Whole-animal connectomes of both Caenorhabditis elegans sexes

Brain wiring: Love the one you're with

Recent electron microscopy-based connectomes of the Caenorhabditis elegans nervous system provide a new opportunity to test classic models for the development of brain wiring. Statistical analyses now reveal that neuronal adjacencies (the contactome) can partly predict synaptic connectivity (the connectome).

Measures of Information Content during Anesthesia and Emergence in the Caenorhabditis elegans Nervous System

BACKGROUND

Suppression of behavioral and physical responses defines the anesthetized state. This is accompanied, in humans, by characteristic changes in electroencephalogram patterns. However, these measures reveal little about the neuron or circuit-level physiologic action of anesthetics nor how information is trafficked between neurons. This study assessed whether entropy-based metrics can differentiate between the awake and anesthetized state in Caenorhabditis elegans and characterize emergence from anesthesia at the level of interneuronal communication.

METHODS

Volumetric fluorescence imaging measured neuronal activity across a large portion of the C. elegans nervous system at cellular resolution during distinct states of isoflurane anesthesia, as well as during emergence from the anesthetized state. Using a generalized model of interneuronal communication, new entropy metrics were empirically derived that can distinguish the awake and anesthetized states.

RESULTS

This study derived three new entropy-based metrics that distinguish between stable awake and anesthetized states (isoflurane, n = 10) while possessing plausible physiologic interpretations. State decoupling is elevated in the anesthetized state (0%: 48.8 ± 3.50%; 4%: 66.9 ± 6.08%; 8%: 65.1 ± 5.16%; 0% vs. 4%, P < 0.001; 0% vs. 8%, P < 0.001), while internal predictability (0%: 46.0 ± 2.94%; 4%: 27.7 ± 5.13%; 8%: 30.5 ± 4.56%; 0% vs. 4%, P < 0.001; 0% vs. 8%, P < 0.001), and system consistency (0%: 2.64 ± 1.27%; 4%: 0.97 ± 1.38%; 8%: 1.14 ± 0.47%; 0% vs. 4%, P = 0.006; 0% vs. 8%, P = 0.015) are suppressed. These new metrics also resolve to baseline during gradual emergence of C. elegans from moderate levels of anesthesia to the awake state (n = 8). The results of this study show that early emergence from isoflurane anesthesia in C. elegans is characterized by the rapid resolution of an elevation in high frequency activity (n = 8, P = 0.032). The entropy-based metrics mutual information and transfer entropy, however, did not differentiate well between the awake and anesthetized states.

CONCLUSIONS

Novel empirically derived entropy metrics better distinguish the awake and anesthetized states compared to extant metrics and reveal meaningful differences in information transfer characteristics between states.

EDITOR’S PERSPECTIVE

The Conscious Nematode: Exploring Hallmarks of Minimal Phenomenal Consciousness in Caenorhabditis Elegans

While subcellular components of cognition and affectivity that involve the interaction between experience, environment, and physiology -such as learning, trauma, or emotion- are being identified, the physical mechanisms of phenomenal consciousness remain more elusive. We are interested in exploring whether ancient, simpler organisms such as nematodes have minimal consciousness. Is there something that feels like to be a worm? Or are worms blind machines? 'Simpler' models allow us to simultaneously extract data from multiple levels such as slow and fast neural dynamics, structural connectivity, molecular dynamics, behavior, decision making, etc., and thus, to test predictions of the current frameworks in dispute. In the present critical review, we summarize the current models of consciousness in order to reassess in light of the new evidence whether Caenorhabditis elegans, a nematode with a nervous system composed of 302 neurons, has minimal consciousness. We also suggest empirical paths to further advance consciousness research using C. elegans.

A robotic system for automated genetic manipulation and analysis of Caenorhabditis elegans

The nematode Caenorhabditis elegans is one of the most widely studied organisms in biology due to its small size, rapid life cycle, and manipulable genetics. Research with C. elegans depends on labor-intensive and time-consuming manual procedures, imposing a major bottleneck for many studies, especially for those involving large numbers of animals. Here, we describe a general-purpose tool, WormPicker, a robotic system capable of performing complex genetic manipulations and other tasks by imaging, phenotyping, and transferring C. elegans on standard agar media. Our system uses a motorized stage to move an imaging system and a robotic arm over an array of agar plates. Machine vision tools identify animals and assay developmental stage, morphology, sex, expression of fluorescent reporters, and other phenotypes. Based on the results of these assays, the robotic arm selectively transfers individual animals using an electrically self-sterilized wire loop, with the aid of machine vision and electrical capacitance sensing. Automated C. elegans manipulation shows reliability and throughput comparable with standard manual methods. We developed software to enable the system to autonomously carry out complex protocols. To validate the effectiveness and versatility of our methods, we used the system to perform a collection of common C. elegans procedures, including genetic crossing, genetic mapping, and genomic integration of a transgene. Our robotic system will accelerate C. elegans research and open possibilities for performing genetic and pharmacological screens that would be impractical using manual methods.

Serotonergic signaling plays a deeply conserved role in improving oocyte quality

Declining germline quality is a major cause of reproductive senescence. Potential remedies could be found by studying regulatory pathways that promote germline quality. Several lines of evidence, including a C. elegans male pheromone ascr#10 that counteracts the effects of germline aging in hermaphrodites, suggest that the nervous system plays an important role in regulating germline quality. Inspired by the fact that serotonin mediates ascr#10 signaling, here we show that serotonin reuptake inhibitors recapitulate the effects of ascr#10 on the germline and promote healthy oocyte aging in C. elegans. Surprisingly, we found that pharmacological increase of serotonin signaling stimulates several developmental processes in D. melanogaster, including improved oocyte quality, although underlying mechanisms appear to be different between worms and flies. Our results reveal a plausibly conserved role for serotonin in maintaining germline quality and identify a class of therapeutic interventions using available compounds that could efficiently forestall reproductive aging.

Bisected graph matching improves automated pairing of bilaterally homologous neurons from connectomes

Graph matching algorithms attempt to find the best correspondence between the nodes of two networks. These techniques have been used to match individual neurons in nanoscale connectomes-in particular, to find pairings of neurons across hemispheres. However, since graph matching techniques deal with two isolated networks, they have only utilized the ipsilateral (same hemisphere) subgraphs when performing the matching. Here, we present a modification to a state-of-the-art graph matching algorithm that allows it to solve what we call the bisected graph matching problem. This modification allows us to leverage the connections between the brain hemispheres when predicting neuron pairs. Via simulations and experiments on real connectome datasets, we show that this approach improves matching accuracy when sufficient edge correlation is present between the contralateral (between hemisphere) subgraphs. We also show how matching accuracy can be further improved by combining our approach with previously proposed extensions to graph matching, which utilize edge types and previously known neuron pairings. We expect that our proposed method will improve future endeavors to accurately match neurons across hemispheres in connectomes, and be useful in other applications where the bisected graph matching problem arises.

Sleep is required to consolidate odor memory and remodel olfactory synapses

Animals with complex nervous systems demand sleep for memory consolidation and synaptic remodeling. Here, we show that, although the Caenorhabditis elegans nervous system has a limited number of neurons, sleep is necessary for both processes. In addition, it is unclear if, in any system, sleep collaborates with experience to alter synapses between specific neurons and whether this ultimately affects behavior. C. elegans neurons have defined connections and well-described contributions to behavior. We show that spaced odor-training and post-training sleep induce long-term memory. Memory consolidation, but not acquisition, requires a pair of interneurons, the AIYs, which play a role in odor-seeking behavior. In worms that consolidate memory, both sleep and odor conditioning are required to diminish inhibitory synaptic connections between the AWC chemosensory neurons and the AIYs. Thus, we demonstrate in a living organism that sleep is required for events immediately after training that drive memory consolidation and alter synaptic structures.

Learning the Biochemical Basis of Axonal Guidance: Using Caenorhabditis elegans as a Model

AIM

Experimental models are a powerful aid in visualizing molecular phenomena. This work reports how the worm Caenorhabditis elegans (C. elegans) can be effectively explored for students to learn how molecular cues dramatically condition axonal guidance and define nervous system structure and behavior at the organism level. Summary of work: A loosely oriented observational activity preceded detailed discussions on molecules implied in axonal migration. C. elegans mutants were used to introduce second-year medical students to the deleterious effects of gene malfunctioning in neuron response to extracellular biochemical cues and to establish links between molecular function, nervous system structure, and animal behavior. Students observed C. elegans cultures and associated animal behavior alterations with the lack of function of specific axon guidance molecules (the soluble cue netrin/UNC-6 or two receptors, DCC/UNC-40 and UNC-5H). Microscopical observations of these strains, in combination with pan-neuronal GFP expression, allowed optimal visualization of severely affected neurons. Once the list of mutated genes in each strain was displayed, students could also relate abnormal patterns in axon migration/ventral and dorsal nerve cord neuron formation in C. elegans with mutated molecular components homologous to those in humans.

SUMMARY OF RESULTS

Students rated the importance and effectiveness of the activity very highly. Ninety-three percent found it helpful to grasp human axonal migration, and all students were surprised with the power of the model in helping to visualize the phenomenon.

High-resolution volumetric imaging constrains compartmental models to explore synaptic integration and temporal processing by cochlear nucleus globular bushy cells

Globular bushy cells (GBCs) of the cochlear nucleus play central roles in the temporal processing of sound. Despite investigation over many decades, fundamental questions remain about their dendrite structure, afferent innervation, and integration of synaptic inputs. Here, we use volume electron microscopy (EM) of the mouse cochlear nucleus to construct synaptic maps that precisely specify convergence ratios and synaptic weights for auditory nerve innervation and accurate surface areas of all postsynaptic compartments. Detailed biophysically based compartmental models can help develop hypotheses regarding how GBCs integrate inputs to yield their recorded responses to sound. We established a pipeline to export a precise reconstruction of auditory nerve axons and their endbulb terminals together with high-resolution dendrite, soma, and axon reconstructions into biophysically detailed compartmental models that could be activated by a standard cochlear transduction model. With these constraints, the models predict auditory nerve input profiles whereby all endbulbs onto a GBC are subthreshold (coincidence detection mode), or one or two inputs are suprathreshold (mixed mode). The models also predict the relative importance of dendrite geometry, soma size, and axon initial segment length in setting action potential threshold and generating heterogeneity in sound-evoked responses, and thereby propose mechanisms by which GBCs may homeostatically adjust their excitability. Volume EM also reveals new dendritic structures and dendrites that lack innervation. This framework defines a pathway from subcellular morphology to synaptic connectivity, and facilitates investigation into the roles of specific cellular features in sound encoding. We also clarify the need for new experimental measurements to provide missing cellular parameters, and predict responses to sound for further in vivo studies, thereby serving as a template for investigation of other neuron classes.

Dissecting the functional organization of the C. elegans serotonergic system at whole-brain scale

Serotonin influences many aspects of animal behavior. But how serotonin acts on its diverse receptors across the brain to modulate global activity and behavior is unknown. Here, we examine how serotonin release in C. elegans alters brain-wide activity to induce foraging behaviors, like slow locomotion and increased feeding. Comprehensive genetic analyses identify three core serotonin receptors (MOD-1, SER-4, and LGC-50) that induce slow locomotion upon serotonin release and others (SER-1, SER-5, and SER-7) that interact with them to modulate this behavior. SER-4 induces behavioral responses to sudden increases in serotonin release, whereas MOD-1 induces responses to persistent release. Whole-brain imaging reveals widespread serotonin-associated brain dynamics, spanning many behavioral networks. We map all sites of serotonin receptor expression in the connectome, which, together with synaptic connectivity, helps predict which neurons show serotonin-associated activity. These results reveal how serotonin acts at defined sites across a connectome to modulate brain-wide activity and behavior.

Embodied cognitive morphogenesis as a route to intelligent systems

The embryological view of development is that coordinated gene expression, cellular physics and migration provides the basis for phenotypic complexity. This stands in contrast with the prevailing view of embodied cognition, which claims that informational feedback between organisms and their environment is key to the emergence of intelligent behaviours. We aim to unite these two perspectives as embodied cognitive morphogenesis, in which morphogenetic symmetry breaking produces specialized organismal subsystems which serve as a substrate for the emergence of autonomous behaviours. As embodied cognitive morphogenesis produces fluctuating phenotypic asymmetry and the emergence of information processing subsystems, we observe three distinct properties: acquisition, generativity and transformation. Using a generic organismal agent, such properties are captured through models such as tensegrity networks, differentiation trees and embodied hypernetworks, providing a means to identify the context of various symmetry-breaking events in developmental time. Related concepts that help us define this phenotype further include concepts such as modularity, homeostasis and 4E (embodied, enactive, embedded and extended) cognition. We conclude by considering these autonomous developmental systems as a process called connectogenesis, connecting various parts of the emerged phenotype into an approach useful for the analysis of organisms and the design of bioinspired computational agents.

Cyclic structure with cellular precision in a vertebrate sensorimotor neural circuit

Neuronal wiring diagrams reconstructed by electron microscopy1,2,3,4,5 pose new questions about the organization of nervous systems following the time-honored tradition of cross-species comparisons.6,7 The C. elegans connectome has been conceptualized as a sensorimotor circuit that is approximately feedforward,8,9,10,11 starting from sensory neurons proceeding to interneurons and ending with motor neurons. Overrepresentation of a 3-cell motif often known as the "feedforward loop" has provided further evidence for feedforwardness.10,12 Here, we contrast with another sensorimotor wiring diagram that was recently reconstructed from a larval zebrafish brainstem.13 We show that the 3-cycle, another 3-cell motif, is highly overrepresented in the oculomotor module of this wiring diagram. This is a first for any neuronal wiring diagram reconstructed by electron microscopy, whether invertebrate12,14 or mammalian.15,16,17 The 3-cycle of cells is "aligned" with a 3-cycle of neuronal groups in a stochastic block model (SBM)18 of the oculomotor module. However, the cellular cycles exhibit more specificity than can be explained by the group cycles-recurrence to the same neuron is surprisingly common. Cyclic structure could be relevant for theories of oculomotor function that depend on recurrent connectivity. The cyclic structure coexists with the classic vestibulo-ocular reflex arc for horizontal eye movements,19 and could be relevant for recurrent network models of temporal integration by the oculomotor system.20,21.

Muscle-directed mechanosensory feedback activates egg-laying circuit activity and behavior in Caenorhabditis elegans

Mechanosensory feedback of the internal reproductive state drives decisions about when and where to reproduce.1 For instance, stretch in the Drosophila reproductive tract produced by artificial distention or from accumulated eggs regulates the attraction to acetic acid to ensure optimal oviposition.2 How such mechanosensory feedback modulates neural circuits to coordinate reproductive behaviors is incompletely understood. We previously identified a stretch-dependent homeostat that regulates egg laying in Caenorhabditis elegans. Sterilized animals lacking eggs show reduced Ca2+ transient activity in the presynaptic HSN command motoneurons that drive egg-laying behavior, while animals forced to accumulate extra eggs show dramatically increased circuit activity that restores egg laying.3 Interestingly, genetic ablation or electrical silencing of the HSNs delays, but does not abolish, the onset of egg laying,3,4,5 with animals recovering vulval muscle Ca2+ transient activity upon egg accumulation.6 Using an acute gonad microinjection technique to mimic changes in pressure and stretch resulting from germline activity and egg accumulation, we find that injection rapidly stimulates Ca2+ activity in both neurons and muscles of the egg-laying circuit. Injection-induced vulval muscle Ca2+ activity requires L-type Ca2+ channels but is independent of presynaptic input. Conversely, injection-induced neural activity is disrupted in mutants lacking the vulval muscles, suggesting "bottom-up" feedback from muscles to neurons. Direct mechanical prodding activates the vulval muscles, suggesting that they are the proximal targets of the stretch-dependent stimulus. Our results show that egg-laying behavior in C. elegans is regulated by a stretch-dependent homeostat that scales postsynaptic muscle responses with egg accumulation in the uterus.

Towards reliable reconstruction of the mouse brain corticothalamic connectivity using diffusion MRI

Diffusion magnetic resonance imaging (dMRI) tractography has yielded intriguing insights into brain circuits and their relationship to behavior in response to gene mutations or neurological diseases across a number of species. Still, existing tractography approaches suffer from limited sensitivity and specificity, leading to uncertain interpretation of the reconstructed connections. Hence, in this study, we aimed to optimize the imaging and computational pipeline to achieve the best possible spatial overlaps between the tractography and tracer-based axonal projection maps within the mouse brain corticothalamic network. We developed a dMRI-based atlas of the mouse forebrain with structural labels imported from the Allen Mouse Brain Atlas (AMBA). Using the atlas and dMRI tractography, we first reconstructed detailed node-to-node mouse brain corticothalamic structural connectivity matrices using different imaging and tractography parameters. We then investigated the effects of each condition for accurate reconstruction of the corticothalamic projections by quantifying the similarities between the tractography and the tracer data from the Allen Mouse Brain Connectivity Atlas (AMBCA). Our results suggest that these parameters significantly affect tractography outcomes and our atlas can be used to investigate macroscopic structural connectivity in the mouse brain. Furthermore, tractography in mouse brain gray matter still face challenges and need improved imaging and tractography methods.

Multigenerational exposure of cadmium trans-generationally impairs locomotive and chemotactic behaviors in Caenorhabditis elegans

Cadmium is a naturally existing heavy metal and a notorious environmental pollutant. While its toxic outcomes and underlying mechanisms remain largely elusive. To explore the behavioral change caused by multigenerational exposure of cadmium to C. elegans, we challenged the C. elegans with cadmium for six generations and observed its impact on animal behaviors. Wild-type worms were randomly divided into two groups, the control and cadmium exposure groups. Locomotive and chemotactic behaviors were observed across six generations. Head thrashing frequency, chemotaxis index, and fold change index were used to evaluate the neurotoxicity of multigenerational cadmium exposure. Multigenerational cadmium exposure can trans-generationally increase the head thrashing frequency of C. elegans during swimming, and impair the chemotactic behaviors to isoamyl alcohol, diacetyl, and 2-nonanone. Our findings proposed a trans-generationally behavioral impact induced by multigenerational cadmium exposure.

Temporal transitions in the postembryonic nervous system of the nematode Caenorhabditis elegans: Recent insights and open questions

After the generation, differentiation and integration into functional circuitry, post-mitotic neurons continue to change certain phenotypic properties throughout postnatal juvenile stages until an animal has reached a fully mature state in adulthood. We will discuss such changes in the context of the nervous system of the nematode C. elegans, focusing on recent descriptions of anatomical and molecular changes that accompany postembryonic maturation of neurons. We summarize the characterization of genetic timer mechanisms that control these temporal transitions or maturational changes, and discuss that many but not all of these transitions relate to sexual maturation of the animal. We describe how temporal, spatial and sex-determination pathways are intertwined to sculpt the emergence of cell-type specific maturation events. Finally, we lay out several unresolved questions that should be addressed to move the field forward, both in C. elegans and in vertebrates.

Comparative connectomics and escape behavior in larvae of closely related Drosophila species

Evolution has generated an enormous variety of morphological, physiological, and behavioral traits in animals. How do behaviors evolve in different directions in species equipped with similar neurons and molecular components? Here we adopted a comparative approach to investigate the similarities and differences of escape behaviors in response to noxious stimuli and their underlying neural circuits between closely related drosophilid species. Drosophilids show a wide range of escape behaviors in response to noxious cues, including escape crawling, stopping, head casting, and rolling. Here we find that D. santomea, compared with its close relative D. melanogaster, shows a higher probability of rolling in response to noxious stimulation. To assess whether this behavioral difference could be attributed to differences in neural circuitry, we generated focused ion beam-scanning electron microscope volumes of the ventral nerve cord of D. santomea to reconstruct the downstream partners of mdIV, a nociceptive sensory neuron in D. melanogaster. Along with partner interneurons of mdVI (including Basin-2, a multisensory integration neuron necessary for rolling) previously identified in D. melanogaster, we identified two additional partners of mdVI in D. santomea. Finally, we showed that joint activation of one of the partners (Basin-1) and a common partner (Basin-2) in D. melanogaster increased rolling probability, suggesting that the high rolling probability in D. santomea is mediated by the additional activation of Basin-1 by mdIV. These results provide a plausible mechanistic explanation for how closely related species exhibit quantitative differences in the likelihood of expressing the same behavior.

Neuronal contact predicts connectivity in the C. elegans brain

Axons must project to particular brain regions, contact adjacent neurons, and choose appropriate synaptic targets to form a nervous system. Multiple mechanisms have been proposed to explain synaptic partnership choice. In a "lock-and-key" mechanism, first proposed by Sperry's chemoaffinity model,¹ a neuron selectively chooses a synaptic partner among several different, adjacent target cells, based on a specific molecular recognition code.² Alternatively, Peters' rule posits that neurons indiscriminately form connections with other neuron types in their proximity; hence, neighborhood choice, determined by initial neuronal process outgrowth and position, is the main predictor of connectivity.^3,4 However, whether Peters' rule plays an important role in synaptic wiring remains unresolved.⁵ To assess the nanoscale relationship between neuronal adjacency and connectivity, we evaluate the expansive set of C. elegans connectomes. We find that synaptic specificity can be accurately modeled as a process mediated by a neurite adjacency threshold and brain strata, offering strong support for Peters' rule as an organizational principle of C. elegans brain wiring.

Connecting Connectomes to Physiology

With the advent of volumetric EM techniques, large connectomic datasets are being created, providing neuroscience researchers with knowledge about the full connectivity of neural circuits under study. This allows for numerical simulation of detailed, biophysical models of each neuron participating in the circuit. However, these models typically include a large number of parameters, and insight into which of these are essential for circuit function is not readily obtained. Here, we review two mathematical strategies for gaining insight into connectomics data: linear dynamical systems analysis and matrix reordering techniques. Such analytical treatment can allow us to make predictions about time constants of information processing and functional subunits in large networks.SIGNIFICANCE STATEMENT This viewpoint provides a concise overview on how to extract important insights from Connectomics data by mathematical methods. First, it explains how new dynamics and new time constants can evolve, simply through connectivity between neurons. These new time-constants can be far longer than the intrinsic membrane time-constants of the individual neurons. Second, it summarizes how structural motifs in the circuit can be discovered. Specifically, there are tools to decide whether or not a circuit is strictly feed-forward or whether feed-back connections exist. Only by reordering connectivity matrices can such motifs be made visible.

A proprioceptive feedback circuit drives Caenorhabditis elegans locomotor adaptation through dopamine signaling

An animal adapts its motor behavior to navigate the external environment. This adaptation depends on proprioception, which provides feedback on an animal's body postures. How proprioception mechanisms interact with motor circuits and contribute to locomotor adaptation remains unclear. Here, we describe and characterize proprioception-mediated homeostatic control of undulatory movement in the roundworm Caenorhabditis elegans. We found that the worm responds to optogenetically or mechanically induced decreases in midbody bending amplitude by increasing its anterior amplitude. Conversely, it responds to increased midbody amplitude by decreasing the anterior amplitude. Using genetics, microfluidic and optogenetic perturbation response analyses, and optical neurophysiology, we elucidated the neural circuit underlying this compensatory postural response. The dopaminergic PDE neurons proprioceptively sense midbody bending and signal to AVK interneurons via the D2-like dopamine receptor DOP-3. The FMRFamide-like neuropeptide FLP-1, released by AVK, regulates SMB head motor neurons to modulate anterior bending. We propose that this homeostatic behavioral control optimizes locomotor efficiency. Our findings demonstrate a mechanism in which proprioception works with dopamine and neuropeptide signaling to mediate motor control, a motif that may be conserved in other animals.

retro-Tango enables versatile retrograde circuit tracing in Drosophila

Transsynaptic tracing methods are crucial tools in studying neural circuits. Although a couple of anterograde tracing methods and a targeted retrograde tool have been developed in Drosophila melanogaster, there is still need for an unbiased, user-friendly, and flexible retrograde tracing system. Here, we describe retro-Tango, a method for transsynaptic, retrograde circuit tracing and manipulation in Drosophila. In this genetically encoded system, a ligand-receptor interaction at the synapse triggers an intracellular signaling cascade that results in reporter gene expression in presynaptic neurons. Importantly, panneuronal expression of the elements of the cascade renders this method versatile, enabling its use not only to test hypotheses but also to generate them. We validate retro-Tango in various circuits and benchmark it by comparing our findings with the electron microscopy reconstruction of the Drosophila hemibrain. Our experiments establish retro-Tango as a key method for circuit tracing in neuroscience research.

Principles for coding associative memories in a compact neural network

A major goal in neuroscience is to elucidate the principles by which memories are stored in a neural network. Here, we have systematically studied how four types of associative memories (short- and long-term memories, each as positive and negative associations) are encoded within the compact neural network of Caenorhabditis elegans worms. Interestingly, sensory neurons were primarily involved in coding short-term, but not long-term, memories, and individual sensory neurons could be assigned to coding either the conditioned stimulus or the experience valence (or both). Moreover, when considering the collective activity of the sensory neurons, the specific training experiences could be decoded. Interneurons integrated the modulated sensory inputs and a simple linear combination model identified the experience-specific modulated communication routes. The widely distributed memory suggests that integrated network plasticity, rather than changes to individual neurons, underlies the fine behavioral plasticity. This comprehensive study reveals basic memory-coding principles and highlights the central roles of sensory neurons in memory formation.

Neural engineering with photons as synaptic transmitters

Neuronal computation is achieved through connections of individual neurons into a larger network. To expand the repertoire of endogenous cellular communication, we developed a synthetic, photon-assisted synaptic transmission (PhAST) system. PhAST is based on luciferases and channelrhodopsins that enable the transmission of a neuronal state across space, using photons as neurotransmitters. PhAST overcomes synaptic barriers and rescues the behavioral deficit of a glutamate mutant with conditional, calcium-triggered photon emission between two neurons of the Caenorhabditis elegans nociceptive avoidance circuit. To demonstrate versatility and flexibility, we generated de novo synaptic transmission between two unconnected cells in a sexually dimorphic neuronal circuit, suppressed endogenous nocifensive response through activation of an anion channelrhodopsin and switched attractive to aversive behavior in an olfactory circuit. Finally, we applied PhAST to dissect the calcium dynamics of the temporal pattern generator in a motor circuit for ovipositioning. In summary, we established photon-based synaptic transmission that facilitates the modification of animal behavior.

Structure and function in artificial, zebrafish and human neural networks

Network science provides a set of tools for the characterization of the structure and functional behavior of complex systems. Yet a major problem is to quantify how the structural domain is related to the dynamical one. In other words, how the diversity of dynamical states of a system can be predicted from the static network structure? Or the reverse problem: starting from a set of signals derived from experimental recordings, how can one discover the network connections or the causal relations behind the observed dynamics? Despite the advances achieved over the last two decades, many challenges remain concerning the study of the structure-dynamics interplay of complex systems. In neuroscience, progress is typically constrained by the low spatio-temporal resolution of experiments and by the lack of a universal inferring framework for empirical systems. To address these issues, applications of network science and artificial intelligence to neural data have been rapidly growing. In this article, we review important recent applications of methods from those fields to the study of the interplay between structure and functional dynamics of human and zebrafish brain. We cover the selection of topological features for the characterization of brain networks, inference of functional connections, dynamical modeling, and close with applications to both the human and zebrafish brain. This review is intended to neuroscientists who want to become acquainted with techniques from network science, as well as to researchers from the latter field who are interested in exploring novel application scenarios in neuroscience.

C. elegans males optimize mate-choice decisions via sex-specific responses to multimodal sensory cues

For sexually reproducing animals, selecting optimal mates is essential for maximizing reproductive fitness. Because the nematode C. elegans reproduces mostly by self-fertilization, little is known about its mate-choice behaviors. While several sensory cues have been implicated in males' ability to recognize hermaphrodites, achieving an integrated understanding of the ways males use these cues to assess relevant characteristics of potential mates has proven challenging. Here, we use a choice-based social-interaction assay to explore the ability of C. elegans males to make and optimize mate choices. We find that males use a combination of volatile sex pheromones (VSPs), ascaroside pheromones, surface-bound chemical cues, and other signals to robustly assess a variety of features of potential mates. Specific aspects of mate choice are communicated by distinct signals: the presence of a sperm-depleted, receptive hermaphrodite is likely signaled by VSPs, while developmental stage and sex are redundantly specified by ascaroside pheromones and surface-associated cues. Ascarosides also signal nutritional information, allowing males to choose well-fed over starved mates, while both ascarosides and surface-associated cues cause males to prefer virgin over previously mated hermaphrodites. The male-specificity of these behavioral responses is determined by both male-specific neurons and the male state of sex-shared circuits, and we reveal an unexpected role for the sex-shared ASH sensory neurons in male attraction to endogenously produced hermaphrodite ascarosides. Together, our findings lead to an integrated view of the signaling and behavioral mechanisms by which males use diverse sensory cues to assess multiple features of potential mates and optimize mate choice.

The Caenorhabditis elegans innexin INX-20 regulates nociceptive behavioral sensitivity

Organisms rely on chemical cues in their environment to indicate the presence or absence of food, reproductive partners, predators, or other harmful stimuli. In the nematode Caenorhabditis elegans, the bilaterally symmetric pair of ASH sensory neurons serves as the primary nociceptors. ASH activation by aversive stimuli leads to backward locomotion and stimulus avoidance. We previously reported a role for guanylyl cyclases in dampening nociceptive sensitivity that requires an innexin-based gap junction network to pass cGMP between neurons. Here, we report that animals lacking function of the gap junction component INX-20 are hypersensitive in their behavioral response to both soluble and volatile chemical stimuli that signal through G protein-coupled receptor pathways in ASH. We find that expressing inx-20 in the ADL and AFD sensory neurons is sufficient to dampen ASH sensitivity, which is supported by new expression analysis of endogenous INX-20 tagged with mCherry via the CRISPR-Cas9 system. Although ADL does not form gap junctions directly with ASH, it does so via gap junctions with the interneuron RMG and the sensory neuron ASK. Ablating either ADL or RMG and ASK also resulted in nociceptive hypersensitivity, suggesting an important role for RMG/ASK downstream of ADL in the ASH modulatory circuit. This work adds to our growing understanding of the repertoire of ways by which ASH activity is regulated via its connectivity to other neurons and identifies a previously unknown role for ADL and RMG in the modulation of aversive behavior.

A single neuron in C. elegans orchestrates multiple motor outputs through parallel modes of transmission

Animals generate a wide range of highly coordinated motor outputs, which allows them to execute purposeful behaviors. Individual neuron classes in the circuits that generate behavior have a remarkable capacity for flexibility, as they exhibit multiple axonal projections, transmitter systems, and modes of neural activity. How these multi-functional properties of neurons enable the generation of highly coordinated behaviors remains unknown. Here we show that the HSN neuron in C. elegans evokes multiple motor programs over different timescales to enable a suite of behavioral changes during egg-laying. Using HSN activity perturbations and in vivo calcium imaging, we show that HSN acutely increases egg-laying and locomotion while also biasing the animals towards low-speed dwelling behavior over longer timescales. The acute effects of HSN on egg-laying and high-speed locomotion are mediated by separate sets of HSN transmitters and different HSN axonal projections. The long-lasting effects on dwelling are mediated by HSN release of serotonin that is taken up and re-released by NSM, another serotonergic neuron class that directly evokes dwelling. Our results show how the multi-functional properties of a single neuron allow it to induce a coordinated suite of behaviors and also reveal for the first time that neurons can borrow serotonin from one another to control behavior.

Translational Neuroscience Approaches to Understanding Autism

While autism spectrum disorder affects nearly 2% of children in the United States, little is known with certainty concerning the etiologies and brain systems involved. This is due, in part, to the substantial heterogeneity in the presentation of the core symptoms of autism as well as the great number of co-occurring conditions that are common in autistic individuals. Understanding the neurobiology of autism is further hampered by the limited availability of postmortem brain tissue to determine the cellular and molecular alterations that take place in the autistic brain. Animal models therefore provide great translational value in helping to define the neural systems that constitute the social brain and mediate repetitive behaviors or interests. If they are based on genetic or environmental factors that contribute to autism, organisms from flies to nonhuman primates may serve as models of the neural structure or function of the autistic brain. Ultimately, successful models can also be employed to test the safety and effectiveness of potential therapeutics. This is an overview of the major animal species that are currently used as models of autism, including an appraisal of the advantages and limitations of each.

Generative network modeling reveals quantitative definitions of bilateral symmetry exhibited by a whole insect brain connectome

Comparing connectomes can help explain how neural connectivity is related to genetics, disease, development, learning, and behavior. However, making statistical inferences about the significance and nature of differences between two networks is an open problem, and such analysis has not been extensively applied to nanoscale connectomes. Here, we investigate this problem via a case study on the bilateral symmetry of a larval Drosophila brain connectome. We translate notions of 'bilateral symmetry' to generative models of the network structure of the left and right hemispheres, allowing us to test and refine our understanding of symmetry. We find significant differences in connection probabilities both across the entire left and right networks and between specific cell types. By rescaling connection probabilities or removing certain edges based on weight, we also present adjusted definitions of bilateral symmetry exhibited by this connectome. This work shows how statistical inferences from networks can inform the study of connectomes, facilitating future comparisons of neural structures.

Molecular heterogeneity of C. elegans glia across sexes

A comprehensive description of nervous system function, and sex dimorphism within, is incomplete without clear assessment of the diversity of its component cell types, neurons and glia. C. elegans has an invariant nervous system with the first mapped connectome of a multicellular organism and single-cell atlas of component neurons. Here we present single nuclear RNA-seq evaluation of glia across the entire adult C. elegans nervous system, including both sexes. Machine learning models enabled us to identify both sex-shared and sex-specific glia and glial subclasses. We have identified and validated molecular markers in silico and in vivo for these molecular subcategories. Comparative analytics also reveals previously unappreciated molecular heterogeneity in anatomically identical glia between and within sexes, indicating consequent functional heterogeneity. Furthermore, our datasets reveal that while adult C. elegans glia express neuropeptide genes, they lack the canonical unc-31/CAPS-dependent dense core vesicle release machinery. Thus, glia employ alternate neuromodulator processing mechanisms. Overall, this molecular atlas, available at www.wormglia.org, reveals rich insights into heterogeneity and sex dimorphism in glia across the entire nervous system of an adult animal.

Disexcitation in the ASH/RIM/ADL negative feedback circuit fine-tunes hyperosmotic sensation and avoidance in Caenorhabditis elegans

Sensations, especially nociception, are tightly controlled and regulated by the central and peripheral nervous systems. Osmotic sensation and related physiological and behavioral reactions are essential for animal well-being and survival. In this study, we find that interaction between secondary nociceptive ADL and primary nociceptive ASH neurons upregulates Caenorhabditis elegans avoidance of the mild and medium hyperosmolality of 0.41 and 0.88 Osm but does not affect avoidance of high osmolality of 1.37 and 2.29 Osm. The interaction between ASH and ADL is actualized through a negative feedback circuit consisting of ASH, ADL, and RIM interneurons. In this circuit, hyperosmolality-sensitive ADL augments the ASH hyperosmotic response and animal hyperosmotic avoidance; RIM inhibits ADL and is excited by ASH; thus, ASH exciting RIM reduces ADL augmenting ASH. The neuronal signal integration modality in the circuit is disexcitation. In addition, ASH promotes hyperosmotic avoidance through ASH/RIC/AIY feedforward circuit. Finally, we find that in addition to ASH and ADL, multiple sensory neurons are involved in hyperosmotic sensation and avoidance behavior.

Function of cell adhesion molecules in differentiation of ray sensory neurons in C. elegans

For proper functioning of the nervous system, it is crucial that neurons find their appropriate partners and build the correct neural connection patterns. Although cell adhesion molecules (CAMs) have been studied for many years as essential players in neural connections, we have yet to unravel the code by which CAMs encode synaptic specificity. We analyzed the effects of mutations in CAM genes on the morphology and synapses of a set of sensory neurons in the Caenorhabditis elegans male tail. B-type ray sensory neurons express 10 genes encoding CAMs. We examined the effect on axon trajectory and localization of pre-synaptic components in viable mutants of nine of these. We found axon trajectory defects in mutants of UNC-40/DCC, SAX-3/ROBO, and FMI-1/Flamingo/Celsr1. None of the mutations caused loss of pre-synaptic components in axons, and in several the level even appeared to increase, suggesting possible accumulation of pre-synapses. B-type sensory neurons fasciculate with a second type of ray sensory neuron, the A-type, in axon commissures. We found a CAM expressed in A-type functions additively with a CAM expressed in B-type in axon guidance, and lack of a CAM expressed in B-type affected A-type axon guidance. Overall, single and multiple mutants of CAM genes had limited effects on ray neuron trajectories and accumulation of synaptic components.

Caenorhabditis elegans as a Model System to Study Human Neurodegenerative Disorders

In recent years, advances in science and technology have improved our quality of life, enabling us to tackle diseases and increase human life expectancy. However, longevity is accompanied by an accretion in the frequency of age-related neurodegenerative diseases, creating a growing burden, with pervasive social impact for human societies. The cost of managing such chronic disorders and the lack of effective treatments highlight the need to decipher their molecular and genetic underpinnings, in order to discover new therapeutic targets. In this effort, the nematode Caenorhabditis elegans serves as a powerful tool to recapitulate several disease-related phenotypes and provides a highly malleable genetic model that allows the implementation of multidisciplinary approaches, in addition to large-scale genetic and pharmacological screens. Its anatomical transparency allows the use of co-expressed fluorescent proteins to track the progress of neurodegeneration. Moreover, the functional conservation of neuronal processes, along with the high homology between nematode and human genomes, render C. elegans extremely suitable for the study of human neurodegenerative disorders. This review describes nematode models used to study neurodegeneration and underscores their contribution in the effort to dissect the molecular basis of human diseases and identify novel gene targets with therapeutic potential.

Statistical power in network neuroscience

Network neuroscience has emerged as a leading method to study brain connectivity. The success of these investigations is dependent not only on approaches to accurately map connectivity but also on the ability to detect real effects in the data - that is, statistical power. We review the state of statistical power in the field and discuss sample size, effect size, measurement error, and network topology as key factors that influence the power of brain connectivity investigations. We use the term 'differential power' to describe how power can vary between nodes, edges, and graph metrics, leaving traces in both positive and negative connectome findings. We conclude with strategies for working with, rather than around, power in connectivity studies.

Comparison of the chronic and multigenerational toxicity of racemic glufosinate and l-glufosinate to Caenorhabditis elegans at environmental concentrations

Glufosinate-ammonium, the second largest transgene crop resistant herbicide, is classified as a mobile persistent pollutant by the U.S. Environmental Protection Agencybecause of its slow decomposition and easy mobile transfer in a water environment. The chronic and multigeneration toxicity of this compound to environmental organisms are alarming. In this study, racemic glufosinate-ammonium and the effective isomer, l-glufosinate-ammonium, were used as the test agents. The developmental, neurotoxic and reproductive toxicities of Caenorhabditis elegans to their parents and progeny were studied by continuous exposure in water at concentrations of 0.1, 1, 10 and 100 μg/L. The causes of toxicity differences were analysed from oxidative stress and transcription levels. Through oxidative stress of C. elegans, racemic glufosinate-ammonium and l-glufosinate-ammonium both mediated the developmental toxicity (shortened developmental cycle, reduced body length and width, promoted ageingand decreased longevity), neurotoxicity (inhibited head swinging, body bending frequency and acetylcholinesterase [AchE] activity) and reproductive toxicity (significant reductions in the number of eggs and offspring in vivo and induced apoptosis of gonadal cells). These phenomena caused oxidative damage (protein and membrane lipid peroxidation) and further induced apoptosis. The changes in various indicators caused by racemic glufosinate-ammonium exposure were more significant than those caused by l-glufosinate-ammonium exposure, and the reproduction-related indicators were more significant than the developmental and neurological indicators. A continuous accumulation of toxicity was observed after multiple generations of continuous exposure. These research results provide a data reference for the ecotoxicological evaluation and risk assessment of glufosinate-ammonium and contribute to the revision and improvement of the related environmental policies of glufosinate-ammonium.

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.

Transcriptome age of individual cell types in Caenorhabditis elegans

The phylotranscriptomic analysis of development in several species revealed the expression of older and more conserved genes in midembryonic stages and younger and more divergent genes in early and late embryonic stages, which supported the hourglass mode of development. However, previous work only studied the transcriptome age of whole embryos or embryonic sublineages, leaving the cellular basis of the hourglass pattern and the variation of transcriptome ages among cell types unexplored. By analyzing both bulk and single-cell transcriptomic data, we studied the transcriptome age of the nematode Caenorhabditis elegans throughout development. Using the bulk RNA-seq data, we identified the morphogenesis phase in midembryonic development as the phylotypic stage with the oldest transcriptome and confirmed the results using whole-embryo transcriptome assembled from single-cell RNA-seq data. The variation in transcriptome ages among individual cell types remained small in early and midembryonic development and grew bigger in late embryonic and larval stages as cells and tissues differentiate. Lineages that give rise to certain tissues (e.g., hypodermis and some neurons) but not all recapitulated the hourglass pattern across development at the single-cell transcriptome level. Further analysis of the variation in transcriptome ages among the 128 neuron types in C. elegans nervous system found that a group of chemosensory neurons and their downstream interneurons expressed very young transcriptomes and may contribute to adaptation in recent evolution. Finally, the variation in transcriptome age among the neuron types, as well as the age of their cell fate regulators, led us to hypothesize the evolutionary history of some neuron types.

A single-cell resolution gene expression atlas of the larval zebrafish brain

The advent of multimodal brain atlases promises to accelerate progress in neuroscience by allowing in silico queries of neuron morphology, connectivity, and gene expression. We used multiplexed fluorescent in situ RNA hybridization chain reaction (HCR) technology to generate expression maps across the larval zebrafish brain for a growing set of marker genes. The data were registered to the Max Planck Zebrafish Brain (mapzebrain) atlas, thus allowing covisualization of gene expression, single-neuron tracings, and expertly curated anatomical segmentations. Using post hoc HCR labeling of the immediate early gene cfos, we mapped responses to prey stimuli and food ingestion across the brain of freely swimming larvae. This unbiased approach revealed, in addition to previously described visual and motor areas, a cluster of neurons in the secondary gustatory nucleus, which express the marker calb2a, as well as a specific neuropeptide Y receptor, and project to the hypothalamus. This discovery exemplifies the power of this new atlas resource for zebrafish neurobiology.

Improved Link Entropy with Dynamic Community Number Detection for Quantifying Significance of Edges in Complex Social Networks

Discovering communities in complex networks is essential in performing analyses, such as dynamics of political fragmentation and echo chambers in social networks. In this work, we study the problem of quantifying the significance of edges in a complex network, and propose a significantly improved version of the Link Entropy method. Using Louvain, Leiden and Walktrap methods, our proposal detects the number of communities in each iteration on discovering the communities. Running experiments on various benchmark networks, we show that our proposed method outperforms the Link Entropy method in quantifying edge significance. Considering also the computational complexities and possible defects, we conclude that Leiden or Louvain algorithms are the best choice for community number detection in quantifying edge significance. We also discuss designing a new algorithm for not only discovering the number of communities, but also computing the community membership uncertainties.

Synaptogenesis: unmasking molecular mechanisms using Caenorhabditis elegans

The nematode Caenorhabditis elegans is a research model organism particularly suited to the mechanistic understanding of synapse genesis in the nervous system. Armed with powerful genetics, knowledge of complete connectomics, and modern genomics, studies using C. elegans have unveiled multiple key regulators in the formation of a functional synapse. Importantly, many signaling networks display remarkable conservation throughout animals, underscoring the contributions of C. elegans research to advance the understanding of our brain. In this chapter, we will review up-to-date information of the contribution of C. elegans to the understanding of chemical synapses, from structure to molecules and to synaptic remodeling.

Characterization of the Doublesex/MAB-3 transcription factor DMD-9 in Caenorhabditis elegans

DMD-9 is a Caenorhabditis elegans Doublesex/MAB-3 Domain transcription factor (TF) of unknown function. Single-cell transcriptomics has revealed that dmd-9 is highly expressed in specific head sensory neurons, with lower levels detected in non-neuronal tissues (uterine cells and sperm). Here, we characterized endogenous dmd-9 expression and function in hermaphrodites and males to identify potential sexually dimorphic roles. In addition, we dissected the trans- and cis-regulatory mechanisms that control DMD-9 expression in neurons. Our results show that of the 22 neuronal cell fate reporters we assessed in DMD-9-expressing neurons, only the neuropeptide-encoding flp-19 gene is cell-autonomously regulated by DMD-9. Further, we did not identify defects in behaviors mediated by DMD-9 expressing neurons in dmd-9 mutants. We found that dmd-9 expression in neurons is regulated by 4 neuronal fate regulatory TFs: ETS-5, EGL-13, CHE-1, and TTX-1. In conclusion, our study characterized the DMD-9 expression pattern and regulatory logic for its control. The lack of detectable phenotypes in dmd-9 mutant animals suggests that other proteins compensate for its loss.

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.

Dissecting the Functional Organization of the C. elegans Serotonergic System at Whole-Brain Scale

Serotonin controls many aspects of animal behavior and cognition. But how serotonin acts on its diverse receptor types in neurons across the brain to modulate global activity and behavior is unknown. Here, we examine how serotonin release from a feeding-responsive neuron in C. elegans alters brain-wide activity to induce foraging behaviors, like slow locomotion and increased feeding. A comprehensive genetic analysis identifies three core serotonin receptors that collectively induce slow locomotion upon serotonin release and three others that interact with them to further modulate this behavior. The core receptors have different functional roles: some induce behavioral responses to sudden increases in serotonin release, whereas others induce responses to persistent release. Whole-brain calcium imaging reveals widespread serotonin-associated brain dynamics, impacting different behavioral networks in different ways. We map out all sites of serotonin receptor expression in the connectome, which, together with synaptic connectivity, helps predict serotonin-associated brain-wide activity changes. These results provide a global view of how serotonin acts at defined sites across a connectome to modulate brain-wide activity and behavior.

The synaptic organization in the Caenorhabditis elegans neural network suggests significant local compartmentalized computations

Neurons are characterized by elaborate tree-like dendritic structures that support local computations by integrating multiple inputs from upstream presynaptic neurons. It is less clear whether simple neurons, consisting of a few or even a single neurite, may perform local computations as well. To address this question, we focused on the compact neural network of Caenorhabditis elegans animals for which the full wiring diagram is available, including the coordinates of individual synapses. We find that the positions of the chemical synapses along the neurites are not randomly distributed nor can they be explained by anatomical constraints. Instead, synapses tend to form clusters, an organization that supports local compartmentalized computations. In mutually synapsing neurons, connections of opposite polarity cluster separately, suggesting that positive and negative feedback dynamics may be implemented in discrete compartmentalized regions along neurites. In triple-neuron circuits, the nonrandom synaptic organization may facilitate local functional roles, such as signal integration and coordinated activation of functionally related downstream neurons. These clustered synaptic topologies emerge as a guiding principle in the network, presumably to facilitate distinct parallel functions along a single neurite, which effectively increase the computational capacity of the neural network.

Restructuring of an asymmetric neural circuit during associative learning

Asymmetric brain function is common across the animal kingdom and involved in language processing, and likely in learning and memory. What regulates asymmetric brain function remains elusive. Here, we show that the nematode Caenorhabditis elegans restructures an asymmetric salt sensing neural circuit during associative learning. Worms memorize and prefer the salt concentration at which they were raised in the presence of food through a left-biased network architecture. When conditioned at elevated salt concentrations, animals change the left-biased to a right-biased network, which explains the changed salt-seeking behavior. The changes in circuit architecture require new synapse formation induced through asymmetric, paracrine insulin-signaling. Therefore, experience-dependent changes in asymmetric network architecture rely on paracrine insulin signaling and are fundamental to learning and behavior.

Learning the dynamics of realistic models of C. elegans nervous system with recurrent neural networks

Given the inherent complexity of the human nervous system, insight into the dynamics of brain activity can be gained from studying smaller and simpler organisms. While some of the potential target organisms are simple enough that their behavioural and structural biology might be well-known and understood, others might still lead to computationally intractable models that require extensive resources to simulate. Since such organisms are frequently only acting as proxies to further our understanding of underlying phenomena or functionality, often one is not interested in the detailed evolution of every single neuron in the system. Instead, it is sufficient to observe the subset of neurons that capture the effect that the profound nonlinearities of the neuronal system have in response to different stimuli. In this paper, we consider the well-known nematode Caenorhabditis elegans and seek to investigate the possibility of generating lower complexity models that capture the system's dynamics with low error using only measured or simulated input-output information. Such models are often termed black-box models. We show how the nervous system of C. elegans can be modelled and simulated with data-driven models using different neural network architectures. Specifically, we target the use of state-of-the-art recurrent neural network architectures such as Long Short-Term Memory and Gated Recurrent Units and compare these architectures in terms of their properties and their accuracy (Root Mean Square Error), as well as the complexity of the resulting models. We show that Gated Recurrent Unit models with a hidden layer size of 4 are able to accurately reproduce the system response to very different stimuli. We furthermore explore the relative importance of their inputs as well as scalability to more scenarios.

Emergent dynamics in a robotic model based on the Caenorhabditis elegans connectome

We analyze the neural dynamics and their relation with the emergent actions of a robotic vehicle that is controlled by a neural network numerical simulation based on the nervous system of the nematode Caenorhabditis elegans. The robot interacts with the environment through a sensor that transmits the information to sensory neurons, while motor neurons outputs are connected to wheels. This is enough to allow emergent robot actions in complex environments, such as avoiding collisions with obstacles. Working with robotic models makes it possible to simultaneously keep track of the dynamics of all the neurons and also register the actions of the robot in the environment in real time, while avoiding the complex technicalities of simulating a real environment. This allowed us to identify several relevant features of the neural dynamics associated with the emergent actions of the robot, some of which have already been observed in biological worms. These results suggest that some basic aspects of behaviors observed in living beings are determined by the underlying structure of the associated neural network.

MorphoFeatures for unsupervised exploration of cell types, tissues, and organs in volume electron microscopy

Electron microscopy (EM) provides a uniquely detailed view of cellular morphology, including organelles and fine subcellular ultrastructure. While the acquisition and (semi-)automatic segmentation of multicellular EM volumes are now becoming routine, large-scale analysis remains severely limited by the lack of generally applicable pipelines for automatic extraction of comprehensive morphological descriptors. Here, we present a novel unsupervised method for learning cellular morphology features directly from 3D EM data: a neural network delivers a representation of cells by shape and ultrastructure. Applied to the full volume of an entire three-segmented worm of the annelid Platynereis dumerilii, it yields a visually consistent grouping of cells supported by specific gene expression profiles. Integration of features across spatial neighbours can retrieve tissues and organs, revealing, for example, a detailed organisation of the animal foregut. We envision that the unbiased nature of the proposed morphological descriptors will enable rapid exploration of very different biological questions in large EM volumes, greatly increasing the impact of these invaluable, but costly resources.