and dynamic foraging situations and confirm that costs and benefits of learning depend on the environment animals forage in. Although behavioural studies of small animals in natural habitats remain challenging, novel methods such as our multi‐camera system contribute to understanding the nuances of animal foraging behaviour.

Glutamate signaling from a single sensory neuron mediates experience-dependent bidirectional behavior in Caenorhabditis elegans

Orientation and navigation behaviors of animals are modulated by past experiences. However, little is known about the mechanisms by which sensory inputs are translated into multi-directional orientation behaviors in an experience-dependent manner. Here, we report a neural mechanism for bidirectional salt-concentration chemotaxis of Caenorhabditis elegans. The salt-sensing neuron ASE right (ASER) is always activated by a decrease of salt concentration, while the directionality of reorientation behaviors is inverted depending on previous salt experiences. AIB, the interneuron postsynaptic to ASER, and neurons farther downstream of AIB show experience-dependent bidirectional responses, which are correlated with reorientation behaviors. These bidirectional behavioral and neural responses are mediated by glutamate released from ASER. Glutamate acts through the excitatory glutamate receptor GLR-1 and inhibitory glutamate receptor AVR-14, both acting in AIB. These findings suggest that experience-dependent reorientation behaviors are generated by altering the magnitude of excitatory and inhibitory postsynaptic signals from a sensory neuron to interneurons.

Active perception during angiogenesis: filopodia speed up Notch selection of tip cells in silico and in vivo

How do cells make efficient collective decisions during tissue morphogenesis? Humans and other organisms use feedback between movement and sensing known as 'sensorimotor coordination' or 'active perception' to inform behaviour, but active perception has not before been investigated at a cellular level within organs. Here we provide the first proof of concept in silico/in vivo study demonstrating that filopodia (actin-rich, dynamic, finger-like cell membrane protrusions) play an unexpected role in speeding up collective endothelial decisions during the time-constrained process of 'tip cell' selection during blood vessel formation (angiogenesis). We first validate simulation predictions in vivo with live imaging of zebrafish intersegmental vessel growth. Further simulation studies then indicate the effect is due to the coupled positive feedback between movement and sensing on filopodia conferring a bistable switch-like property to Notch lateral inhibition, ensuring tip selection is a rapid and robust process. We then employ measures from computational neuroscience to assess whether filopodia function as a primitive (basal) form of active perception and find evidence in support. By viewing cell behaviour through the 'basal cognitive lens' we acquire a fresh perspective on the tip cell selection process, revealing a hidden, yet vital time-keeping role for filopodia. Finally, we discuss a myriad of new and exciting research directions stemming from our conceptual approach to interpreting cell behaviour. This article is part of the theme issue 'Basal cognition: multicellularity, neurons and the cognitive lens'.

Identification of Exploration and Exploitation Balance in the Silkmoth Olfactory Search Behavior by Information-Theoretic Modeling

Insects search for and find odor sources as their basic behaviors, such as when looking for food or a mate. This has motivated research to describe how they achieve such behavior under turbulent odor plumes with a small number of neurons. Among different insects, the silk moth has been studied owing to its clear motor response to olfactory input. In past studies, the "programmed behavior" of the silk moth has been modeled as the average duration of a sequence of maneuvers based on the duration of periods without odor hits. However, this model does not fully represent the fine variations in their behavior. In this study, we used silk moth olfactory search trajectories from an experimental virtual reality device. We achieved an accurate input by using optogenetic silk moths that react to blue light. We then modeled such trajectories as a probabilistic learning agent with a belief of possible source locations. We found that maneuvers mismatching the programmed behavior are related to larger entropy decrease, that is, they are more likely to increase the certainty of the belief. This implies that silkmoths include some stochasticity in their search policy to balance the exploration and exploitation of olfactory information by matching or mismatching the programmed behavior model. We believe that this information-theoretic representation of insect behavior is important for the future implementation of olfactory searches in artificial agents such as robots.

A three-dimensional habitat for C. elegans environmental enrichment

As we learn more about the importance of gene-environment interactions and the effects of environmental enrichment, it becomes evident that minimalistic laboratory conditions can affect gene expression patterns and behaviors of model organisms. In the laboratory, Caenorhabditis elegans is generally cultured on two-dimensional, homogeneous agar plates abundantly covered with axenic bacteria culture as a food source. However, in the wild, this nematode thrives in rotting fruits and plant stems feeding on bacteria and small eukaryotes. This contrast in habitat complexity suggests that studying C. elegans in enriched laboratory conditions can deepen our understanding of its fundamental traits and behaviors. Here, we developed a protocol to create three-dimensional habitable scaffolds for trans-generational culture of C. elegans in the laboratory. Using decellularization and sterilization of fruit tissue, we created an axenic environment that can be navigated throughout and where the microbial environment can be strictly controlled. C. elegans were maintained over generations on this habitat, and showed a clear behavioral bias for the enriched environment. As an initial assessment of behavioral variations, we found that dauer populations in scaffolds exhibit high-frequency, complex nictation behavior including group towering and jumping behavior.

Walking Drosophila navigate complex plumes using stochastic decisions biased by the timing of odor encounters

How insects navigate complex odor plumes, where the location and timing of odor packets are uncertain, remains unclear. Here we imaged complex odor plumes simultaneously with freely-walking flies, quantifying how behavior is shaped by encounters with individual odor packets. We found that navigation was stochastic and did not rely on the continuous modulation of speed or orientation. Instead, flies turned stochastically with stereotyped saccades, whose direction was biased upwind by the timing of prior odor encounters, while the magnitude and rate of saccades remained constant. Further, flies used the timing of odor encounters to modulate the transition rates between walks and stops. In more regular environments, flies continuously modulate speed and orientation, even though encounters can still occur randomly due to animal motion. We find that in less predictable environments, where encounters are random in both space and time, walking flies navigate with random walks biased by encounter timing.

Behavioral States

Caenorhabditis elegans' behavioral states, like those of other animals, are shaped by its immediate environment, its past experiences, and by internal factors. We here review the literature on C. elegans behavioral states and their regulation. We discuss dwelling and roaming, local and global search, mate finding, sleep, and the interaction between internal metabolic states and behavior.

Tuning movement for sensing in an uncertain world

While animals track or search for targets, sensory organs make small unexplained movements on top of the primary task-related motions. While multiple theories for these movements exist-in that they support infotaxis, gain adaptation, spectral whitening, and high-pass filtering-predicted trajectories show poor fit to measured trajectories. We propose a new theory for these movements called energy-constrained proportional betting, where the probability of moving to a location is proportional to an expectation of how informative it will be balanced against the movement's predicted energetic cost. Trajectories generated in this way show good agreement with measured trajectories of fish tracking an object using electrosense, a mammal and an insect localizing an odor source, and a moth tracking a flower using vision. Our theory unifies the metabolic cost of motion with information theory. It predicts sense organ movements in animals and can prescribe sensor motion for robots to enhance performance.

The elaboration of exploratory play

We apply a new quantitative method for investigating how children's exploration changes across age in order to gain insight into how exploration unfolds over the course of a human life from a life-history perspective. In this study, different facets of exploratory play were quantified using a novel touchscreen environment across a large sample and wide age range of children in the USA (n = 105, ages = 1 year and 10 months to 12 years and 2 months). In contrast with previous theories that have suggested humans transition from more exploratory to less throughout maturation, we see children transition from less broadly exploratory as toddlers to more efficient and broad as adolescents. Our data cast doubt on the picture of human life history as involving a linear transition from more curious in early childhood to less curious with age. Instead, exploration appears to become more elaborate throughout human childhood. This article is part of the theme issue 'Life history and learning: how childhood, caregiving and old age shape cognition and culture in humans and other animals'.

Hybrid foraging in patchy environments using spatial memory

Efficient random searches are essential to the survival of foragers searching for sparsely distributed targets. Lévy walks have been found to optimize the search over a wide range of constraints. When targets are distributed within patches, generating a spatial memory over the detected targets can be beneficial towards optimizing the search efficiency. Because foragers have limited memory, storing each target location separately is unrealistic. Instead, we propose incrementally learning a spatial distribution in favour of memorizing target locations. We demonstrate that an ensemble of Gaussian mixture models is a suitable candidate for such a spatial distribution. Using this, a hybrid foraging strategy is proposed, which interchanges random searches with informed movement. Informed movement results in displacements towards target locations, and is more likely to occur if the learned spatial distribution is correct. We show that, depending on the strength of the memory effects, foragers optimize search efficiencies by continuous revisitation of non-destructive targets. However, this negatively affects both the target and patch diversity, indicating that memory does not necessarily optimize multi-objective searches. Hence, the benefits of memory depend on the specific goals of the forager. Furthermore, through analysis of the distribution over walking distances of the forager, we show that memory changes the underlying walk characteristics. Specifically, the forager resorts to Brownian motion instead of Lévy walks, due to truncation of the long straight line displacements resulting from memory effects. This study provides a framework that opens up new avenues for investigating memory effects on foraging in sparse environments.

Information generation as a functional basis of consciousness

What is the biological advantage of having consciousness? Functions of consciousness have been elusive due to the subjective nature of consciousness and ample empirical evidence showing the presence of many nonconscious cognitive performances in the human brain. Drawing upon empirical literature, here, we propose that a core function of consciousness be the ability to internally generate representations of events possibly detached from the current sensory input. Such representations are constructed by generative models learned through sensory-motor interactions with the environment. We argue that the ability to generate information underlies a variety of cognitive functions associated with consciousness such as intention, imagination, planning, short-term memory, attention, curiosity, and creativity, all of which contribute to non-reflexive behavior. According to this view, consciousness emerged in evolution when organisms gained the ability to perform internal simulations using internal models, which endowed them with flexible intelligent behavior. To illustrate the notion of information generation, we take variational autoencoders (VAEs) as an analogy and show that information generation corresponds the decoding (or decompression) part of VAEs. In biological brains, we propose that information generation corresponds to top-down predictions in the predictive coding framework. This is compatible with empirical observations that recurrent feedback activations are linked with consciousness whereas feedforward processing alone seems to occur without evoking conscious experience. Taken together, the information generation hypothesis captures many aspects of existing ideas about potential functions of consciousness and provides new perspectives on the functional roles of consciousness.

Coordinated Behavioral and Physiological Responses to a Social Signal Are Regulated by a Shared Neuronal Circuit

Successful reproduction in animals requires orchestration of behavior and physiological processes. Pheromones can induce both "releaser" (behavioral) and "priming" (physiological) effects [1] in vertebrates [2, 3] and invertebrates [4, 5]. Therefore, understanding the mechanisms underlying pheromone responses could reveal how reproduction-related behaviors and physiology are coordinated. Here, we describe a neuronal circuit that couples the reproductive system and behavior in adult Caenorhabditis elegans hermaphrodites. We found that the response of the oogenic germline to the male pheromone requires serotonin signal from NSM and HSN neurons that acts via the mod-1 receptor in AIY and RIF interneurons and is antagonized by pigment-dispersing factor (PDF). Surprisingly, the same neurons and pathways have been previously implicated in regulation of exploratory behavior in the absence of male-produced signals [6]. We demonstrate that male pheromone acts via this circuit in hermaphrodites to reduce exploration and decrease mating latency, thereby tuning multiple fitness-proximal processes. Our results demonstrate how a single circuit could coordinate behavioral and physiological responses to the environment, even those that unfold on different timescales. Our findings suggest the existence of a centralized regulatory mechanism that balances organismal resources between reproductive investment and somatic maintenance.

Switching Gears, Structuring the Right Search Strategy

Nematodes can use local and global search strategies to find food. In this issue of Neuron, López-Cruz et al. (2019) unravel a neural circuit mechanism that allows worms to select and switch between these search modes depending on recent experience of food.

Modelling the ballistic-to-diffusive transition in nematode motility reveals variation in exploratory behaviour across species

A quantitative understanding of organism-level behaviour requires predictive models that can capture the richness of behavioural phenotypes, yet are simple enough to connect with underlying mechanistic processes. Here, we investigate the motile behaviour of nematodes at the level of their translational motion on surfaces driven by undulatory propulsion. We broadly sample the nematode behavioural repertoire by measuring motile trajectories of the canonical laboratory strain Caenorhabditis elegans N2 as well as wild strains and distant species. We focus on trajectory dynamics over time scales spanning the transition from ballistic (straight) to diffusive (random) movement and find that salient features of the motility statistics are captured by a random walk model with independent dynamics in the speed, bearing and reversal events. We show that the model parameters vary among species in a correlated, low-dimensional manner suggestive of a common mode of behavioural control and a trade-off between exploration and exploitation. The distribution of phenotypes along this primary mode of variation reveals that not only the mean but also the variance varies considerably across strains, suggesting that these nematode lineages employ contrasting 'bet-hedging' strategies for foraging.

Environmental Programming of Adult Foraging Behavior in C. elegans

Foraging strategies should be tuned to the expected distribution of resources in the environment. Tuning can occur over generations and lead to genetic differences in innate foraging behavior or over shorter timescales within an individual's lifespan. Both genetically encoded and experience-based strategies are implemented by neural circuits that respond to environmental cues and track internal states. Caenorhabditis elegans exhibit both between-strain genetic differences and within-strain plasticity in foraging. In individuals, changes in foraging are usually short term and based on recent experience. Here, we tested whether developmental experience could permanently alter foraging. We found that, in most wild strains, early-life starvation led to "cautious" foraging strategies, in which exploration is reduced, and these behavioral changes are associated with altered dynamics in a locomotory circuit. Possessing either the derived (domestication-associated) or ancestral allele of the neuroglobin glb-5 determines foraging plasticity. Overall, we show that C. elegans exhibit adaptive developmental plasticity that affects multiple aspects of foraging behavior and leads to changes in a core navigation circuit and that innate foraging traits and plasticity in those traits are genetically separable. VIDEO ABSTRACT.

Foraging as an evidence accumulation process

The patch-leaving problem is a canonical foraging task, in which a forager must decide to leave a current resource in search for another. Theoretical work has derived optimal strategies for when to leave a patch, and experiments have tested for conditions where animals do or do not follow an optimal strategy. Nevertheless, models of patch-leaving decisions do not consider the imperfect and noisy sampling process through which an animal gathers information, and how this process is constrained by neurobiological mechanisms. In this theoretical study, we formulate an evidence accumulation model of patch-leaving decisions where the animal averages over noisy measurements to estimate the state of the current patch and the overall environment. We solve the model for conditions where foraging decisions are optimal and equivalent to the marginal value theorem, and perform simulations to analyze deviations from optimal when these conditions are not met. By adjusting the drift rate and decision threshold, the model can represent different “strategies”, for example an incremental, decremental, or counting strategy. These strategies yield identical decisions in the limiting case but differ in how patch residence times adapt when the foraging environment is uncertain. To describe sub-optimal decisions, we introduce an energy-dependent marginal utility function that predicts longer than optimal patch residence times when food is plentiful. Our model provides a quantitative connection between ecological models of foraging behavior and evidence accumulation models of decision making. Moreover, it provides a theoretical framework for potential experiments which seek to identify neural circuits underlying patch-leaving decisions.

Foraging is a ubiquitous animal behavior, performed by organisms as different as worms, birds, rats, and humans. Although the behavior has been extensively studied, it is not known how the brain processes information obtained during foraging activity to make subsequent foraging decisions. We form an evidence accumulation model of foraging decisions that describes the process through which an animal gathers information and uses it to make foraging decisions. By building on studies of the neural decision mechanisms within systems neuroscience, this model connects the foraging decision process with ecological models of patch-leaving decisions, such as the marginal value theorem. The model suggests the existence of different foraging strategies, which optimize for different environmental conditions and their potential implementation by neural decision making circuits. The model also shows how state-dependence, such as satiation level, can affect evidence accumulation to lead to sub-optimal foraging decisions. Our model provides a framework for future experimental studies which seek to elucidate how neural decision making mechanisms have been shaped by evolutionary forces in an animal’s surrounding environment.

Digging deeper: methodologies for high-content phenotyping in Caenorhabditis elegans

Deep phenotyping is an emerging conceptual paradigm and experimental approach aimed at measuring and linking many aspects of a phenotype to understand its underlying biology. To date, deep phenotyping has been applied mostly in cultured cells and used less in multicellular organisms. However, in the past decade, it has increasingly been recognized that deep phenotyping could lead to a better understanding of how genetics, environment and stochasticity affect the development, physiology and behavior of an organism. The nematode Caenorhabditis elegans is an invaluable model system for studying how genes affect a phenotypic trait, and new technologies have taken advantage of the worm's physical attributes to increase the throughput and informational content of experiments. Coupling of these technical advancements with computational and analytical tools has enabled a boom in deep-phenotyping studies of C. elegans. In this Review, we highlight how these new technologies and tools are digging into the biological origins of complex, multidimensional phenotypes.

Parallel Multimodal Circuits Control an Innate Foraging Behavior

Foraging strategies emerge from genetically encoded programs that are similar across animal species. Here, we examine circuits that control a conserved foraging state, local search behavior after food removal, in Caenorhabditis elegans. We show that local search is triggered by two parallel groups of chemosensory and mechanosensory glutamatergic neurons that detect food-related cues. Each group of sensory neurons suppresses distinct integrating neurons through a G protein-coupled metabotropic glutamate receptor, MGL-1, to release local search. The chemosensory and mechanosensory modules are separate and redundant; glutamate release from either module can drive the full behavior. A transition from local search to global search over several minutes after food removal is associated with two changes in circuit function. First, the spontaneous activity of sensory neurons falls. Second, the motor pattern generator for local search becomes less responsive to sensory input. This multimodal, distributed short-term food memory provides robust control of an innate behavior.

Monkeys are curious about counterfactual outcomes

Many non-human animals show exploratory behaviors. It remains unclear whether any possess human-like curiosity. We previously proposed three criteria for applying the term curiosity to animal behavior: (1) the subject is willing to sacrifice reward to obtain information, (2) the information provides no immediate instrumental or strategic benefit, and (3) the amount the subject is willing to pay depends systematically on the amount of information available. In previous work on information-seeking in animals, information generally predicts upcoming rewards, and animals' decisions may therefore be a byproduct of reinforcement processes. Here we get around this potential confound by taking advantage of macaques' ability to reason counterfactually (that is, about outcomes that could have occurred had the subject chosen differently). Specifically, macaques sacrificed fluid reward to obtain information about counterfactual outcomes. Moreover, their willingness to pay scaled with the information (Shannon entropy) offered by the counterfactual option. These results demonstrate the existence of human-like curiosity in non-human primates according to our criteria, which circumvent several confounds associated with less stringent criteria.