Adaptive motor control in crayfish

International audienceThis article reviews the principles that rule the organization of motor commands that have been described over the past ®ve decades in cray®sh. The adaptation of motor behaviors requires the integration of sensory cues into the motor command. The respective roles of central neural networks and sensory feedback are presented in the order of increasing complexity. The simplest circuits described are those involved in the control of a single joint during posture (negative feedback±resistance re¯ex) and movement (modulation of sensory feedback and reversal of the re¯ex into an assistance re¯ex). More complex integration is required to solve problems of coordination of joint movements in a pluri-segmental appendage, and coordination of dierent limbs and dierent motor systems. In addition, beyond the question of mechanical ®tting, the motor command must be appropriate to the behavioral context. Therefore, sensory information is used also to select adequate motor programs. A last aspect of adaptability concerns the possibility of neural networks to change their properties either temporarily (such on-line modulation exerted, for example, by presynaptic mechanisms) or more permanently (such as plastic changes that modify the synaptic ecacy). Finally, the question of how``automatic'' local component networks are controlled by descending pathways, in order to achieve behaviors, is discussed.

Neuromechanical Analysis of Locust Jumping

The nervous systems of animals evolved to exert dynamic control of behavior in response to the needs of the animal and changing signals from the environment. To understand the mechanisms of dynamic control, we need a means of predicting how individual neural and body elements will interact to produce the performance of the entire system. We have developed a neuromechanical application named AnimatLab that addresses this problem through simulation. A computational model of a body and nervous system can be constructed from simple components and situated in a virtual world for testing. Simulations and live experiments were used to investigate questions about locust jumping. The neural circuitry and biomechanics of kicking in locusts have been extensively studied. It has been hypothesized that the same neural circuit and biomechanics governed both behaviors, but this hypothesis was not testable with current technology. We built a neuromechanical model to test this and to gain a better understanding of the role of the semi-lunar process (SLP) in jump dynamics. The SLP are bands of cuticle that store energy for use during jumping. The results of the model were compared to a variety of published data and were similar. The SLP significantly increased jump distance, power, total energy, and duration of the jump impulse. Locust can jump precisely to a target, but also exhibit tumbling. We proposed two mechanisms for controlling tumbling during the jump. The first was that locusts adjust the pitch of their body prior to the jump to move the center of mass closer to the thrust vector. The second was that contraction of the abdominal muscles during the jump produced torques that countered the torque due to thrust. There was a strong correlation relating increased pitch and takeoff angle. In simulations there was an optimal pitch-takeoff combination that minimized tumbling that was similar to the live data. The direction and magnitude of tumbling could be controlled by adjusting abdominal tension. Tumbling also influenced jump elevation. Neuromechanical simulation addressed problems that would be difficult to examine using traditional physiological approaches. It is a powerful tool for understanding the neural basis of behavior

Modular construction of nervous systems: a basic principle of design for invertebrates and vertebrates

As evidenced by the proliferation of papers in the last 30 years it is now well accepted that an iterative columnar or modular organization of the neocortex is characteristic of mammalian sensory, motor and frontal association areas. This does not imply that all mammalian neocortical areas are thus arranged; exceptions occur, particularly in the rodents

Multimodal Proprioceptive Integration in Sensorimotor Networks of an Insect Leg

An animal’s nervous system monitors the actions of the body using its sense of proprioception. This information is used for precise motor control and to enable coordinated interaction with the animal’s surroundings. Proprioception is a multimodal sense that includes feedback about limb movement and loading from various peripheral sense organs. The sensory information from distinct sense organs must be integrated by the network to form a coherent representation of the current proprioceptive state and to elicit appropriate motor behavior. By combining intra- and extracellular electrophysiological recording techniques with precise mechanical sensory stimulation paradigms, I studied multimodal proprioceptive integration in the sensorimotor network of the stick insect leg. The findings demonstrate where, when, and how sensory feedback from load-sensing campaniform sensilla (CS) is integrated with movement information from the femoral chordotonal organ (fCO) in the sensorimotor network controlling movement of the femur-tibia (FTi) joint. Proprioceptive information about distinct sensory modalities (load / movement) and from distinct sense organs of the same sensory modality (trochanterofemoral CS (tr/fCS) / tibial CS (tiCS)) was distributed into one network of local premotor nonspiking interneurons (NSIs). The NSIs’ processing of fCO, tr/fCS, and tiCS was antagonistic with respect to a given NSI’s effect on the motor output of extensor tibiae motor neurons (ExtTi MNs). Spatial summation of load and movement feedback occurred in the network of premotor NSIs, whereas temporal summation was shifted between sensory modalities. Load feedback (tr/fCS / tiCS) was consistently delayed relative to movement signals (fCO) throughout the sensorimotor pathways of sensory afferents, premotor NSIs, and ExtTi MNs. The connectivity between these neuron types was inferred using transmission times and followed distinct patterns for individual sense organs. At the motor output level of the system, the temporal shift of simultaneously elicited load and movement feedback caused load responses to be superimposed onto ongoing movement responses. These results raised the hypothesis that load could alter movement signal processing. Load (tiCS) affected movement (fCO) signal gain by presynaptic afferent inhibition. In postsynaptic premotor NSIs, this led to altered movement parameter dependence and nonlinear summation of load and movement signals. Specifically, the amplitude dependence of NSIs opposing ExtTi MN output was increased, and, consistently, the movement response gain of the slow ExtTi MN was decreased. Movement signal processing in the premotor network was altered depending on the proprioceptive context, i.e. the presence or absence of load feedback. Lateral presynaptic interactions between load (tiCS) and movement (fCO) afferents were reciprocal, i.e. existed from fCO to tiCS afferents and vice versa, and also occurred between sensory afferents of the same sense organ. Additionally, a new type of presynaptic interaction was identified. Load signals increased the gain of directional movement information by releasing unidirectionally velocity- or acceleration-sensitive fCO afferents from tonic presynaptic inhibition. Paired double recordings showed lateral connectivity also at the level of the premotor NSI network. NSIs interacted via reciprocal excitatory connections. Additionally, the activity of individual NSIs was correlated in the absence of external stimuli, and specific types of NSIs showed rhythmic 30 Hz oscillations of the resting membrane potential, indicating an underlying mechanism of network synchronization. Taken together, the results of this dissertation provide an understanding of the integration of multimodal proprioceptive feedback in the sensorimotor network by identifying neuronal pathways and mechanism underlying spatial and temporal signal summation. The local network uses multimodal signal integration for context-dependent sensory processing, thereby providing insights into the mechanism by which a local network can adapt sensory processing to the behavioral context. Initial results clearly highlight the necessity to consider lateral connections along sensorimotor pathways to unravel the complex computations underlying proprioceptive processing and motor control. The findings on the integration of proprioceptive signals, obtained in the resting animal, broaden our understanding of sensorimotor processing and motor control not only in the stationary, but also in the walking animal

Locomotor Network Dynamics Governed By Feedback Control In Crayfish Posture And Walking

Sensorimotor circuits integrate biomechanical feedback with ongoing motor activity to produce behaviors that adapt to unpredictable environments. Reflexes are critical in modulating motor output by facilitating rapid responses. During posture, resistance reflexes generate negative feedback that opposes perturbations to stabilize a body. During walking, assistance reflexes produce positive feedback that facilitates fast transitions between swing and stance of each step cycle. Until recently, sensorimotor networks have been studied using biomechanical feedback based on external perturbations in the presence or absence of intrinsic motor activity. Experiments in which biomechanical feedback driven by intrinsic motor activity is studied in the absence of perturbation have been limited. Thus, it is unclear whether feedback plays a role in facilitating transitions between behavioral states or mediating different features of network activity independent of perturbation. These properties are important to understand because they can elucidate how a circuit coordinates with other neural networks or contributes to adaptable motor output. Computational simulations and mathematical models have been used extensively to characterize interactions of negative and positive feedback with nonlinear oscillators. For example, neuronal action potentials are generated by positive and negative feedback of ionic currents via a membrane potential. While simulations enable manipulation of system parameters that are inaccessible through biological experiments, mathematical models ascertain mechanisms that help to generate biological hypotheses and can be translated across different systems. Here, a three-tiered approach was employed to determine the role of sensory feedback in a crayfish locomotor circuit involved in posture and walking. In vitro experiments using a brain-machine interface illustrated that unperturbed motor output of the circuit was changed by closing the sensory feedback loop. Then, neuromechanical simulations of the in vitro experiments reproduced a similar range of network activity and showed that the balance of sensory feedback determined how the network behaved. Finally, a reduced mathematical model was designed to generate waveforms that emulated simulation results and demonstrated how sensory feedback can control the output of a sensorimotor circuit. Together, these results showed how the strengths of different approaches can complement each other to facilitate an understanding of the mechanisms that mediate sensorimotor integration

Task-specific modulation of a proprioceptive reflex in a walking insect

The generation of task-dependent and goal-directed walking behaviour requires feedback from leg sense organs for regulating and adapting the ongoing motor activity. Sensory feedback from movement and force sensors influences the magnitude and the timing of neural activity generated in the neural networks driving individual joints of a leg. In many animals, the effects of sensory feedback on the generated motor output change between posture maintenance and locomotion. These changes can occur as reflex reversals in which sensory information, that usually counteract perturbations in posture control, instead reinforce movements in walking. In stick insects, for example, flexion of the femur-tibia joint is measured by the femoral chordotonal organ, which mediates reinforcement of the stance phase motor output of the femur-tibia joint when the locomotor system is active. Flexion signals promote flexor and inhibit extensor motoneuron activity. However, the mechanisms underlying these changes are only partially understood. Therefore, the purpose of the present thesis was to investigate whether the processing of movement and position signals of the FTi joint is task-specifically modified in the generation of adaptive leg movements, which is required when locomotion is adapted to changes in walking direction or in turning movements. To study the role of these task-dependent changes in walking behaviour on the processing of local sensory signals, the generation of reflex reversals mediated by the femoral chordotonal organ in the femur-tibia joint of the stick insect Carausius morosus was measured in a semi-intact walking preparation. In several experimental conditions either in front, in one or both middle or in hind legs, the femoral chordotonal organ was mechanically displaced and the motoneuronal responses in the flexor and extensor tibia were monitored, while the remaining legs performed either forward, backward or curve walking on a slippery surface. I demonstrated that the occurrence of reflex reversals depends on the specific motor behaviour executed. While in forward walking flexion signals from the front leg fCO regularly elicit reflex reversal in the tibial motoneurons, this cannot be observed in backward walking. Similarly, during optomotor-induced curve walking, reflex reversal occurred reliably in the middle leg on the inside of the turn, however not in the contralateral leg on the outside of the turn. Thus, the experiments revealed that the nervous system modulates proprioceptive reflexes in individual legs during task-specific walking adaptation. Furthermore, I showed that nonspiking interneurons, known to be involved in the premotor network of the FTi joint, participate in reflex responses in both the inner and outer middle leg during curve walking. First results show that the reflex response in some interneuron types is altered between the inner and outer leg, while no differences were found in others

Encoding of Coordinating Information in a Network of Coupled Oscillators

Animal locomotion is driven by cyclic movements of the body or body appendages. These movements are under the control of neural networks that are driven by central pattern generators (CPG). In order to produce meaningful behavior, CPGs need to be coordinated. The crayfish swimmeret system is a model to investigate the coordination of distributed CPGs. Swimmerets are four pairs of limbs on the animal’s abdomen, which move in cycles of alternating power-strokes and return-strokes. The swimmeret pairs are coordinated in a metachronal wave from posterior to anterior with a phase lag of approximately 25% between segments. Each swimmeret is controlled by its own neural microcircuit, located in the body segment’s hemiganglion. Three neurons per hemiganglion are necessary and sufficient for the 25% phase lag. ASCE DSC encode information about their home ganglion’s activity state and send it to their anterior or posterior target ganglia, respectively. ComInt 1, which is electrically coupled to the CPG, receives the coordinating information. The isolated abdominal ganglia chain reliably produces fictive swimming. Motor burst strength is encoded by the number of spikes per ASCE and DSC burst. If motor burst strength varies spontaneously, the coordinating neurons track these changes linearly. The neurons are hypothesized to adapt their spiking range to the occurring motor burst strengths. One aim of this study was to investigate the putative adaptive encoding of the coordinating neurons in electrophysiological experiments. This revealed that the system’s excitation level influenced both the whole system and the individual coordinating neurons. These mechanisms allowed the coordinating neurons to adapt to the range of burst strengths at any given excitation level by encoding relative burst strengths. The second aim was to identify the transmitters of the coordinating neurons at the synapse to ComInt 1. Immunohistochemical experiments demonstrated that coordinating neurons were not co-localized with serotonin-immunoreactive positive neurons. MALDI-TOF mass spectrometry suggested acetylcholine as presumable transmitter

A neurobiological and computational analysis of target discrimination in visual clutter by the insect visual system.

Some insects have the capability to detect and track small moving objects, often against cluttered moving backgrounds. Determining how this task is performed is an intriguing challenge, both from a physiological and computational perspective. Previous research has characterized higher-order neurons within the fly brain known as 'small target motion detectors‘ (STMD) that respond selectively to targets, even within complex moving surrounds. Interestingly, these cells still respond robustly when the velocity of the target is matched to the velocity of the background (i.e. with no relative motion cues). We performed intracellular recordings from intermediate-order neurons in the fly visual system (the medulla). These full-wave rectifying, transient cells (RTC) reveal independent adaptation to luminance changes of opposite signs (suggesting separate 'on‘ and 'off‘ channels) and fast adaptive temporal mechanisms (as seen in some previously described cell types). We show, via electrophysiological experiments, that the RTC is temporally responsive to rapidly changing stimuli and is well suited to serving an important function in a proposed target-detecting pathway. To model this target discrimination, we use high dynamic range (HDR) natural images to represent 'real-world‘ luminance values that serve as inputs to a biomimetic representation of photoreceptor processing. Adaptive spatiotemporal high-pass filtering (1st-order interneurons) shapes the transient 'edge-like‘ responses, useful for feature discrimination. Following this, a model for the RTC implements a nonlinear facilitation between the rapidly adapting, and independent polarity contrast channels, each with centre-surround antagonism. The recombination of the channels results in increased discrimination of small targets, of approximately the size of a single pixel, without the need for relative motion cues. This method of feature discrimination contrasts with traditional target and background motion-field computations. We show that our RTC-based target detection model is well matched to properties described for the higher-order STMD neurons, such as contrast sensitivity, height tuning and velocity tuning. The model output shows that the spatiotemporal profile of small targets is sufficiently rare within natural scene imagery to allow our highly nonlinear 'matched filter‘ to successfully detect many targets from the background. The model produces robust target discrimination across a biologically plausible range of target sizes and a range of velocities. We show that the model for small target motion detection is highly correlated to the velocity of the stimulus but not other background statistics, such as local brightness or local contrast, which normally influence target detection tasks. From an engineering perspective, we examine model elaborations for improved target discrimination via inhibitory interactions from correlation-type motion detectors, using a form of antagonism between our feature correlator and the more typical motion correlator. We also observe that a changing optimal threshold is highly correlated to the value of observer ego-motion. We present an elaborated target detection model that allows for implementation of a static optimal threshold, by scaling the target discrimination mechanism with a model-derived velocity estimation of ego-motion. Finally, we investigate the physiological relevance of this target discrimination model. We show that via very subtle image manipulation of the visual stimulus, our model accurately predicts dramatic changes in observed electrophysiological responses from STMD neurons.Thesis (Ph.D.) - University of Adelaide, School of Molecular and Biomedical Science, 200

Adaptive Motor Control: Neuronal Mechanisms Underlying (Targeted) Searching Movements

Animals move through a complex environment and therefore constantly need to adapt their behavior to the surroundings. For this purpose, they use sensory information of various kind. As one strategy to gain tactile cues, animals perform leg searching movements when loosing foothold. The kinematics of these searching movements have been well investigated in the stick insect. In this thesis, the modification of stick insect searching movements following a tactile cue are explored as an example of a sensory-motor system that adapts to environmental conditions. Furthermore, the premotor neuronal network underlying the generation of searching behavior is investigated. Searching movements were studied in animals with a single intact leg that was free to move in the vertical plane. After several cycles of searching movements, a stick was introduced into the plane of movements such that animals would touch it with its distal leg. As is known from previous studies, in such a situation stick insects try to grasp the object that they touch. In my experiments, the stick was retracted as soon as a brief contact with the animals' leg had occurred. Therefore, animals could not grasp the stick. I could show that following this short tactile cue, stick insects modify their searching movements to target the former position of the object (PO). Targeting occurs by a change in two parameters of searching movements: animals (i) shift the average leg position of their searching movements towards the PO and (ii) confine searching movements to the PO by a reduction in movement amplitude. These two parameters, position and amplitude, can be changed independently of each other. Searching movements are flexibly adjusted to different locations of the object which demonstrates the targeted response to be a situation-dependent adaptive behavior. The targeted response outlasts the tactile stimulus by several seconds suggesting a simple form of short term memory of the PO as proposed for targeted movements of other insects. Vision is not necessary for a targeted response. Instead, tactile cues from leg sensory organs are important. Two proprioceptive organs, the trochanteral hairplate (trHP) and the femoral chordotonal organ (fCO), are crucial for targeting. Other sensory organs like tactile hairs and campaniform sensilla are dispensable. The brain is not necessary for a targeted response, therefore the adaptation of searching movements is likely to be mediated on the thoracic level. The premotor neuronal network underlying searching movement generation was investigated using the same single-leg preparation as described above. Nonspiking interneurons (NSIs) of the premotor network were recorded intracellularly during searching movements. Additionally, EMG recordings of the four main leg muscles that generate searching movements in the vertical plane were recorded. The membrane potential of previously described, as well as newly identified NSIs providing synaptic drive to leg motoneurons is shown to be phasically modulated during searching. Therefore, NSIs are part of the premotor network for the generation of searching movements. NSIs that were previously described to contribute to the generation of walking behavior are shown to contribute to the generation of searching behavior. When artificially de- or hyperpolarized by current injection, several NSIs are able to induce changes in searching movement parameters like position, amplitude, velocity of movements, or inter-joint coordination. One NSI is able to drive or stop searching movements. Each NSI acts on a specific set of parameters. The same NSIs that were recorded during searching also were recorded during walking behavior. In comparison, NSI membrane potential modulations during searching are smaller in amplitude and more undulated than during walking. In contrast, fast transitions in NSI membrane potential are closely coupled to step phase transitions during walking. The most prominent difference in NSI membrane potential occurs during step phase (when walking) as compared to flexion phase (during searching). This difference might be attributed to load signals from campaniform sensilla. Analogous to results of previous studies in the stick insect, this highlights the importance of sensory feedback in shaping the motor output. Finally, NSIs were recorded intracellularly while animals with their searching leg made contact with the stick that was introduced into the plane of movement. First results indicate that the response of a given NSI to this contact is characteristic and depends on the direction of touch

Tarsal intersegmental reflex responses in the locust hind leg

Locomotion is vital for vertebrates and invertebrates to survive. However, the mechanisms for locomotion are partially unknown. Central Pattern Generators and reflex systems have been shown to be the basis of most movements performed by arthropods. Much has been investigated lately on Central Pattern Generators, but little work has been done in reflex systems. Locomotion and motor output in feet (or tarsus in arthropods) has also been disregarded in research. Despite that feet are responsible for stability and agility in most animals, research on feet movements is scarce.In this thesis the tarsal intersegmental reflex of the locust hind leg is investigated. The tarsal reflex consists of a response in the tarsus when there is a change in the femoro-tibial joint. The main objective of the thesis is to describe the system and to develop mathematical and experimental methods to study, model and analyse it. Through a set of experiments is shown that as the knee joint is extended, the tarsus is depressed, and as the knee joint flexes, the tarsus levates. The experiments demonstrated that there is a purely neuronal link between the femoro-tibial joint position and the tibio-tarsal joint position. Moreover, it also reveals the effect of neuromodulatory compounds, such as dopamine, serotonin or octopamine. The tarsal reflex responses are fairly consistent across individuals, although significant variability across animals was found.To model a system where variability is an issue, a mathematical model with strong generalisation abilities is used: Artificial Neural Networks (ANNs). To design the ANNs, a metaheuristic algorithm has been implemented. The resulting ANNs are shown to be as accurate as other mathematical models used in physiology when used in a well known reflex system, the FETi responses. This results showed that ANNs are as good as Wiener methods in predicting responses and they outperform them in prediction of Gaussian inputs. Furthermore, they are able to predict responses in different animals, independently of the variability, with a more limited performance.New experimental methods are also designed to obtain accurate recordings of tarsal movements in response to knee joint changes. These experimental methods facilitate the data acquisition and its accuracy, reducing measurement errors. Using the mathematical methods validated, these responses are modelled and studied, showing responses to Gaussian and sinusoidal inputs, variability across individuals and effects of neuromodulators.With the tarsal reflex described and modelled, it can be used as a tool for further research in disciplines such as medicine, in the diagnose and treatment of euromuscular dysfunction or design of prosthesis and orthoses. This model can also be implemented in robotics to aid in stability when walking on irregular terrain