Spikes alone do not behavior make: why neuroscience needs biomechanics

Sound-seeking before and after hearing loss in mice

How we move our bodies affects how we perceive sound. For instance, head movements help us to better localize the source of a sound and to compensate for asymmetric hearing loss. However, many auditory experiments are designed to restrict head and body movements. To study the role of movement in hearing, we developed a behavioral task called sound-seeking that rewarded freely moving mice for tracking down an ongoing sound source. Over the course of learning, mice more efficiently navigated to the sound. Next, we asked how sound-seeking was affected by hearing loss induced by surgical removal of the malleus from the middle ear. After bilateral hearing loss sound-seeking performance drastically declined and did not recover. In striking contrast, after unilateral hearing loss mice were only transiently impaired and then recovered their sound-seek ability over about a week. Throughout recovery, unilateral mice increasingly relied on a movement strategy of sequentially checking potential locations for the sound source. In contrast, the startle reflex (an innate auditory behavior) was preserved after unilateral hearing loss and abolished by bilateral hearing loss without recovery over time. In sum, mice compensate with body movement for permanent unilateral damage to the peripheral auditory system. Looking forward, this paradigm provides an opportunity to examine how movement enhances perception and enables resilient adaptation to sensory disorders.

Optical mapping of ground reaction force dynamics in freely behaving Drosophila melanogaster larvae

During locomotion, soft-bodied terrestrial animals solve complex control problems at substrate interfaces, but our understanding of how they achieve this without rigid components remains incomplete. Here, we develop new all-optical methods based on optical interference in a deformable substrate to measure ground reaction forces (GRFs) with micrometre and nanonewton precision in behaving Drosophila larvae. Combining this with a kinematic analysis of substrate-interfacing features, we shed new light onto the biomechanical control of larval locomotion. Crawling in larvae measuring ~1 mm in length involves an intricate pattern of cuticle sequestration and planting, producing GRFs of 1-7 µN. We show that larvae insert and expand denticulated, feet-like structures into substrates as they move, a process not previously observed in soft-bodied animals. These 'protopodia' form dynamic anchors to compensate counteracting forces. Our work provides a framework for future biomechanics research in soft-bodied animals and promises to inspire improved soft-robot design.

Form and function of anguilliform swimming

Anguilliform swimmers are long and narrow animals that propel themselves by undulating their bodies. Observations in nature and recent investigations suggest that anguilliform swimming is highly efficient. However, understanding the underlying reasons for the efficiency of this type of locomotion requires interdisciplinary studies spanning from biology to hydrodynamics. Regrettably, these different fields are rarely discussed together, which hinders our ability to understand the repeated evolution of this swimming mode in vertebrates. This review compiles the current knowledge of the anatomical features that drive anguilliform swimming, compares the resulting kinematics across a wide range of anguilliform swimmers, and describes the resulting hydrodynamic interactions using data from both in vivo experiments and computational studies.

Sensorimotor adaptation to destabilizing dynamics in weakly electric fish

Humans and other animals can readily learn to compensate for changes in the dynamics of movement. Such changes can result from an injury or changes in the weight of carried objects. These changes in dynamics can lead not only to reduced performance but also to dramatic instabilities. We evaluated the impacts of compensatory changes in control policies in relation to stability and robustness in Eigenmannia virescens, a species of weakly electric fish. We discovered that these fish retune their sensorimotor control system in response to experimentally generated destabilizing dynamics. Specifically, we used an augmented reality system to manipulate sensory feedback during an image stabilization task in which a fish maintained its position within a refuge. The augmented reality system measured the fish's movements in real time. These movements were passed through a high-pass filter and multiplied by a gain factor before being fed back to the refuge motion. We adjusted the gain factor to gradually destabilize the fish's sensorimotor loop. The fish retuned their sensorimotor control system to compensate for the experimentally induced destabilizing dynamics. This retuning was partially maintained when the augmented reality feedback was abruptly removed. The compensatory changes in sensorimotor control improved tracking performance as well as control-theoretic measures of robustness, including reduced sensitivity to disturbances and improved phase margins.

Fantastic beasts and how to study them: rethinking experimental animal behavior

Humans have been trying to understand animal behavior at least since recorded history. Recent rapid development of new technologies has allowed us to make significant progress in understanding the physiological and molecular mechanisms underlying behavior, a key goal of neuroethology. However, there is a tradeoff when studying animal behavior and its underlying biological mechanisms: common behavior protocols in the laboratory are designed to be replicable and controlled, but they often fail to encompass the variability and breadth of natural behavior. This Commentary proposes a framework of 10 key questions that aim to guide researchers in incorporating a rich natural context into their experimental design or in choosing a new animal study system. The 10 questions cover overarching experimental considerations that can provide a template for interspecies comparisons, enable us to develop studies in new model organisms and unlock new experiments in our quest to understand behavior.

Sound-seeking before and after hearing loss in mice

How we move our bodies affects how we perceive sound. For instance, we can explore an environment to seek out the source of a sound and we can use head movements to compensate for hearing loss. How we do this is not well understood because many auditory experiments are designed to limit head and body movements. To study the role of movement in hearing, we developed a behavioral task called sound-seeking that rewarded mice for tracking down an ongoing sound source. Over the course of learning, mice more efficiently navigated to the sound. We then asked how auditory behavior was affected by hearing loss induced by surgical removal of the malleus from the middle ear. An innate behavior, the auditory startle response, was abolished by bilateral hearing loss and unaffected by unilateral hearing loss. Similarly, performance on the sound-seeking task drastically declined after bilateral hearing loss and did not recover. In striking contrast, mice with unilateral hearing loss were only transiently impaired on sound-seeking; over a recovery period of about a week, they regained high levels of performance, increasingly reliant on a different spatial sampling strategy. Thus, even in the face of permanent unilateral damage to the peripheral auditory system, mice recover their ability to perform a naturalistic sound-seeking task. This paradigm provides an opportunity to examine how body movement enables better hearing and resilient adaptation to sensory deprivation.

Adaptability of the load sharing between the longissimus and components of the multifidus muscle during isometric trunk extension in healthy individuals

PURPOSE

Redundancy of the musculoskeletal system implies multiple strategies are theoretically available to coordinate back extensor muscles. This study investigated whether coordination between back muscles during a tightly constrained isometric trunk extension task varies within and between individuals, and whether this changes following brief exposure to activation feedback of a muscle.

METHODS

Nine healthy participants performed three blocks of two repetitions of ramped isometric trunk extension in side-lying against resistance from 0-30% of maximum voluntary contraction over 30 s (force feedback). Between blocks, participants repeated contractions with visual feedback of electromyography (EMG) from either superficial (SM) or deep multifidus (DM), in two conditions; 'After SM' and 'After DM'. Intramuscular EMG was recorded from SM, DM, and longissimus (LG) simultaneously with shear wave elastography (SWE) from SM or DM.

RESULTS

In the 'Natural' condition (force feedback only), group data showed incremental increases in EMG with force, with minor changes in distribution of activation between muscles as force increased. SM was the most active muscle during the 'Natural' condition, but with DM most active in some participants. Individual data showed that coordination between muscles differed substantially between repetitions and individuals. Brief exposure to EMG feedback altered coordination. SWE showed individual variation, but findings differed from EMG.

CONCLUSION

This study revealed substantial variation in coordination between back extensor muscles within and between participants, and after exposure to feedback, in a tightly constrained task. Shear modulus revealed similar variation, but with an inconsistent relationship to EMG. These data highlight highly flexible control of back muscles.

Mechanosensory Feedback in Lamprey Swimming Models and Applications in the Field of Spinal Cord Regeneration

The central pattern generator (CPG) in anguilliform swimming has served as a model for examining the neural basis of locomotion. This system has been particularly valuable for the development of mathematical models. As our biological understanding of the neural basis of locomotion has expanded, so too have these models. Recently, there have been significant advancements in our understanding of the critical role that mechanosensory feedback plays in robust locomotion. This work has led to a push in the field of mathematical modeling to incorporate mechanosensory feedback into CPG models. In this perspective piece, we review advances in the development of these models and discuss how newer complex models can support biological investigation. We highlight lamprey spinal cord regeneration as an area that can both inform these models and benefit from them.

Linking neural circuits to the mechanics of animal behavior in Drosophila larval locomotion

The motions that make up animal behavior arise from the interplay between neural circuits and the mechanical parts of the body. Therefore, in order to comprehend the operational mechanisms governing behavior, it is essential to examine not only the underlying neural network but also the mechanical characteristics of the animal's body. The locomotor system of fly larvae serves as an ideal model for pursuing this integrative approach. By virtue of diverse investigation methods encompassing connectomics analysis and quantification of locomotion kinematics, research on larval locomotion has shed light on the underlying mechanisms of animal behavior. These studies have elucidated the roles of interneurons in coordinating muscle activities within and between segments, as well as the neural circuits responsible for exploration. This review aims to provide an overview of recent research on the neuromechanics of animal locomotion in fly larvae. We also briefly review interspecific diversity in fly larval locomotion and explore the latest advancements in soft robots inspired by larval locomotion. The integrative analysis of animal behavior using fly larvae could establish a practical framework for scrutinizing the behavior of other animal species.

Structural damping renders the hawkmoth exoskeleton mechanically insensitive to non-sinusoidal deformations

Muscles act through elastic and dissipative elements to mediate movement, which can introduce dissipation and filtering which are important for energetics and control. The high power requirements of flapping flight can be reduced by an insect's exoskeleton, which acts as a spring with frequency-independent material properties under purely sinusoidal deformation. However, this purely sinusoidal dynamic regime does not encompass the asymmetric wing strokes of many insects or non-periodic deformations induced by external perturbations. As such, it remains unknown whether a frequency-independent model applies broadly and what implications it has for control. We used a vibration testing system to measure the mechanical properties of isolated Manduca sexta thoraces under symmetric, asymmetric and band-limited white noise deformations. The asymmetric and white noise conditions represent two types of generalized, multi-frequency deformations that may be encountered during steady-state and perturbed flight. Power savings and dissipation were indistinguishable between symmetric and asymmetric conditions, demonstrating that no additional energy is required to deform the thorax non-sinusoidally. Under white noise conditions, stiffness and damping were invariant with frequency, suggesting that the thorax has no frequency-dependent filtering properties. A simple flat frequency response function fits our measured frequency response. This work demonstrates the potential of materials with frequency-independent damping to simplify motor control by eliminating any velocity-dependent filtering that viscoelastic elements usually impose between muscle and wing.

Flexible locomotion in complex environments: the influence of species, speed and sensory feedback on panarthropod inter-leg coordination

Panarthropods (a clade containing arthropods, tardigrades and onychophorans) can adeptly move across a wide range of challenging terrains and their ability to do so given their relatively simple nervous systems makes them compelling study organisms. Studies of forward walking on flat terrain excitingly point to key features in inter-leg coordination patterns that seem to be 'universally' shared across panarthropods. However, when movement through more complex, naturalistic terrain is considered, variability in coordination patterns - from the intra-individual to inter-species level - becomes more apparent. This variability is likely to be due to the interplay between sensory feedback and local pattern-generating activity, and depends crucially on species, walking speed and behavioral goal. Here, I gather data from the literature of panarthropod walking coordination on both flat ground and across more complex terrain. This Review aims to emphasize the value of: (1) designing experiments with an eye towards studying organisms in natural environments; (2) thoughtfully integrating results from various experimental techniques, such as neurophysiological and biomechanical studies; and (3) ensuring that data is collected and made available from a wider range of species for future comparative analyses.

Neuromuscular embodiment of feedback control elements in Drosophila flight

While insects such as Drosophila are flying, aerodynamic instabilities require that they make millisecond time scale adjustments to their wing motion to stay aloft and on course. These stabilization reflexes can be modeled as a proportional-integral (PI) controller; however, it is unclear how such control might be instantiated in insects at the level of muscles and neurons. Here, we show that the b1 and b2 motor units-prominent components of the fly's steering muscle system-modulate specific elements of the PI controller: the angular displacement (integral) and angular velocity (proportional), respectively. Moreover, these effects are observed only during the stabilization of pitch. Our results provide evidence for an organizational principle in which each muscle contributes to a specific functional role in flight control, a finding that highlights the power of using top-down behavioral modeling to guide bottom-up cellular manipulation studies.

Biomechanical and Sensory Feedback Regularize the Behavior of Different Locomotor Central Pattern Generators

This work presents an in-depth numerical investigation into a hypothesized two-layer central pattern generator (CPG) that controls mammalian walking and how different parameter choices might affect the stepping of a simulated neuromechanical model. Particular attention is paid to the functional role of features that have not received a great deal of attention in previous work: the weak cross-excitatory connectivity within the rhythm generator and the synapse strength between the two layers. Sensitivity evaluations of deafferented CPG models and the combined neuromechanical model are performed. Locomotion frequency is increased in two different ways for both models to investigate whether the model's stability can be predicted by trends in the CPG's phase response curves (PRCs). Our results show that the weak cross-excitatory connection can make the CPG more sensitive to perturbations and that increasing the synaptic strength between the two layers results in a trade-off between forced phase locking and the amount of phase delay that can exist between the two layers. Additionally, although the models exhibit these differences in behavior when disconnected from the biomechanical model, these differences seem to disappear with the full neuromechanical model and result in similar behavior despite a variety of parameter combinations. This indicates that the neural variables do not have to be fixed precisely for stable walking; the biomechanical entrainment and sensory feedback may cancel out the strengths of excitatory connectivity in the neural circuit and play a critical role in shaping locomotor behavior. Our results support the importance of including biomechanical models in the development of computational neuroscience models that control mammalian locomotion.

The trunk-tail junctional region in Ciona larvae autonomously expresses tail-beating bursts at ∼20 second intervals

Swimming locomotion in aquatic vertebrates, such as fish and tadpoles, is expressed through neuron networks in the spinal cord. These networks are arranged in parallel, ubiquitously distributed and mutually coupled along the spinal cord to express undulation patterns accommodated to various inputs into the networks. While these systems have been widely studied in vertebrate swimmers, their evolutionary origin along the chordate phylogeny remains unclear. Ascidians, representing a sister group of vertebrates, give rise to tadpole larvae that swim freely in seawater. In the present study, we examined the locomotor ability of the anterior and posterior body fragments of larvae of the ascidian Ciona that had been cut at an arbitrary position. Examination of more than 200 fragments revealed a necessary and sufficient body region that spanned only ∼10% of the body length and included the trunk-tail junction. 'Mid-piece' body fragments, which included the trunk-tail junctional region, but excluded most of the anterior trunk and posterior tail, autonomously expressed periodic tail-beating bursts at ∼20 s intervals. We compared the durations and intervals of tail-beating bursts expressed by mid-piece fragments, and also by whole larvae under different sensory conditions. The results suggest that body parts outside the mid-piece effect shortening of swimming intervals, particularly in the dark, and vary the burst duration. We propose that Ciona larvae express swimming behaviors by modifying autonomous and periodic locomotor drives that operate locally in the trunk-tail junctional region.

A neuromechanical model for Drosophila larval crawling based on physical measurements

BACKGROUND

Animal locomotion requires dynamic interactions between neural circuits, the body (typically muscles), and surrounding environments. While the neural circuitry of movement has been intensively studied, how these outputs are integrated with body mechanics (neuromechanics) is less clear, in part due to the lack of understanding of the biomechanical properties of animal bodies. Here, we propose an integrated neuromechanical model of movement based on physical measurements by taking Drosophila larvae as a model of soft-bodied animals.

RESULTS

We first characterized the kinematics of forward crawling in Drosophila larvae at a segmental and whole-body level. We then characterized the biomechanical parameters of fly larvae, namely the contraction forces generated by neural activity, and passive elastic and viscosity of the larval body using a stress-relaxation test. We established a mathematical neuromechanical model based on the physical measurements described above, obtaining seven kinematic values characterizing crawling locomotion. By optimizing the parameters in the neural circuit, our neuromechanical model succeeded in quantitatively reproducing the kinematics of larval locomotion that were obtained experimentally. This model could reproduce the observation of optogenetic studies reported previously. The model predicted that peristaltic locomotion could be exhibited in a low-friction condition. Analysis of floating larvae provided results consistent with this prediction. Furthermore, the model predicted a significant contribution of intersegmental connections in the central nervous system, which contrasts with a previous study. This hypothesis allowed us to make a testable prediction for the variability in intersegmental connection in sister species of the genus Drosophila.

CONCLUSIONS

We generated a neurochemical model based on physical measurement to provide a new foundation to study locomotion in soft-bodied animals and soft robot engineering.

Reinforcement Learning for Central Pattern Generation in Dynamical Recurrent Neural Networks

Lifetime learning, or the change (or acquisition) of behaviors during a lifetime, based on experience, is a hallmark of living organisms. Multiple mechanisms may be involved, but biological neural circuits have repeatedly demonstrated a vital role in the learning process. These neural circuits are recurrent, dynamic, and non-linear and models of neural circuits employed in neuroscience and neuroethology tend to involve, accordingly, continuous-time, non-linear, and recurrently interconnected components. Currently, the main approach for finding configurations of dynamical recurrent neural networks that demonstrate behaviors of interest is using stochastic search techniques, such as evolutionary algorithms. In an evolutionary algorithm, these dynamic recurrent neural networks are evolved to perform the behavior over multiple generations, through selection, inheritance, and mutation, across a population of solutions. Although, these systems can be evolved to exhibit lifetime learning behavior, there are no explicit rules built into these dynamic recurrent neural networks that facilitate learning during their lifetime (e.g., reward signals). In this work, we examine a biologically plausible lifetime learning mechanism for dynamical recurrent neural networks. We focus on a recently proposed reinforcement learning mechanism inspired by neuromodulatory reward signals and ongoing fluctuations in synaptic strengths. Specifically, we extend one of the best-studied and most-commonly used dynamic recurrent neural networks to incorporate the reinforcement learning mechanism. First, we demonstrate that this extended dynamical system (model and learning mechanism) can autonomously learn to perform a central pattern generation task. Second, we compare the robustness and efficiency of the reinforcement learning rules in relation to two baseline models, a random walk and a hill-climbing walk through parameter space. Third, we systematically study the effect of the different meta-parameters of the learning mechanism on the behavioral learning performance. Finally, we report on preliminary results exploring the generality and scalability of this learning mechanism for dynamical neural networks as well as directions for future work.

From decision to action: Detailed modelling of frog tadpoles reveals neuronal mechanisms of decision-making and reproduces unpredictable swimming movements in response to sensory signals

How does the brain process sensory stimuli, and decide whether to initiate locomotor behaviour? To investigate this question we develop two whole body computer models of a tadpole. The "Central Nervous System" (CNS) model uses evidence from whole-cell recording to define 2300 neurons in 12 classes to study how sensory signals from the skin initiate and stop swimming. In response to skin stimulation, it generates realistic sensory pathway spiking and shows how hindbrain sensory memory populations on each side can compete to initiate reticulospinal neuron firing and start swimming. The 3-D "Virtual Tadpole" (VT) biomechanical model with realistic muscle innervation, body flexion, body-water interaction, and movement is then used to evaluate if motor nerve outputs from the CNS model can produce swimming-like movements in a volume of "water". We find that the whole tadpole VT model generates reliable and realistic swimming. Combining these two models opens new perspectives for experiments.

Clarifying the Biomechanical Concept of Coordination Through Comparison With Coordination in Motor Control

Coordination is a multidisciplinary concept in human movement science, particularly in the field of biomechanics and motor control. However, the term is not used synonymously by researchers and has substantially different meanings depending on the studies. Therefore, it is necessary to clarify the meaning of coordination to avoid confusion. The meaning of coordination in motor control from computational and ecological perspectives has been clarified, and the meanings differed between them. However, in biomechanics, each study has defined the meaning of the term and the meanings are diverse, and no study has attempted to bring together the diversity of the meanings of the term. Therefore, the purpose of this study is to provide a summary of the different meanings of coordination across the theoretical landscape and clarify the meaning of coordination in biomechanics. We showed that in biomechanics, coordination generally means the relation between elements that act toward the achievement of a motor task, which we call biomechanical coordination. We also showed that the term coordination used in computational and ecological perspectives has two different meanings, respectively. Each one had some similarities with biomechanical coordination. The findings of this study lead to an accurate understanding of the concept of coordination, which would help researchers formulate their empirical arguments for coordination in a more transparent manner. It would allow for accurate interpretation of data and theory development. By comprehensively providing multiple perspectives on coordination, this study intends to promote coordination studies in biomechanics.

A connectome is not enough - what is still needed to understand the brain of Drosophila?

Understanding the structure and operation of any nervous system has been a subject of research for well over a century. A near-term opportunity in this quest is to understand the brain of a model species, the fruit fly Drosophila melanogaster. This is an enticing target given its relatively small size (roughly 200,000 neurons), coupled with the behavioral richness that this brain supports, and the wide variety of techniques now available to study both brain and behavior. It is clear that within a few years we will possess a connectome for D. melanogaster: an electron-microscopy-level description of all neurons and their chemical synaptic connections. Given what we will soon have, what we already know and the research that is currently underway, what more do we need to know to enable us to understand the fly's brain? Here, we itemize the data we will need to obtain, collate and organize in order to build an integrated model of the brain of D. melanogaster.

Unsupervised quantification of naturalistic animal behaviors for gaining insight into the brain

Neural computation has evolved to optimize the behaviors that enable our survival. Although much previous work in neuroscience has focused on constrained task behaviors, recent advances in computer vision are fueling a trend toward the study of naturalistic behaviors. Automated tracking of fine-scale behaviors is generating rich datasets for animal models including rodents, fruit flies, zebrafish, and worms. However, extracting meaning from these large and complex data often requires sophisticated computational techniques. Here we review the latest methods and modeling approaches providing new insights into the brain from behavior. We focus on unsupervised methods for identifying stereotyped behaviors and for resolving details of the structure and dynamics of behavioral sequences.

Emergence of robust self-organized undulatory swimming based on local hydrodynamic force sensing

Undulatory swimming represents an ideal behavior to investigate locomotion control and the role of the underlying central and peripheral components in the spinal cord. Many vertebrate swimmers have central pattern generators and local pressure-sensitive receptors that provide information about the surrounding fluid. However, it remains difficult to study experimentally how these sensors influence motor commands in these animals. Here, using a specifically designed robot that captures the essential components of the animal neuromechanical system and using simulations, we tested the hypothesis that sensed hydrodynamic pressure forces can entrain body actuation through local feedback loops. We found evidence that this peripheral mechanism leads to self-organized undulatory swimming by providing intersegmental coordination and body oscillations. Swimming can be redundantly induced by central mechanisms, and we show that, therefore, a combination of both central and peripheral mechanisms offers a higher robustness against neural disruptions than any of them alone, which potentially explains how some vertebrates retain locomotor capabilities after spinal cord lesions. These results broaden our understanding of animal locomotion and expand our knowledge for the design of robust and modular robots that physically interact with the environment.

Neurally driven synthesis of learned, complex vocalizations

Brain machine interfaces (BMIs) hold promise to restore impaired motor function and serve as powerful tools to study learned motor skill. While limb-based motor prosthetic systems have leveraged nonhuman primates as an important animal model,^1-4 speech prostheses lack a similar animal model and are more limited in terms of neural interface technology, brain coverage, and behavioral study design.^5-7 Songbirds are an attractive model for learned complex vocal behavior. Birdsong shares a number of unique similarities with human speech,^8-10 and its study has yielded general insight into multiple mechanisms and circuits behind learning, execution, and maintenance of vocal motor skill.^11-18 In addition, the biomechanics of song production bear similarity to those of humans and some nonhuman primates.^19-23 Here, we demonstrate a vocal synthesizer for birdsong, realized by mapping neural population activity recorded from electrode arrays implanted in the premotor nucleus HVC onto low-dimensional compressed representations of song, using simple computational methods that are implementable in real time. Using a generative biomechanical model of the vocal organ (syrinx) as the low-dimensional target for these mappings allows for the synthesis of vocalizations that match the bird's own song. These results provide proof of concept that high-dimensional, complex natural behaviors can be directly synthesized from ongoing neural activity. This may inspire similar approaches to prosthetics in other species by exploiting knowledge of the peripheral systems and the temporal structure of their output.

Elastic-instability-enabled locomotion

Locomotion of an organism interacting with an environment is the consequence of a symmetry-breaking action in space-time. Here we show a minimal instantiation of this principle using a thin circular sheet, actuated symmetrically by a pneumatic source, using pressure to change shape nonlinearly via a spontaneous buckling instability. This leads to a polarized, bilaterally symmetric cone that can walk on land and swim in water. In either mode of locomotion, the emergence of shape asymmetry in the sheet leads to an asymmetric interaction with the environment that generates movement--via anisotropic friction on land, and via directed inertial forces in water. Scaling laws for the speed of the sheet of the actuator as a function of its size, shape, and the frequency of actuation are consistent with our observations. The presence of easily controllable reversible modes of buckling deformation further allows for a change in the direction of locomotion in open arenas and the ability to squeeze through confined environments--both of which we demonstrate using simple experiments. Our simple approach of harnessing elastic instabilities in soft structures to drive locomotion enables the design of novel shape-changing robots and other bioinspired machines at multiple scales.

Synthetic Birdsongs as a Tool to Induce, and Iisten to, Replay Activity in Sleeping Birds

Birdsong is a complex vocal behavior, which emerges out of the interaction between a nervous system and a highly nonlinear vocal device, the syrinx. In this work we discuss how low dimensional dynamical systems, interpretable in terms of the biomechanics involved, are capable of synthesizing realistic songs. We review the experimental and conceptual steps that lead to the formulation of low dimensional dynamical systems for the song system and describe the tests that quantify their success. In particular, we show how to evaluate computational models by comparing the responses of highly selective neurons to the bird's own song and to synthetic copies generated mathematically. Beyond testing the hypothesis behind the model's construction, these low dimensional models allow designing precise stimuli in order to explore the sensorimotor integration of acoustic signals.

Integrative Neuroscience of Paramecium, a "Swimming Neuron"

Paramecium is a unicellular organism that swims in fresh water by beating thousands of cilia. When it is stimulated (mechanically, chemically, optically, thermally…), it often swims backward then turns and swims forward again. This "avoiding reaction" is triggered by a calcium-based action potential. For this reason, some authors have called Paramecium a "swimming neuron." This review summarizes current knowledge about the physiological basis of behavior of Paramecium.

Comparing the turn performance of different motor control schemes in multilink fish-inspired robots

Fish robots have many possible applications in exploration, industry, research, and continue to increase in design complexity, control, and the behaviors they can complete. Maneuverability is an important metric of fish robot performance, with several strategies being implemented. By far the most common control scheme for fish robot maneuvers is an offset control scheme, wherein the robot's steady swimming is controlled by sinusoidal function and turns are generated biasing bending to one side or another. An early bio-inspired turn control scheme is based on the C-start escape response observed in live fish. We developed a control scheme that is based on the kinematics of routine maneuvers in live fish that we call the 'pulse', which is a pattern of increasing and decreasing curvature that propagates down the body. This pattern of curvature is consistent across a wide range of turn types and can be described with a limited number of variables. We compared the performance of turns using each of these three control schemes across a range of durations and bending amplitudes. We found that C-start and offset turns had the highest heading changes for a given set of inputs, whereas the bio-inspired pulse turns had the highest linear accelerations for a given set of inputs. However, pulses shift the conceptualization of swimming away from it being a continuous behavior towards it being an intermittent behavior that is built by combining individual bending events. Our bio-inspired pulse control scheme has the potential to increase the behavioral flexibility of bio-inspired robotic fish and solve some of the problems associated with integrating different swimming behaviors, despite lower maximal turning performance.

Revealing the unique features of each individual's muscle activation signatures

There is growing evidence that each individual has unique movement patterns, or signatures. The exact origin of these movement signatures, however, remains unknown. We developed an approach that can identify individual muscle activation signatures during two locomotor tasks (walking and pedalling). A linear support vector machine was used to classify 78 participants based on their electromyographic (EMG) patterns measured on eight lower limb muscles. To provide insight into decision-making by the machine learning classification model, a layer-wise relevance propagation (LRP) approach was implemented. This enabled the model predictions to be decomposed into relevance scores for each individual input value. In other words, it provided information regarding which features of the time-varying EMG profiles were unique to each individual. Through extensive testing, we have shown that the LRP results, and by extent the activation signatures, are highly consistent between conditions and across days. In addition, they are minimally influenced by the dataset used to train the model. Additionally, we proposed a method for visualizing each individual's muscle activation signature, which has several potential clinical and scientific applications. This is the first study to provide conclusive evidence of the existence of individual muscle activation signatures.

Individual differences in the neural strategies to control the lateral and medial head of the quadriceps during a mechanically constrained task

We observed that the distribution of the strength of neural drive between the vastus lateralis and vastus medialis during a single-joint isometric task varied across participants. Also, we observed that the proportion of neural drive that was shared within and between these muscles also varied across participants. These results provide evidence that the neural strategies to control the vastus lateralis and vastus medialis muscles widely vary across individuals, even during a mechanically constrained task.

Long Limbless Locomotors Over Land: The Mechanics and Biology of Elongate, Limbless Vertebrate Locomotion

Elongate, limbless body plans are widespread in nature and frequently converged upon (with over two dozen independent convergences in Squamates alone, and many outside of Squamata). Despite their lack of legs, these animals move effectively through a wide range of microhabitats, and have a particular advantage in cluttered or confined environments. This has elicited interest from multiple disciplines in many aspects of their movements, from how and when limbless morphologies evolve to the biomechanics and control of limbless locomotion within and across taxa to its replication in elongate robots. Increasingly powerful tools and technology enable more detailed examinations of limbless locomotor biomechanics, and improved phylogenies have shed increasing light on the origins and evolution of limblessness, as well as the high frequency of convergence. Advances in actuators and control are increasing the capability of "snakebots" to solve real-world problems (e.g., search and rescue), while biological data have proven to be a potent inspiration for improvements in snakebot control. This collection of research brings together prominent researchers on the topic from around the world, including biologists, physicists, and roboticists to offer new perspective on locomotor modes, musculoskeletal mechanisms, locomotor control, and the evolution and diversity of limbless locomotion.

Increasing Muscle Speed Drives Changes in the Neuromuscular Transform of Motor Commands during Postnatal Development in Songbirds

Progressive changes in vocal behavior over the course of vocal imitation leaning are often attributed exclusively to developing neural circuits, but the effects of postnatal body changes remain unknown. In songbirds, the syrinx transforms song system motor commands into sound and exhibits changes during song learning. Here we test the hypothesis that the transformation from motor commands to force trajectories by syringeal muscles functionally changes over vocal development in zebra finches. Our data collected in both sexes show that, only in males, muscle speed significantly increases and that supralinear summation occurs and increases with muscle contraction speed. Furthermore, we show that previously reported submillisecond spike timing in the avian cortex can be resolved by superfast syringeal muscles and that the sensitivity to spike timing increases with speed. Because motor neuron and muscle properties are tightly linked, we make predictions on the boundaries of the yet unknown motor code that correspond well with cortical activity. Together, we show that syringeal muscles undergo essential transformations during song learning that drastically change how neural commands are translated into force profiles and thereby acoustic features. We propose that the song system motor code must compensate for these changes to achieve its acoustic targets. Our data thus support the hypothesis that the neuromuscular transformation changes over vocal development and emphasizes the need for an embodied view of song motor learning.SIGNIFICANCE STATEMENT Fine motor skill learning typically occurs in a postnatal period when the brain is learning to control a body that is changing dramatically due to growth and development. How the developing body influences motor code formation and vice versa remains largely unknown. Here we show that vocal muscles in songbirds undergo critical transformations during song learning that drastically change how neural commands are translated into force profiles and thereby acoustic features. We propose that the motor code must compensate for these changes to achieve its acoustic targets. Our data thus support the hypothesis that the neuromuscular transformation changes over vocal development and emphasizes the need for an embodied view of song motor learning.

Knee Compliance Reduces Peak Swing Phase Collision Forces in a Lower-Limb Exoskeleton Leg: A Test Bench Evaluation

Powered lower limb exoskeletons are a viable solution for people with a spinal cord injury to regain mobility for their daily activities. However, the commonly employed rigid actuation and pre-programmed trajectories increase the risk of falling in case of collisions with external objects. Compliant actuation may reduce forces during collisions, thus protecting hardware and user. However, experimental data of collisions specific to lower limb exoskeletons are not available. In this work, we investigated how a variable stiffness actuator at the knee joint influences collision forces transmitted to the user via the exoskeleton. In a test bench experiment, we compared three configurations of an exoskeleton leg with a variable stiffness knee actuator in (i) compliant or (ii) stiff configurations, and with (iii) a rigid actuator. The peak torque observed at the pelvis was reduced from 260.2 Nm to 116.2 Nm as stiffness decreased. In addition, the mechanical impulse was reduced by a factor of three. These results indicate that compliance in the knee joint of an exoskeleton can be favorable in case of collision and should be considered when designing powered lower limb exoskeletons. Overall, this could decrease the effort necessary to maintain balance after a collision, and improved collision handling in exoskeletons could result in safer use and benefit their usefulness in daily life.

Rapid Adaptation to Changing Mechanical Load by Ordered Recruitment of Identified Motor Neurons

As they interact with their environment and encounter challenges, animals adjust their behavior on a moment-to-moment basis to maintain task fitness. This dynamic process of adaptive motor control occurs in the nervous system, but an understanding of the biomechanics of the body is essential to properly interpret the behavioral outcomes. To study how animals respond to changing task conditions, we used a model system in which the functional roles of identified neurons and the relevant biomechanics are well understood and can be studied in intact behaving animals: feeding in the marine mollusc Aplysia We monitored the motor neuronal output of the feeding circuitry as intact animals fed on uniform food stimuli under unloaded and loaded conditions, and we measured the force of retraction during loaded swallows. We observed a previously undescribed pattern of force generation, which can be explained within the appropriate biomechanical context by the activity of just a few key, identified motor neurons. We show that, when encountering load, animals recruit identified retractor muscle motor neurons for longer and at higher frequency to increase retraction force duration. Our results identify a mode by which animals robustly adjust behavior to their environment, which is experimentally tractable to further mechanistic investigation.

Dynamical model for the neural activity of singing Serinus canaria

Vocal production in songbirds is a key topic regarding the motor control of a complex, learned behavior. Birdsong is the result of the interaction between the activity of an intricate set of neural nuclei specifically dedicated to song production and learning (known as the "song system"), the respiratory system and the vocal organ. These systems interact and give rise to precise biomechanical motor gestures which result in song production. Telencephalic neural nuclei play a key role in the production of motor commands that drive the periphery, and while several attempts have been made to understand their coding strategy, difficulties arise when trying to understand neural activity in the frame of the song system as a whole. In this work, we report neural additive models embedded in an architecture compatible with the song system to provide a tool to reduce the dimensionality of the problem by considering the global activity of the units in each neural nucleus. This model is capable of generating outputs compatible with measurements of air sac pressure during song production in canaries (Serinus canaria). In this work, we show that the activity in a telencephalic nucleus required by the model to reproduce the observed respiratory gestures is compatible with electrophysiological recordings of single neuron activity in freely behaving animals.

The Life of Behavior

Neuroscience needs behavior. However, it is daunting to render the behavior of organisms intelligible without suppressing most, if not all, references to life. When animals are treated as passive stimulus-response, disembodied and identical machines, the life of behavior perishes. Here, we distill three biological principles (materiality, agency, and historicity), spell out their consequences for the study of animal behavior, and illustrate them with various examples from the literature. We propose to put behavior back into context, with the brain in a species-typical body and with the animal's body situated in the world; stamp Newtonian time with nested ontogenetic and phylogenetic processes that give rise to individuals with their own histories; and supplement linear cause-and-effect chains and information processing with circular loops of purpose and meaning. We believe that conceiving behavior in these ways is imperative for neuroscience.

High-fidelity continuum modeling predicts avian voiced sound production

Voiced sound production is the primary form of acoustic communication in terrestrial vertebrates, particularly birds and mammals, including humans. Developing a causal physics-based model that ultimately links descending vocal motor control to tissue vibration and sound requires embodied approaches that include realistic representations of voice physiology. Here, we first implement and then experimentally test a high-fidelity three-dimensional (3D) continuum model for voiced sound production in birds. Driven by individual-based physiologically quantifiable inputs, combined with noninvasive inverse methods for tissue material parameterization, our model accurately predicts observed key vibratory and acoustic performance traits. These results demonstrate that realistic models lead to accurate predictions and support the continuum model approach as a critical tool toward a causal model of voiced sound production.

Variability in locomotor dynamics reveals the critical role of feedback in task control

Animals vary considerably in size, shape, and physiological features across individuals, but yet achieve remarkably similar behavioral performances. We examined how animals compensate for morphophysiological variation by measuring the system dynamics of individual knifefish (Eigenmannia virescens) in a refuge tracking task. Kinematic measurements of Eigenmannia were used to generate individualized estimates of each fish’s locomotor plant and controller, revealing substantial variability between fish. To test the impact of this variability on behavioral performance, these models were used to perform simulated ‘brain transplants’—computationally swapping controllers and plants between individuals. We found that simulated closed-loop performance was robust to mismatch between plant and controller. This suggests that animals rely on feedback rather than precisely tuned neural controllers to compensate for morphophysiological variability.

People come in different shapes and sizes, but most will perform similarly well if asked to complete a task requiring fine manual dexterity – such as holding a pen or picking up a single grape. How can different individuals, with different sized hands and muscles, produce such similar movements? One explanation is that an individual’s brain and nervous system become precisely tuned to mechanics of the body’s muscles and skeleton. An alternative explanation is that brain and nervous system use a more “robust” control policy that can compensate for differences in the body by relying on feedback from the senses to guide the movements.

To distinguish between these two explanations, Uyanik et al. turned to weakly electric freshwater fish known as glass knifefish. These fish seek refuge within root systems, reed grass and among other objects in the water. They swim backwards and forwards to stay hidden despite constantly changing currents. Each fish shuttles back and forth by moving a long ribbon-like fin on the underside of its body. Uyanik et al. measured the movements of the ribbon fin under controlled conditions in the laboratory, and then used the data to create computer models of the brain and body of each fish. The models of each fish’s brain and body were quite different.

To study how the brain interacts with the body, Uyanik et al. then conducted experiments reminiscent of those described in the story of Frankenstein and transplanted the brain from each computer model into the body of different model fish. These “brain swaps” had almost no effect on the model’s simulated swimming behavior. Instead, these “Frankenfish” used sensory feedback to compensate for any mismatch between their brain and body.

This suggests that, for some behaviors, an animal’s brain does not need to be precisely tuned to the specific characteristics of its body. Instead, robust control of movement relies on many seemingly redundant systems that provide sensory feedback. This has implications for the field of robotics. It further suggests that when designing robots, engineers should prioritize enabling the robots to use sensory feedback to cope with unexpected events, a well-known idea in control engineering.

From single neurons to behavior in the jellyfish Aurelia aurita

Jellyfish nerve nets provide insight into the origins of nervous systems, as both their taxonomic position and their evolutionary age imply that jellyfish resemble some of the earliest neuron-bearing, actively-swimming animals. Here, we develop the first neuronal network model for the nerve nets of jellyfish. Specifically, we focus on the moon jelly Aurelia aurita and the control of its energy-efficient swimming motion. The proposed single neuron model disentangles the contributions of different currents to a spike. The network model identifies factors ensuring non-pathological activity and suggests an optimization for the transmission of signals. After modeling the jellyfish's muscle system and its bell in a hydrodynamic environment, we explore the swimming elicited by neural activity. We find that different delays between nerve net activations lead to well-controlled, differently directed movements. Our model bridges the scales from single neurons to behavior, allowing for a comprehensive understanding of jellyfish neural control of locomotion.

Degeneracy in hippocampal physiology and plasticity

Degeneracy, defined as the ability of structurally disparate elements to perform analogous function, has largely been assessed from the perspective of maintaining robustness of physiology or plasticity. How does the framework of degeneracy assimilate into an encoding system where the ability to change is an essential ingredient for storing new incoming information? Could degeneracy maintain the balance between the apparently contradictory goals of the need to change for encoding and the need to resist change towards maintaining homeostasis? In this review, we explore these fundamental questions with the mammalian hippocampus as an example encoding system. We systematically catalog lines of evidence, spanning multiple scales of analysis that point to the expression of degeneracy in hippocampal physiology and plasticity. We assess the potential of degeneracy as a framework to achieve the conjoint goals of encoding and homeostasis without cross-interferences. We postulate that biological complexity, involving interactions among the numerous parameters spanning different scales of analysis, could establish disparate routes towards accomplishing these conjoint goals. These disparate routes then provide several degrees of freedom to the encoding-homeostasis system in accomplishing its tasks in an input- and state-dependent manner. Finally, the expression of degeneracy spanning multiple scales offers an ideal reconciliation to several outstanding controversies, through the recognition that the seemingly contradictory disparate observations are merely alternate routes that the system might recruit towards accomplishment of its goals.

Scalable co-optimization of morphology and control in embodied machines

Evolution sculpts both the body plans and nervous systems of agents together over time. By contrast, in artificial intelligence and robotics, a robot's body plan is usually designed by hand, and control policies are then optimized for that fixed design. The task of simultaneously co-optimizing the morphology and controller of an embodied robot has remained a challenge. In psychology, the theory of embodied cognition posits that behaviour arises from a close coupling between body plan and sensorimotor control, which suggests why co-optimizing these two subsystems is so difficult: most evolutionary changes to morphology tend to adversely impact sensorimotor control, leading to an overall decrease in behavioural performance. Here, we further examine this hypothesis and demonstrate a technique for 'morphological innovation protection', which temporarily reduces selection pressure on recently morphologically changed individuals, thus enabling evolution some time to 'readapt' to the new morphology with subsequent control policy mutations. We show the potential for this method to avoid local optima and converge to similar highly fit morphologies across widely varying initial conditions, while sustaining fitness improvements further into optimization. While this technique is admittedly only the first of many steps that must be taken to achieve scalable optimization of embodied machines, we hope that theoretical insight into the cause of evolutionary stagnation in current methods will help to enable the automation of robot design and behavioural training-while simultaneously providing a test bed to investigate the theory of embodied cognition.

Modelling the mechanics of exploration in larval Drosophila

The Drosophila larva executes a stereotypical exploratory routine that appears to consist of stochastic alternation between straight peristaltic crawling and reorientation events through lateral bending. We present a model of larval mechanics for axial and transverse motion over a planar substrate, and use it to develop a simple, reflexive neuromuscular model from physical principles. The mechanical model represents the midline of the larva as a set of point masses which interact with each other via damped translational and torsional springs, and with the environment via sliding friction forces. The neuromuscular model consists of: 1. segmentally localised reflexes that amplify axial compression in order to counteract frictive energy losses, and 2. long-range mutual inhibition between reflexes in distant segments, enabling overall motion of the model larva relative to its substrate. In the absence of damping and driving, the mechanical model produces axial travelling waves, lateral oscillations, and unpredictable, chaotic deformations. The neuromuscular model counteracts friction to recover these motion patterns, giving rise to forward and backward peristalsis in addition to turning. Our model produces spontaneous exploration, even though the nervous system has no intrinsic pattern generating or decision making ability, and neither senses nor drives bending motions. Ultimately, our model suggests a novel view of larval exploration as a deterministic superdiffusion process which is mechanistically grounded in the chaotic mechanics of the body. We discuss how this may provide new interpretations for existing observations at the level of tissue-scale activity patterns and neural circuitry, and provide some experimental predictions that would test the extent to which the mechanisms we present translate to the real larva.

We investigate the relationship between brain, body and environment in the exploratory behaviour of fruitfly larva. A larva crawls forward by propagating a wave of compression through its segmented body, and changes its crawling direction by bending to one side or the other. We show first that a purely mechanical model of the larva’s body can produce travelling compression waves, sideways bending, and unpredictable, chaotic motions. For this body to locomote through its environment, it is necessary to add a neuromuscular system to counteract the loss of energy due to friction, and to limit the simultaneous compression of segments. These simple additions allow our model larva to generate life-like forward and backward crawling as well as spontaneous turns, which occur without any direct sensing or control of reorientation. The unpredictability inherent in the larva’s physics causes the model to explore its environment, despite the lack of any neural mechanism for rhythm generation or for deciding when to switch from crawling to turning. Our model thus demonstrates how understanding body mechanics can generate and simplify neurobiological hypotheses as to how behaviour arises.

Body stiffness and damping depend sensitively on the timing of muscle activation in lampreys

Unlike most manmade machines, animals move through their world using flexible bodies and appendages, which bend due to internal muscle and body forces, and also due to forces from the environment. Fishes in particular must cope with fluid dynamic forces that not only resist their overall swimming movements but also may have unsteady flow patterns, vortices, and turbulence, many of which occur more rapidly than what the nervous system can process. Has natural selection led to mechanical properties of fish bodies and their component tissues that can respond very quickly to environmental perturbations? Here, we focus on the mechanical properties of isolated muscle tissue and of the entire intact body in the silver lamprey, Ichthyomyzon unicuspis. We developed two modified work loop protocols to determine the effect of small perturbations on the whole body and on isolated segments of muscle as a function of muscle activation and phase within the swimming cycle. First, we examined how the mechanical properties of the whole lamprey body change depending on the timing of muscle activity. Relative to passive muscle, muscle activation can modulate the effective stiffness by about two-fold and modulate the effective damping by >10-fold depending on the activation phase. Next, we performed a standard work loop test on small sections of axial musculature while adding low-amplitude sinusoidal perturbations at specific frequencies. We modeled the data using a new system identification technique based on time-periodic system analysis and harmonic transfer functions (HTFs) and used the resulting models to predict muscle function under novel conditions. We found that the effective stiffness and damping of muscle varies during the swimming cycle, and that the timing of activation can alter both the magnitude and timing of peak stiffness and damping. Moreover, the response of the isolated muscle was highly nonlinear and length dependent, but the body's response was much more linear. We applied the resulting HTFs from our experiments to explore the effect of pairs of antagonistic muscles. The results suggest that when muscles work against each other as antagonists, the combined system has weaker nonlinearities than either muscle segment alone. Together, these results begin to provide an integrative understanding of how activation timing can tune the mechanical response properties of muscles, enabling fish to swim effectively in their complex and unpredictable environment.

Is “the brain” a helpful metaphor for neuroscience?

Brette criticizes the notion of neural coding as used in neuroscience as a way to clarify the causal structure of the brain. This criticism will be positioned in a wider range of findings and ideas from other branches of neuroscience and biology. While supporting Brette's critique, these findings also suggest the need for more radical changes in neuroscience than Brette envisions.

OpenWorm: overview and recent advances in integrative biological simulation of Caenorhabditis elegans

The adoption of powerful software tools and computational methods from the software industry by the scientific research community has resulted in a renewed interest in integrative, large-scale biological simulations. These typically involve the development of computational platforms to combine diverse, process-specific models into a coherent whole. The OpenWorm Foundation is an independent research organization working towards an integrative simulation of the nematode Caenorhabditis elegans, with the aim of providing a powerful new tool to understand how the organism's behaviour arises from its fundamental biology. In this perspective, we give an overview of the history and philosophy of OpenWorm, descriptions of the constituent sub-projects and corresponding open-science management practices, and discuss current achievements of the project and future directions.This article is part of a discussion meeting issue 'Connectome to behaviour: modelling C. elegans at cellular resolution'.

Three-dimensional simulation of the Caenorhabditis elegans body and muscle cells in liquid and gel environments for behavioural analysis

To better understand how a nervous system controls the movements of an organism, we have created a three-dimensional computational biomechanical model of the Caenorhabditis elegans body based on real anatomical structure. The body model is created with a particle system-based simulation engine known as Sibernetic, which implements the smoothed particle-hydrodynamics algorithm. The model includes an elastic body-wall cuticle subject to hydrostatic pressure. This cuticle is then driven by body-wall muscle cells that contract and relax, whose positions and shape are mapped from C. elegans anatomy, and determined from light microscopy and electron micrograph data. We show that by using different muscle activation patterns, this model is capable of producing C. elegans-like behaviours, including crawling and swimming locomotion in environments with different viscosities, while fitting multiple additional known biomechanical properties of the animal. This article is part of a discussion meeting issue 'Connectome to behaviour: modelling C. elegans at cellular resolution'.

Signatures of proprioceptive control in Caenorhabditis elegans locomotion

Animal neuromechanics describes the coordinated self-propelled movement of a body, subject to the combined effects of internal neural control and mechanical forces. Here we use a computational model to identify effects of neural and mechanical modulation on undulatory forward locomotion of Caenorhabditis elegans, with a focus on proprioceptively driven neural control. We reveal a fundamental relationship between body elasticity and environmental drag in determining the dynamics of the body and demonstrate the manifestation of this relationship in the context of proprioceptively driven control. By considering characteristics unique to proprioceptive neurons, we predict the signatures of internal gait modulation that contrast with the known signatures of externally or biomechanically modulated gait. We further show that proprioceptive feedback can suppress neuromechanical phase lags during undulatory locomotion, contrasting with well studied advancing phase lags that have long been a signature of centrally generated, feed-forward control.This article is part of a discussion meeting issue 'Connectome to behaviour: modelling C. elegans at cellular resolution'.

Connectome to behaviour: modelling Caenorhabditis elegans at cellular resolution

It has been 30 years since the 'mind of the worm' was published in Philosophical Transactions B (White et al 1986 Phil. Trans. R. Soc. Lond. B314, 1-340). Predicting Caenorhabditis elegans' behaviour from its wiring diagram has been an enduring challenge since then. This special theme issue of Philosophical Transactions B combines research from neuroscientists, physicists, mathematicians and engineers to discuss advances in neural activity imaging, behaviour quantification and multiscale simulations, and how they are bringing the goal of whole-animal modelling at cellular resolution within reach.This article is part of a discussion meeting issue 'Connectome to behaviour: modelling C. elegans at cellular resolution'.

Development of vestibular behaviors in zebrafish

Most animals orient their bodies with respect to gravity to facilitate locomotion and perception. The neural circuits responsible for these orienting movements have long served as a model to address fundamental questions in systems neuroscience. Though postural control is vital, we know little about development of either balance reflexes or the neural circuitry that produces them. Recent work in a genetically and optically accessible vertebrate, the larval zebrafish, has begun to reveal the mechanisms by which such vestibular behaviors and circuits come to function. Here we highlight recent work that leverages the particular advantages of the larval zebrafish to illuminate mechanisms of postural development, the role of sensation for balance circuit development, and the organization of developing vestibular circuits. Further, we frame open questions regarding the developmental mechanisms for functional circuit assembly and maturation where studying the zebrafish vestibular system is likely to open new frontiers.

Towards an integrated view of vocal development

Vocal development is usually studied from the perspective of neuroscience. In this issue, Zhang and Ghazanfar propose a way in which body growth might condition the process. They study the vocalizations of marmoset infants with a wide range of techniques that include computational models and experiments that mimic growth reversal. Their results suggest that the qualitative changes that occur during development are rooted in the nonlinear interaction between the nervous system and the biomechanics involved in respiration. This work illustrates how an integrative approach enriches our understanding of behavior.