C. elegans

Hierarchical competing inhibition circuits govern motor stability in C. elegans

Stable movement and efficient motor transition are both crucial for animals to navigate their environments, yet the neural principles underlying these abilities are not fully understood. In free-moving Caenorhabditis elegans, sustained forward locomotion is occasionally interrupted by backward movements, which are believed to result from reciprocal inhibition between the interneurons AVB and AVA. Here, we discovered that hierarchical competing inhibition circuits stabilize spontaneous movement and ensure motor transition. We found that the modulatory interneuron PVP activated AVB to maintain forward locomotion while inhibiting AVA to prevent backward movement. Another interneuron, DVC activates AVA and forms a disinhibition circuit that inhibits PVP, thereby relieving PVP's inhibition of AVA and facilitating backward movement. Notably, these asymmetrical circuit motifs create a higher-order competing inhibition that likely sharpens the motor transition. We also identified cholinergic and glutamatergic synaptic mechanisms underlying these circuits. This study elucidates a key neural principle that controls motor stability in C. elegans.

Biologically Inspired Spatial-Temporal Perceiving Strategies for Spiking Neural Network

A future unmanned system needs the ability to perceive, decide and control in an open dynamic environment. In order to fulfill this requirement, it needs to construct a method with a universal environmental perception ability. Moreover, this perceptual process needs to be interpretable and understandable, so that future interactions between unmanned systems and humans can be unimpeded. However, current mainstream DNN (deep learning neural network)-based AI (artificial intelligence) is a 'black box'. We cannot interpret or understand how the decision is made by these AIs. An SNN (spiking neural network), which is more similar to a biological brain than a DNN, has the potential to implement interpretable or understandable AI. In this work, we propose a neuron group-based structural learning method for an SNN to better capture the spatial and temporal information from the external environment, and propose a time-slicing scheme to better interpret the spatial and temporal information of responses generated by an SNN. Results show that our method indeed helps to enhance the environment perception ability of the SNN, and possesses a certain degree of robustness, enhancing the potential to build an interpretable or understandable AI in the future.

Neuro-cognitive multilevel causal modeling: A framework that bridges the explanatory gap between neuronal activity and cognition

Explaining how neuronal activity gives rise to cognition arguably remains the most significant challenge in cognitive neuroscience. We introduce neuro-cognitive multilevel causal modeling (NC-MCM), a framework that bridges the explanatory gap between neuronal activity and cognition by construing cognitive states as (behaviorally and dynamically) causally consistent abstractions of neuronal states. Multilevel causal modeling allows us to interchangeably reason about the neuronal- and cognitive causes of behavior while maintaining a physicalist (in contrast to a strong dualist) position. We introduce an algorithm for learning cognitive-level causal models from neuronal activation patterns and demonstrate its ability to learn cognitive states of the nematode C. elegans from calcium imaging data. We show that the cognitive-level model of the NC-MCM framework provides a concise representation of the neuronal manifold of C. elegans and its relation to behavior as a graph, which, in contrast to other neuronal manifold learning algorithms, supports causal reasoning. We conclude the article by arguing that the ability of the NC-MCM framework to learn causally interpretable abstractions of neuronal dynamics and their relation to behavior in a purely data-driven fashion is essential for understanding biological systems whose complexity prohibits the development of hand-crafted computational models.

Neural Sequences Underlying Directed Turning in C. elegans

Complex behaviors like navigation rely on sequenced motor outputs that combine to generate effective movement. The brain-wide organization of the circuits that integrate sensory signals to select and execute appropriate motor sequences is not well understood. Here, we characterize the architecture of neural circuits that control C. elegans olfactory navigation. We identify error-correcting turns during navigation and use whole-brain calcium imaging and cell-specific perturbations to determine their neural underpinnings. These turns occur as motor sequences accompanied by neural sequences, in which defined neurons activate in a stereotyped order during each turn. Distinct neurons in this sequence respond to sensory cues, anticipate upcoming turn directions, and drive movement, linking key features of this sensorimotor behavior across time. The neuromodulator tyramine coordinates these sequential brain dynamics. Our results illustrate how neuromodulation can act on a defined neural architecture to generate sequential patterns of activity that link sensory cues to motor actions.

A Markovian dynamics for Caenorhabditis elegans behavior across scales

How do we capture the breadth of behavior in animal movement, from rapid body twitches to aging? Using high-resolution videos of the nematode worm Caenorhabditis elegans, we show that a single dynamics connects posture-scale fluctuations with trajectory diffusion and longer-lived behavioral states. We take short posture sequences as an instantaneous behavioral measure, fixing the sequence length for maximal prediction. Within the space of posture sequences, we construct a fine-scale, maximum entropy partition so that transitions among microstates define a high-fidelity Markov model, which we also use as a means of principled coarse-graining. We translate these dynamics into movement using resistive force theory, capturing the statistical properties of foraging trajectories. Predictive across scales, we leverage the longest-lived eigenvectors of the inferred Markov chain to perform a top-down subdivision of the worm's foraging behavior, revealing both "runs-and-pirouettes" as well as previously uncharacterized finer-scale behaviors. We use our model to investigate the relevance of these fine-scale behaviors for foraging success, recovering a trade-off between local and global search strategies.

A tonically active master neuron modulates mutually exclusive motor states at two timescales

Continuity of behaviors requires animals to make smooth transitions between mutually exclusive behavioral states. Neural principles that govern these transitions are not well understood. Caenorhabditis elegans spontaneously switch between two opposite motor states, forward and backward movement, a phenomenon thought to reflect the reciprocal inhibition between interneurons AVB and AVA. Here, we report that spontaneous locomotion and their corresponding motor circuits are not separately controlled. AVA and AVB are neither functionally equivalent nor strictly reciprocally inhibitory. AVA, but not AVB, maintains a depolarized membrane potential. While AVA phasically inhibits the forward promoting interneuron AVB at a fast timescale, it maintains a tonic, extrasynaptic excitation on AVB over the longer timescale. We propose that AVA, with tonic and phasic activity of opposite polarities on different timescales, acts as a master neuron to break the symmetry between the underlying forward and backward motor circuits. This master neuron model offers a parsimonious solution for sustained locomotion consisted of mutually exclusive motor states.

Behavioral and molecular response of the insect parasitic nematode Steinernema carpocapsae to plant volatiles

Entomopathogenic nematodes (EPNs) use the chemical cues emitted by insects and insect-damaged plants to locate their hosts. Steinernema carpocapsae, a species of EPN, is an established biocontrol agent used against insect pests. Despite its promising potential, the molecular mechanisms underlying its ability to detect plant volatiles remain poorly understood. In this study, we investigated the response of S. carpocapsae infective juveniles (IJs) to 8 different plant volatiles. Among these, carvone was found to be the most attractive volatile compound. To understand the molecular basis of the response of IJs to carvone, we used RNA-Seq technology to identify gene expression changes in response to carvone treatment. Transcriptome analysis revealed 721 differentially expressed genes (DEGs) between carvone-treated and control groups, with 403 genes being significantly upregulated and 318 genes downregulated. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that the responsive DEGs to carvone attraction were mainly involved in locomotion, localization, behavior, response to stimulus, and olfactory transduction. We also identified four upregulated genes of chemoreceptor and response to stimulus that were involved in the response of IJs to carvone attraction. Our results provide insights into the potential transcriptional mechanisms underlying the response of S. carpocapsae to carvone, which can be utilized to develop environmentally friendly strategies for attracting EPNs.

Brain-wide representations of behavior spanning multiple timescales and states in C. elegans

Changes in an animal's behavior and internal state are accompanied by widespread changes in activity across its brain. However, how neurons across the brain encode behavior and how this is impacted by state is poorly understood. We recorded brain-wide activity and the diverse motor programs of freely moving C. elegans and built probabilistic models that explain how each neuron encodes quantitative behavioral features. By determining the identities of the recorded neurons, we created an atlas of how the defined neuron classes in the C. elegans connectome encode behavior. Many neuron classes have conjunctive representations of multiple behaviors. Moreover, although many neurons encode current motor actions, others integrate recent actions. Changes in behavioral state are accompanied by widespread changes in how neurons encode behavior, and we identify these flexible nodes in the connectome. Our results provide a global map of how the cell types across an animal's brain encode its behavior.

Locomotion modulates olfactory learning through proprioception in C. elegans

Locomotor activities can enhance learning, but the underlying circuit and synaptic mechanisms are largely unknown. Here we show that locomotion facilitates aversive olfactory learning in C. elegans by activating mechanoreceptors in motor neurons, and transmitting the proprioceptive information thus generated to locomotion interneurons through antidromic-rectifying gap junctions. The proprioceptive information serves to regulate experience-dependent activities and functional coupling of interneurons that process olfactory sensory information to produce the learning behavior. Genetic destruction of either the mechanoreceptors in motor neurons, the rectifying gap junctions between the motor neurons and locomotion interneurons, or specific inhibitory synapses among the interneurons impairs the aversive olfactory learning. We have thus uncovered an unexpected role of proprioception in a specific learning behavior as well as the circuit, synaptic, and gene bases for this function.

A GNN-based model for capturing spatio-temporal changes in locomotion behaviors of aging C. elegans

Investigating the locomotion of aging C. elegans is an important way for understanding the basic mechanisms behind age-related changes in organisms. However, the locomotion of aging C. elegans is often quantified using insufficient physical variables, which makes it challenging to capture essential dynamics. To study changes in the locomotion pattern of aging C. elegans, we developed a novel data-driven model based on graph neural networks, in which the C. elegans body is modeled as a long chain with interactions within and between adjacent segments, and their interactions are described by high-dimensional variables. Using this model, we discovered that each segment of the C. elegans body generally tends to maintain its locomotion, i.e., tries to keep the bending angle unchanged, and expects to change the locomotion of the adjacent segments. The ability to maintain its locomotion strengthens with age. Besides, a subtle distinguish in the changes in the locomotion pattern of C. elegans at various aging stages were observed. Our model is anticipated to provide a data-driven method for quantifying the changes in the locomotion pattern of aging C. elegans and for mining the underlying causes of these changes.

AWC thermosensory neuron interferes with information processing in a compact circuit regulating temperature-evoked posture dynamics in the nematode Caenorhabditis elegans

Elucidating how individual neurons encode and integrate sensory information to generate a behavior is crucial for understanding neural logic underlying sensory-dependent behavior. In the nematode Caenorhabditis elegans, information flow from sensory input to behavioral output is traceable at single-cell level due to its entirely solved neural connectivity. C. elegans processes the temperature information for regulating behavior consisting of undulatory posture dynamics in a circuit including two thermosensory neurons AFD and AWC, and their postsynaptic interneuron AIY. However, how the information processing in AFD-AWC-AIY circuit generates the posture dynamics remains elusive. To quantitatively evaluate the posture dynamics, we introduce locomotion entropy, which measures bandwidth of the frequency spectrum of the undulatory posture dynamics, and assess how the motor pattern fluctuates. We here found that AWC disorders the information processing in AFD-AWC-AIY circuit for regulating temperature-evoked posture dynamics. Under slow temperature ramp-up, AWC adjusts AFD response, whereby broadening the temperature range in which animals exhibit fluctuating posture undulation. Under rapid temperature ramp-up, AWC increases inter-individual variability in AIY activity and the fluctuating posture undulation. We propose that a compact nervous system recruits a sensory neuron as a fluctuation inducer for regulating sensory-dependent behavior.

Chaotic heteroclinic networks as models of switching behavior in biological systems

Key features of biological activity can often be captured by transitions between a finite number of semi-stable states that correspond to behaviors or decisions. We present here a broad class of dynamical systems that are ideal for modeling such activity. The models we propose are chaotic heteroclinic networks with nontrivial intersections of stable and unstable manifolds. Due to the sensitive dependence on initial conditions, transitions between states are seemingly random. Dwell times, exit distributions, and other transition statistics can be built into the model through geometric design and can be controlled by tunable parameters. To test our model's ability to simulate realistic biological phenomena, we turned to one of the most studied organisms, C. elegans, well known for its limited behavioral states. We reconstructed experimental data from two laboratories, demonstrating the model's ability to quantitatively reproduce dwell times and transition statistics under a variety of conditions. Stochastic switching between dominant states in complex dynamical systems has been extensively studied and is often modeled as Markov chains. As an alternative, we propose here a new paradigm, namely, chaotic heteroclinic networks generated by deterministic rules (without the necessity for noise). Chaotic heteroclinic networks can be used to model systems with arbitrary architecture and size without a commensurate increase in phase dimension. They are highly flexible and able to capture a wide range of transition characteristics that can be adjusted through control parameters.

A set of hub neurons and non-local connectivity features support global brain dynamics in C. elegans

The wiring architecture of neuronal networks is assumed to be a strong determinant of their dynamical computations. An ongoing effort in neuroscience is therefore to generate comprehensive synapse-resolution connectomes alongside brain-wide activity maps. However, the structure-function relationship, i.e., how the anatomical connectome and neuronal dynamics relate to each other on a global scale, remains unsolved. Systematically, comparing graph features in the C. elegans connectome with correlations in nervous system-wide neuronal dynamics, we found that few local connectivity motifs and mostly other non-local features such as triplet motifs and input similarities can predict functional relationships between neurons. Surprisingly, quantities such as connection strength and amount of common inputs do not improve these predictions, suggesting that the network's topology is sufficient. We demonstrate that hub neurons in the connectome are key to these relevant graph features. Consistently, inhibition of multiple hub neurons specifically disrupts brain-wide correlations. Thus, we propose that a set of hub neurons and non-local connectivity features provide an anatomical substrate for global brain dynamics.

Fixed Point Attractor Theory Bridges Structure and Function in C. elegans Neuronal Network

Understanding the structure–function relationship in a neuronal network is one of the major challenges in neuroscience research. Despite increasing researches at circuit connectivity and neural network structure, their structure-based biological interpretability remains unclear. Based on the attractor theory, here we develop an analytical framework that links neural circuit structures and their functions together through fixed point attractor in Caenorhabditis elegans. In this framework, we successfully established the structural condition for the emergence of multiple fixed points in C. elegans connectome. Then we construct a finite state machine to explain how functions related to bistable phenomena at the neural activity and behavioral levels are encoded. By applying the proposed framework to the command circuit in C. elegans, we provide a circuit level interpretation for the forward-reverse switching behaviors. Interestingly, network properties of the command circuit and first layer amphid interneuron circuit can also be inferred from their functions in this framework. Our research indicates the reliability of the fixed point attractor bridging circuit structure and functions, suggesting its potential applicability to more complex neuronal circuits in other species.

Dynamic functional connectivity in the static connectome of Caenorhabditis elegans

A hallmark of adaptive behavior is the ability to flexibly respond to sensory cues. To understand how neural circuits implement this flexibility, it is critical to resolve how a static anatomical connectome can be modulated such that functional connectivity in the network can be dynamically regulated. Here, we review recent work in the roundworm Caenorhabditis elegans on this topic. EM studies have mapped anatomical connectomes of many C. elegans animals, highlighting the level of stereotypy in the anatomical network. Brain-wide calcium imaging and studies of specified neural circuits have uncovered striking flexibility in the functional coupling of neurons. The coupling between neurons is controlled by neuromodulators that act over long timescales. This gives rise to persistent behavioral states that animals switch between, allowing them to generate adaptive behavioral responses across environmental conditions. Thus, the dynamic coupling of neurons enables multiple behavioral states to be encoded in a physically stereotyped connectome.

Motor Rhythm Dissection From the Backward Circuit in C. elegans

Motor rhythm is initiated and sustained by oscillatory neuronal activity. We recently discovered that the A-class excitatory motor neurons (MNs) (A-MNs) function as intrinsic oscillators. They drive backward locomotion by generating rhythmic postsynaptic currents (rPSCs) in body wall muscles. Molecular underpinning of the rPSCs, however, is not fully elucidated. We report here that there are three types of the rPSC patterns, namely the phasic, tonic, and long-lasting, each with distinct kinetics and channel-dependence. The Na⁺ leak channel is required for all rPSC patterns. The tonic rPSCs exhibit strong dependence on the high-voltage-gated Ca²⁺ channels. Three K⁺ channels, the BK-type Ca²⁺-activated K⁺ channel, Na⁺-activated K⁺ channel, and voltage-gated K⁺ channel (Kv4), primarily inhibit tonic and long-lasting rPSCs with varying degrees and preferences. The elaborate regulation of rPSCs by different channels, through increasing or decreasing the rPSCs frequency and/or charge, correlates with the changes in the reversal velocity for respective channel mutants. The molecular dissection of different A-MNs-rPSC components therefore reveals different mechanisms for multiplex motor rhythm.

Primer on Mathematical Modeling in C. elegans

Recently, applications of mathematical and computational models to biological processes have helped investigators to systematically interpret data, test hypotheses built on experimental data, generate new hypotheses, and guide the design of new experiments, protocols, and synthetic biological systems. Availability of diverse quantitative data is a prerequisite for successful mathematical modeling. The ability to acquire high-quality quantitative data for a broad range of biological processes and perform precise perturbation makes C. elegans an ideal model system for such studies. In this primer, we examine the general procedure of modeling biological systems and demonstrate this process using the heat-shock response in C. elegans as a case study. Our goal is to facilitate the initial discussion between worm biologists and their potential collaborators from quantitative disciplines.

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 neural circuit for flexible control of persistent behavioral states

To adapt to their environments, animals must generate behaviors that are closely aligned to a rapidly changing sensory world. However, behavioral states such as foraging or courtship typically persist over long time scales to ensure proper execution. It remains unclear how neural circuits generate persistent behavioral states while maintaining the flexibility to select among alternative states when the sensory context changes. Here, we elucidate the functional architecture of a neural circuit controlling the choice between roaming and dwelling states, which underlie exploration and exploitation during foraging in C. elegans. By imaging ensemble-level neural activity in freely moving animals, we identify stereotyped changes in circuit activity corresponding to each behavioral state. Combining circuit-wide imaging with genetic analysis, we find that mutual inhibition between two antagonistic neuromodulatory systems underlies the persistence and mutual exclusivity of the neural activity patterns observed in each state. Through machine learning analysis and circuit perturbations, we identify a sensory processing neuron that can transmit information about food odors to both the roaming and dwelling circuits and bias the animal towards different states in different sensory contexts, giving rise to context-appropriate state transitions. Our findings reveal a potentially general circuit architecture that enables flexible, sensory-driven control of persistent behavioral states.

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.

Decoding locomotion from population neural activity in moving C. elegans

We investigated the neural representation of locomotion in the nematode C. elegans by recording population calcium activity during movement. We report that population activity more accurately decodes locomotion than any single neuron. Relevant signals are distributed across neurons with diverse tunings to locomotion. Two largely distinct subpopulations are informative for decoding velocity and curvature, and different neurons’ activities contribute features relevant for different aspects of a behavior or different instances of a behavioral motif. To validate our measurements, we labeled neurons AVAL and AVAR and found that their activity exhibited expected transients during backward locomotion. Finally, we compared population activity during movement and immobilization. Immobilization alters the correlation structure of neural activity and its dynamics. Some neurons positively correlated with AVA during movement become negatively correlated during immobilization and vice versa. This work provides needed experimental measurements that inform and constrain ongoing efforts to understand population dynamics underlying locomotion in C. elegans.

A Keller-Segel model for C elegans L1 aggregation

We describe a mathematical model for the aggregation of starved first-stage C elegans larvae (L1s). We propose that starved L1s produce and respond chemotactically to two labile diffusible chemical signals, a short-range attractant and a longer range repellent. This model takes the mathematical form of three coupled partial differential equations, one that describes the movement of the worms and one for each of the chemical signals. Numerical solution of these equations produced a pattern of aggregates that resembled that of worm aggregates observed in experiments. We also describe the identification of a sensory receptor gene, srh-2, whose expression is induced under conditions that promote L1 aggregation. Worms whose srh-2 gene has been knocked out form irregularly shaped aggregates. Our model suggests this phenotype may be explained by the mutant worms slowing their movement more quickly than the wild type.

Chemosensory signal transduction in Caenorhabditis elegans

Chemosensory neurons translate perception of external chemical cues, including odorants, tastants, and pheromones, into information that drives attraction or avoidance motor programs. In the laboratory, robust behavioral assays, coupled with powerful genetic, molecular and optical tools, have made Caenorhabditis elegans an ideal experimental system in which to dissect the contributions of individual genes and neurons to ethologically relevant chemosensory behaviors. Here, we review current knowledge of the neurons, signal transduction molecules and regulatory mechanisms that underlie the response of C. elegans to chemicals, including pheromones. The majority of identified molecules and pathways share remarkable homology with sensory mechanisms in other organisms. With the development of new tools and technologies, we anticipate that continued study of chemosensory signal transduction and processing in C. elegans will yield additional new insights into the mechanisms by which this animal is able to detect and discriminate among thousands of chemical cues with a limited sensory neuron repertoire.

Nonlinear Control in the Nematode C. elegans

Recent whole-brain calcium imaging recordings of the nematode C. elegans have demonstrated that the neural activity associated with behavior is dominated by dynamics on a low-dimensional manifold that can be clustered according to behavioral states. Previous models of C. elegans dynamics have either been linear models, which cannot support the existence of multiple fixed points in the system, or Markov-switching models, which do not describe how control signals in C. elegans neural dynamics can produce switches between stable states. It remains unclear how a network of neurons can produce fast and slow timescale dynamics that control transitions between stable states in a single model. We propose a global, nonlinear control model which is minimally parameterized and captures the state transitions described by Markov-switching models with a single dynamical system. The model is fit by reproducing the timeseries of the dominant PCA mode in the calcium imaging data. Long and short time-scale changes in transition statistics can be characterized via changes in a single parameter in the control model. Some of these macro-scale transitions have experimental correlates to single neuro-modulators that seem to act as biological controls, allowing this model to generate testable hypotheses about the effect of these neuro-modulators on the global dynamics. The theory provides an elegant characterization of control in the neuron population dynamics in C. elegans. Moreover, the mathematical structure of the nonlinear control framework provides a paradigm that can be generalized to more complex systems with an arbitrary number of behavioral states.

Measuring and modeling whole-brain neural dynamics in Caenorhabditis elegans

The compact nervous system of the nematode Caenorhabditis elegans makes it a powerful playground to study how neural dynamics constrained by neuroanatomy generate neural function and behavior. The ability to record neural activity from the whole brain simultaneously in this worm has opened several research avenues and is providing insights into brain-wide neural coding of locomotion, sleep, and other behaviors. We review these findings and the development of new methods, including new microscopes, new genetic tools, and new modeling approaches. We conclude with a discussion of the role of theory in interpreting or driving new experiments in C. elegans and potential paths forward.

Unsupervised learning of control signals and their encodings in Caenorhabditis elegans whole-brain recordings

A major goal of computational neuroscience is to understand the relationship between synapse-level structure and network-level functionality. Caenorhabditis elegans is a model organism to probe this relationship due to the historic availability of the synaptic structure (connectome) and recent advances in whole brain calcium imaging techniques. Recent work has applied the concept of network controllability to neuronal networks, discovering some neurons that are able to drive the network to a certain state. However, previous work uses a linear model of the network dynamics, and it is unclear if the real neuronal network conforms to this assumption. Here, we propose a method to build a global, low-dimensional model of the dynamics, whereby an underlying global linear dynamical system is actuated by temporally sparse control signals. A key novelty of this method is discovering candidate control signals that the network uses to control itself. We analyse these control signals in two ways, showing they are interpretable and biologically plausible. First, these control signals are associated with transitions between behaviours, which were previously annotated via expert-generated features. Second, these signals can be predicted both from neurons previously implicated in behavioural transitions but also additional neurons previously unassociated with these behaviours. The proposed mathematical framework is generic and can be generalized to other neurosensory systems, potentially revealing transitions and their encodings in a completely unsupervised way.

GABAergic motor neurons bias locomotor decision-making in C. elegans

Proper threat-reward decision-making is critical to animal survival. Emerging evidence indicates that the motor system may participate in decision-making but the neural circuit and molecular bases for these functions are little known. We found in C. elegans that GABAergic motor neurons (D-MNs) bias toward the reward behavior in threat-reward decision-making by retrogradely inhibiting a pair of premotor command interneurons, AVA, that control cholinergic motor neurons in the avoidance neural circuit. This function of D-MNs is mediated by a specific ionotropic GABA receptor (UNC-49) in AVA, and depends on electrical coupling between the two AVA interneurons. Our results suggest that AVA are hub neurons where sensory inputs from threat and reward sensory modalities and motor information from D-MNs are integrated. This study demonstrates at single-neuron resolution how motor neurons may help shape threat-reward choice behaviors through interacting with other neurons.

Statistical analysis of unidirectional and reciprocal chemical connections in the C. elegans connectome

We analyze unidirectional and reciprocally connected pairs of neurons in the chemical connectomes of the male and hermaphrodite Caenorhabditis elegans, using recently published data. Our analysis reveals that reciprocal connections provide communication between most neurons with chemical synapses, and comprise on average more synapses than both unidirectional connections and the entire connectome. We further reveal that the C. elegans connectome is wired so that afferent connections onto neurons with large numbers of presynaptic neighbors (in-degree) comprise an above-average number of synapses (synaptic multiplicity). Notably, the larger the in-degree of a neuron the larger the synaptic multiplicity of its afferent connections. Finally, we show that the male forms two times fewer reciprocal connections between sex-shared neurons than the hermaphrodite, but a large number of reciprocal connections with male-specific neurons. These observations provide evidence for Hebbian structural plasticity in the C. elegans.

Isoflurane Exposure in Juvenile Caenorhabditis elegans Causes Persistent Changes in Neuron Dynamics

BACKGROUND

Animal studies demonstrate that anesthetic exposure during neurodevelopment can lead to persistent behavioral impairment. The changes in neuronal function underlying these effects are incompletely understood. Caenorhabditis elegans is well suited for functional imaging of postanesthetic effects on neuronal activity. This study aimed to examine such effects within the neurocircuitry underlying C. elegans locomotion.

METHODS

C. elegans were exposed to 8% isoflurane for 3 h during the neurodevelopmentally critical L1 larval stage. Locomotion was assessed during early and late adulthood. Spontaneous activity was measured within the locomotion command interneuron circuitry using confocal and light-sheet microscopy of the calcium-sensitive fluorophore GCaMP6s.

RESULTS

C. elegans exposed to isoflurane demonstrated attenuation in spontaneous reversal behavior, persisting throughout the animal's lifespan (reversals/min: untreated early adulthood, 1.14 ± 0.42, vs. isoflurane-exposed early adulthood, 0.83 ± 0.55; untreated late adulthood, 1.75 ± 0.64, vs. isoflurane-exposed late adulthood, 1.14 ± 0.68; P = 0.001 and 0.006, respectively; n > 50 animal tracks/condition). Likewise, isoflurane exposure altered activity dynamics in the command interneuron AVA, which mediates crawling reversals. The rate at which AVA transitions between activity states was found to be increased. These anesthetic-induced effects were more pronounced with age (off-to-on activity state transition time (s): untreated early adulthood, 2.5 ± 1.2, vs. isoflurane-exposed early adulthood, 1.9 ± 1.3; untreated late adulthood, 4.6 ± 3.0, vs. isoflurane-exposed late adulthood, 3.0 ± 2.4; P = 0.028 and 0.008, respectively; n > 35 traces acquired from more than 15 animals/condition). Comparable effects were observed throughout the command interneuron circuitry, indicating that isoflurane exposure alters transition rates between behavioral crawling states of the system overall. These effects were modulated by loss-of-function mutations within the FoxO transcription factor daf-16 and by rapamycin-mediated mechanistic Target of Rapamycin (mTOR) inhibition.

CONCLUSIONS

Altered locomotive behavior and activity dynamics indicate a persistent effect on interneuron dynamics and circuit function in C. elegansafter developmental exposure to isoflurane. These effects are modulated by a loss of daf-16 or mTOR activity, consistent with a pathologic activation of stress-response pathways.

Collapse of Global Neuronal States in Caenorhabditis elegans under Isoflurane Anesthesia

BACKGROUND

A comprehensive understanding of how anesthetics facilitate a reversible collapse of system-wide neuronal function requires measurement of neuronal activity with single-cell resolution. Multineuron recording was performed in Caenorhabditis elegans to measure neuronal activity at varying depths of anesthesia. The authors hypothesized that anesthesia is characterized by dyssynchrony between neurons resulting in a collapse of organized system states.

METHODS

Using light-sheet microscopy and transgenic expression of the calcium-sensitive fluorophore GCaMP6s, a majority of neurons (n = 120) in the C. elegans head were simultaneously imaged in vivo and neuronal activity was measured. Neural activity and system-wide dynamics were compared in 10 animals, progressively dosed at 0%, 4%, and 8% isoflurane. System-wide neuronal activity was analyzed using principal component analysis.

RESULTS

Unanesthetized animals display distinct global neuronal states that are reflected in a high degree of correlation (R = 0.196 ± 0.070) between neurons and low-frequency, large-amplitude neuronal dynamics. At 4% isoflurane, the average correlation between neurons is significantly diminished (R = 0.026 ± 0.010; P < 0.0001 vs. unanesthetized) and neuron dynamics shift toward higher frequencies but with smaller dynamic range. At 8% isoflurane, interneuronal correlations indicate that neuronal activity remains uncoordinated (R = 0.053 ± 0.029; P < 0.0001 vs. unanesthetized) with high-frequency dynamics that are even further restricted. Principal component analysis of unanesthetized neuronal activity reveals distinct structure corresponding to known behavioral states. At 4% and 8% isoflurane this structure is lost and replaced with randomized dynamics, as quantified by the percentage of total ensemble variance captured by the first three principal components. In unanesthetized worms, this captured variance is high (88.9 ± 5.4%), reflecting a highly organized system, falling significantly at 4% and 8% isoflurane (57.9 ± 11.2%, P < 0.0001 vs. unanesthetized, and 76.0 ± 7.9%, P < 0.001 vs. unanesthetized, respectively) and corresponding to increased randomization and collapse of system-wide organization.

CONCLUSIONS

Anesthesia with isoflurane in C. elegans corresponds to high-frequency randomization of individual neuron activity, loss of coordination between neurons, and a collapse of system-wide functional organization.

Flexible motor sequence generation during stereotyped escape responses

Complex animal behaviors arise from a flexible combination of stereotyped motor primitives. Here we use the escape responses of the nematode Caenorhabditis elegans to study how a nervous system dynamically explores the action space. The initiation of the escape responses is predictable: the animal moves away from a potential threat, a mechanical or thermal stimulus. But the motor sequence and the timing that follow are variable. We report that a feedforward excitation between neurons encoding distinct motor states underlies robust motor sequence generation, while mutual inhibition between these neurons controls the flexibility of timing in a motor sequence. Electrical synapses contribute to feedforward coupling whereas glutamatergic synapses contribute to inhibition. We conclude that C. elegans generates robust and flexible motor sequences by combining an excitatory coupling and a winner-take-all operation via mutual inhibition between motor modules.

Brain-wide representations of ongoing behavior: a universal principle?

Recent neuronal activity recordings of unprecedented breadth and depth in worms, flies, and mice have uncovered a surprising common feature: brain-wide behavior-related signals. These signals pervade, and even dominate, neuronal populations thought to function primarily in sensory processing. Such convergent findings across organisms suggest that brain-wide representations of behavior might be a universal neuroscientific principle. What purpose(s) do these representations serve? Here we review these findings along with suggested functions, including sensory prediction, context-dependent sensory processing, and, perhaps most speculatively, distributed motor command generation. It appears that a large proportion of the brain's energy and coding capacity is used to represent ongoing behavior; understanding the function of these representations should therefore be a major goal in neuroscience research.

Context-dependent operation of neural circuits underlies a navigation behavior in Caenorhabditis elegans

A free-living nematode Caenorhabditis elegans memorizes an environmental temperature and migrates toward the remembered temperature on a thermal gradient by switching movement up or down the gradient. How does the C. elegans brain, consisting of 302 neurons, achieve this memory-dependent thermotaxis behavior? Here, we addressed this question through large-scale single-cell ablation, high-resolution behavioral analysis, and computational modeling. We found that depending on whether the environmental temperature is below or above the remembered temperature, distinct sets of neurons are responsible to generate opposing motor biases, thereby switching the movement up or down the thermal gradient. Our study indicates that such a context-dependent operation in neural circuits is essential for flexible execution of animal behavior.

The nervous system evaluates environmental cues and adjusts motor output to ensure navigation toward a preferred environment. The nematode Caenorhabditis elegans navigates in the thermal environment and migrates toward its cultivation temperature by moving up or down thermal gradients depending not only on absolute temperature but on relative difference between current and previously experienced cultivation temperature. Although previous studies showed that such thermal context-dependent opposing migration is mediated by bias in frequency and direction of reorientation behavior, the complete neural pathways—from sensory to motor neurons—and their circuit logics underlying the opposing behavioral bias remain elusive. By conducting comprehensive cell ablation, high-resolution behavioral analyses, and computational modeling, we identified multiple neural pathways regulating behavioral components important for thermotaxis, and demonstrate that distinct sets of neurons are required for opposing bias of even single behavioral components. Furthermore, our imaging analyses show that the context-dependent operation is evident in sensory neurons, very early in the neural pathway, and manifested by bidirectional responses of a first-layer interneuron AIB under different thermal contexts. Our results suggest that the contextual differences are encoded among sensory neurons and a first-layer interneuron, processed among different downstream neurons, and lead to the flexible execution of context-dependent behavior.

Synthetic ablations in the C. elegans nervous system

Synthetic lethality, the finding that the simultaneous knockout of two or more individually nonessential genes leads to cell or organism death, has offered a systematic framework to explore cellular function, and also offered therapeutic applications. Yet the concept lacks its parallel in neuroscience—a systematic knowledge base on the role of double or higher order ablations in the functioning of a neural system. Here, we use the framework of network control to systematically predict the effects of ablating neuron pairs and triplets on the gentle touch response. We find that surprisingly small sets of 58 pairs and 46 triplets can reduce muscle controllability in this context, and that these sets are localized in the nervous system in distinct groups. Further, they lead to highly specific experimentally testable predictions about mechanisms of loss of control, and which muscle cells are expected to experience this loss.

“Synthetic lethality” in cell biology is an extreme example of the effects of higher order genetic interactions: The simultaneous knockout of two or more individually nonessential genes leads to cell death. We define a neural analog to this concept in relation to the locomotor response to gentle touch in C. elegans. Two or more neurons are synthetic essential if individually they are not required for this behavior, yet their combination is. We employ a network control approach to systematically assess all pairs and triplets of neurons by their effect on body wall muscle controllability, and find that only surprisingly small sets of neurons are synthetic essential. They are highly localized in the nervous system and predicted to affect control over specific sets of muscles.

A multilayer circuit architecture for the generation of distinct locomotor behaviors in Drosophila

Animals generate diverse motor behaviors, yet how the same motor neurons (MNs) generate two distinct or antagonistic behaviors remains an open question. Here, we characterize Drosophila larval muscle activity patterns and premotor/motor circuits to understand how they generate forward and backward locomotion. We show that all body wall MNs are activated during both behaviors, but a subset of MNs change recruitment timing for each behavior. We used TEM to reconstruct a full segment of all 60 MNs and 236 premotor neurons (PMNs), including differentially-recruited MNs. Analysis of this comprehensive connectome identified PMN-MN 'labeled line' connectivity; PMN-MN combinatorial connectivity; asymmetric neuronal morphology; and PMN-MN circuit motifs that could all contribute to generating distinct behaviors. We generated a recurrent network model that reproduced the observed behaviors, and used functional optogenetics to validate selected model predictions. This PMN-MN connectome will provide a foundation for analyzing the full suite of larval behaviors.

Nested Neuronal Dynamics Orchestrate a Behavioral Hierarchy across Timescales

Classical and modern ethological studies suggest that animal behavior is organized hierarchically across timescales, such that longer-timescale behaviors are composed of specific shorter-timescale actions. Despite progress relating neuronal dynamics to single-timescale behavior, it remains unclear how different timescale dynamics interact to give rise to such higher-order behavioral organization. Here, we show, in the nematode Caenorhabditis elegans, that a behavioral hierarchy spanning three timescales is implemented by nested neuronal dynamics. At the uppermost hierarchical level, slow neuronal population dynamics spanning brain and motor periphery control two faster motor neuron oscillations, toggling them between different activity states and functional roles. At lower hierarchical levels, these faster oscillations are further nested in a manner that enables flexible behavioral control in an otherwise rigid hierarchical framework. Our findings establish nested neuronal activity patterns as a repeated dynamical motif of the C. elegans nervous system, which together implement a controllable hierarchical organization of behavior.

•

Slow dynamics across brain and motor circuits drive upper-hierarchy motor states

•

Fast dynamics in motor circuits drive lower-hierarchy movements within these states

•

Slower dynamics tightly constrain the state and function of faster ones

•

This rigid hierarchy nevertheless enables flexible behavioral control

A GABAergic and peptidergic sleep neuron as a locomotion stop neuron with compartmentalized Ca2+ dynamics

Animals must slow or halt locomotion to integrate sensory inputs or to change direction. In Caenorhabditis elegans, the GABAergic and peptidergic neuron RIS mediates developmentally timed quiescence. Here, we show RIS functions additionally as a locomotion stop neuron. RIS optogenetic stimulation caused acute and persistent inhibition of locomotion and pharyngeal pumping, phenotypes requiring FLP-11 neuropeptides and GABA. RIS photoactivation allows the animal to maintain its body posture by sustaining muscle tone, yet inactivating motor neuron oscillatory activity. During locomotion, RIS axonal Ca2+ signals revealed functional compartmentalization: Activity in the nerve ring process correlated with locomotion stop, while activity in a branch correlated with induced reversals. GABA was required to induce, and FLP-11 neuropeptides were required to sustain locomotion stop. RIS attenuates neuronal activity and inhibits movement, possibly enabling sensory integration and decision making, and exemplifies dual use of one cell across development in a compact nervous system.

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.

Breakdown of Neural Function under Isoflurane Anesthesia: In Vivo, Multineuronal Imaging in Caenorhabditis elegans

WHAT WE ALREADY KNOW ABOUT THIS TOPIC

WHAT THIS ARTICLE TELLS US THAT IS NEW: BACKGROUND:: Previous work on the action of volatile anesthetics has focused at either the molecular level or bulk neuronal measurement such as electroencephalography or functional magnetic resonance imaging. There is a distinct gulf in resolution at the level of cellular signaling within neuronal systems. The authors hypothesize that anesthesia is caused by induced dyssynchrony in cellular signaling rather than suppression of individual neuron activity.

METHODS

Employing confocal microscopy and Caenorhabditis elegans expressing the calcium-sensitive fluorophore GCaMP6s in specific command neurons, the authors measure neuronal activity noninvasively and in parallel within the behavioral circuit controlling forward and reverse crawling. The authors compare neuronal dynamics and coordination in a total of 31 animals under atmospheres of 0, 4, and 8% isoflurane.

RESULTS

When not anesthetized, the interneurons controlling forward or reverse crawling occupy two possible states, with the activity of the "reversal" neurons AVA, AVD, AVE, and RIM strongly intercorrelated, and the "forward" neuron AVB anticorrelated. With exposure to 4% isoflurane and onset of physical quiescence, neuron activity wanders rapidly and erratically through indeterminate states. Neuron dynamics shift toward higher frequencies, and neuron pair correlations within the system are reduced. At 8% isoflurane, physical quiescence continues as neuronal signals show diminished amplitude with little correlation between neurons. Neuronal activity was further studied using statistical tools from information theory to quantify the type of disruption caused by isoflurane. Neuronal signals become noisier and more disordered, as measured by an increase in the randomness of their activity (Shannon entropy). The coordination of the system, measured by whether information exhibited in one neuron is also exhibited in other neurons (multiinformation), decreases significantly at 4% isoflurane (P = 0.00015) and 8% isoflurane (P = 0.0028).

CONCLUSIONS

The onset of anesthesia corresponds with high-frequency randomization of individual neuron activity coupled with induced dyssynchrony and loss of coordination between neurons that disrupts functional signaling.

A quantitative model of conserved macroscopic dynamics predicts future motor commands

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

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

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

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

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

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.

A compressed sensing framework for efficient dissection of neural circuits

A fundamental question in neuroscience is how neural networks generate behavior. The lack of genetic tools and unique promoters to functionally manipulate specific neuronal subtypes makes it challenging to determine the roles of individual subtypes in behavior. We describe a compressed sensing-based framework in combination with non-specific genetic tools to infer candidate neurons controlling behaviors with fewer measurements than previously thought possible. We tested this framework by inferring interneuron subtypes regulating the speed of locomotion of the nematode Caenorhabditis elegans. We developed a real-time stabilization microscope for accurate long-term, high-magnification imaging and targeted perturbation of neural activity in freely moving animals to validate our inferences. We show that a circuit of three interconnected interneuron subtypes, RMG, AVB and SIA control different aspects of locomotion speed as the animal navigates its environment. Our work suggests that compressed sensing approaches can be used to identify key nodes in complex biological networks.

Anthelmintic drug actions in resistant and susceptible C. elegans revealed by electrophysiological recordings in a multichannel microfluidic device

Many anthelmintic drugs used to treat parasitic nematode infections target proteins that regulate electrical activity of neurons and muscles: ion channels (ICs) and neurotransmitter receptors (NTRs). Perturbation of IC/NTR function disrupts worm behavior and can lead to paralysis, starvation, immune attack and expulsion. Limitations of current anthelmintics include a limited spectrum of activity across species and the threat of drug resistance, highlighting the need for new drugs for human and veterinary medicine. Although ICs/NTRs are valuable anthelmintic targets, electrophysiological recordings are not commonly included in drug development pipelines. We designed a medium-throughput platform for recording electropharyngeograms (EPGs)-the electrical signals emitted by muscles and neurons of the pharynx during pharyngeal pumping (feeding)-in Caenorhabditis elegans and parasitic nematodes. The current study in C. elegans expands previous work in several ways. Detecting anthelmintic bioactivity in drugs, compounds or natural products requires robust, sustained pharyngeal pumping under baseline conditions. We generated concentration-response curves for stimulating pumping by perfusing 8-channel microfluidic devices (chips) with the neuromodulator serotonin, or with E. coli bacteria (C. elegans' food in the laboratory). Worm orientation in the chip (head-first vs. tail-first) affected the response to E. coli but not to serotonin. Using a panel of anthelmintics-ivermectin, levamisole and piperazine-targeting different ICs/NTRs, we determined the effects of concentration and treatment duration on EPG activity, and successfully distinguished control (N2) and drug-resistant worms (avr-14; avr-15; glc-1, unc-38 and unc-49). EPG recordings detected anthelmintic activity of drugs that target ICs/NTRs located in the pharynx as well as at extra-pharyngeal sites. A bus-8 mutant with enhanced permeability was more sensitive than controls to drug treatment. These results provide a useful framework for investigators who would like to more easily incorporate electrophysiology as a routine component of their anthelmintic research workflow.

Algorithms for Olfactory Search across Species

Localizing the sources of stimuli is essential. Most organisms cannot eat, mate, or escape without knowing where the relevant stimuli originate. For many, if not most, animals, olfaction plays an essential role in search. While microorganismal chemotaxis is relatively well understood, in larger animals the algorithms and mechanisms of olfactory search remain mysterious. In this symposium, we will present recent advances in our understanding of olfactory search in flies and rodents. Despite their different sizes and behaviors, both species must solve similar problems, including meeting the challenges of turbulent airflow, sampling the environment to optimize olfactory information, and incorporating odor information into broader navigational systems.

Functionally asymmetric motor neurons contribute to coordinating locomotion of Caenorhabditis elegans

Locomotion circuits developed in simple animals, and circuit motifs further evolved in higher animals. To understand locomotion circuit motifs, they must be characterized in many models. The nematode Caenorhabditis elegans possesses one of the best-studied circuits for undulatory movement. Yet, for 1/6^th of the cholinergic motor neurons (MNs), the AS MNs, functional information is unavailable. Ventral nerve cord (VNC) MNs coordinate undulations, in small circuits of complementary neurons innervating opposing muscles. AS MNs differ, as they innervate muscles and other MNs asymmetrically, without complementary partners. We characterized AS MNs by optogenetic, behavioral and imaging analyses. They generate asymmetric muscle activation, enabling navigation, and contribute to coordination of dorso-ventral undulation as well as anterio-posterior bending wave propagation. AS MN activity correlated with forward and backward locomotion, and they functionally connect to premotor interneurons (PINs) for both locomotion regimes. Electrical feedback from AS MNs via gap junctions may affect only backward PINs.

Sensorimotor integration in Caenorhabditis elegans: a reappraisal towards dynamic and distributed computations

The nematode Caenorhabditis elegans is a tractable model system to study locomotion, sensory navigation and decision-making. In its natural habitat, it is thought to navigate complex multisensory environments in order to find food and mating partners, while avoiding threats like predators or toxic environments. While research in past decades has shed much light on the functions and mechanisms of selected sensory neurons, we are just at the brink of understanding how sensory information is integrated by interneuron circuits for action selection in the worm. Recent technological advances have enabled whole-brain Ca2+ imaging and Ca2+ imaging of neuronal activity in freely moving worms. A common principle emerging across multiple studies is that most interneuron activities are tightly coupled to the worm's instantaneous behaviour; notably, these observations encompass neurons receiving direct sensory neuron inputs. The new findings suggest that in the C. elegans brain, sensory and motor representations are integrated already at the uppermost sensory processing layers. Moreover, these results challenge a perhaps more intuitive view of sequential feed-forward sensory pathways that converge onto premotor interneurons and motor neurons. We propose that sensorimotor integration occurs rather in a distributed dynamical fashion. In this perspective article, we will explore this view, discuss the challenges and implications of these discoveries on the interpretation and design of neural activity experiments, and discuss possible functions. Furthermore, we will discuss the broader context of similar findings in fruit flies and rodents, which suggest generalizable principles that can be learnt from this amenable nematode model organism.This article is part of a discussion meeting issue 'Connectome to behaviour: modelling C. elegans at cellular resolution'.

Caenorhabditis elegans excitatory ventral cord motor neurons derive rhythm for body undulation

The intrinsic oscillatory activity of central pattern generators underlies motor rhythm. We review and discuss recent findings that address the origin of Caenorhabditis elegans motor rhythm. These studies propose that the A- and mid-body B-class excitatory motor neurons at the ventral cord function as non-bursting intrinsic oscillators to underlie body undulation during reversal and forward movements, respectively. Proprioception entrains their intrinsic activities, allows phase-coupling between members of the same class motor neurons, and thereby facilitates directional propagation of undulations. Distinct pools of premotor interneurons project along the ventral nerve cord to innervate all members of the A- and B-class motor neurons, modulating their oscillations, as well as promoting their bi-directional coupling. The two motor sub-circuits, which consist of oscillators and descending inputs with distinct properties, form the structural base of dynamic rhythmicity and flexible partition of the forward and backward motor states. These results contribute to a continuous effort to establish a mechanistic and dynamic model of the C. elegans sensorimotor system. C. elegans exhibits rich sensorimotor functions despite a small neuron number. These findings implicate a circuit-level functional compression. By integrating the role of rhythm generation and proprioception into motor neurons, and the role of descending regulation of oscillators into premotor interneurons, this numerically simple nervous system can achieve a circuit infrastructure analogous to that of anatomically complex systems. C. elegans has manifested itself as a compact model to search for general principles of sensorimotor behaviours.This article is part of a discussion meeting issue 'Connectome to behaviour: modelling C. elegans at cellular resolution'.