A mathematical modeling study of inter-segmental coordination during stick insect walking

The biomechanical conditions for walking in the stick insect require a modeling approach that is based on the control of pairs of antagonistic motoneuron (MN) pools for each leg joint by independent central pattern generators (CPGs). Each CPG controls a pair of antagonistic MN pools. Furthermore, specific sensory feedback signals play an important role in the control of single leg movement and in the generation of inter-leg coordination or the interplay between both tasks. Currently, however, no mathematical model exists that provides a theoretical approach to understanding the generation of coordinated locomotion in such a multi-legged locomotor system. In the present study, I created such a theoretical model for the stick insect walking system, which describes the MN activity of a single forward stepping middle leg and helps to explain the neuronal mechanisms underlying coordinating information transfer between ipsilateral legs. In this model, CPGs that belong to the same leg, as well as those belonging to different legs, are connected by specific sensory feedback pathways that convey information about movements and forces generated during locomotion. The model emphasizes the importance of sensory feedback, which is used by the central nervous system to enhance weak excitatory and inhibitory synaptic connections from front to rear between the three thorax-coxa-joint CPGs. Thereby the sensory feedback activates caudal pattern generation networks and helps to coordinate leg movements by generating in-phase and out-of-phase thoracic MN activity.     

Dynamic simulation of insect walking

Insect walking relies on a complex interaction between the environment, body segments, muscles and the nervous system. For the stick insect in particular, previous investigations have highlighted the role of specific sensory signals in the timing of activity of central neural networks driving the individual leg joints. The objective of the current study was to relate specific sensory and neuronal mechanisms, known from experiments on reduced preparations, to the generation of the natural sequence of events forming the step cycle in a single leg. We have done this by simulating a dynamic 3D-biomechanical model of the stick insect coupled to a reduced model of the neural control system, incorporating only the mechanisms under study. The neural system sends muscle activation levels to the biomechanical system, which in turn provides correctly timed propriosensory signals back to the neural model. The first simulations were designed to test if the currently known mechanisms would be sufficient to explain the coordinated activation of the different leg muscles in the middle leg. Two experimental situations were mimicked: restricted stepping where only the coxa-trochanteral joint and the femur-tibia joint were free to move, and the unrestricted single leg movements on a friction-free surface. The first of these experimental situations is in fact similar to the preparation used in gathering much of the detailed knowledge on sensory and neuronal mechanisms. The simulations show that the mechanisms included can indeed account for the entire step cycle in both situations. The second aim was to test to what extent the same sensory and neuronal mechanisms would be adequate also for controlling the front and hind legs, despite the large differences in both leg morphology and kinematic patterns. The simulations show that front leg stepping can be generated by basically the same mechanisms while the hind leg control requires some reorganization. The simulations suggest that the influence from the femoral chordotonal organs on the network controlling levation-depression may have a reversed effect in the hind legs as compared to the middle and front legs. This, and other predictions from the model will have to be confirmed by additional experiments.     

Intersegmental transfer of sensory signals in the stick insect leg muscle control system

Intersegmental coordination during locomotion in legged animals arises from mechanical couplings and the exchange of neuronal information between legs. Here, the information flow from a single leg sense organ of the stick insect Cuniculina impigra onto motoneurons and interneurons of other legs was investigated. The femoral chordotonal organ (fCO) of the right middle leg, which measures posture and movement of the femur-tibia joint, was stimulated, and the responses of the tibial motoneuron pools of the other legs were recorded. In resting animals, fCO signals did not affect motoneuronal activity in neighboring legs. When the locomotor system was activated and antagonistic motoneurons were bursting in alternation, fCO stimuli facilitated transitions from flexor to extensor activity and vice versa in the contralateral leg. Following pharmacological treatment with picrotoxin, a blocker of GABA-ergic inhibition, the tibial motoneurons of all legs showed specific responses to signals from the middle leg fCO. For the contralateral middle leg we show that fCO signals encoding velocity and position of the tibia were processed by those identified local premotor nonspiking interneurons known to contribute to posture and movement control during standing and voluntary leg movements. Interneurons received both excitatory and inhibitory inputs, so that the response of some interneurons supported the motoneuronal output, while others opposed it. Our results demonstrate that sensory information from the fCO specifically affects the motoneuronal activity of other legs and that the layer of premotor nonspiking interneurons is a site of interaction between local proprioceptive sensory signals and proprioceptive signals from other legs.     

Transforming bottom-up topographic representations with top-down signals in the brain

There has been considerable success in allocating function to the different parts of the brain. We also know much about brain organisation in different regions of the brain and how different brain regions connect to one another. One of the most important next steps for modern neuroscience is to work out how different areas of the brain interact with one another. In particular we need to know how sensory regions communicate with association areas and vice versa. This article explores how top-down signals originating from association areas may be used to process and transform bottom-up representations originating from sensory areas of the brain. Simple models of networks containing topographically organised ensembles of neurons used to integrate and process information are described. The different models can be used to process information in a variety of different ways that could be used as the starting point for a variety of cognitive operations, in particular the extraction of abstract information from sensory representations.     

Transforming bottom-up topographic representations with top-down signals in the brain

There has been considerable success in allocating function to the different parts of the brain. We also know much about brain organisation in different regions of the brain and how different brain regions connect to one another. One of the most important next steps for modern neuroscience is to work out how different areas of the brain interact with one another. In particular we need to know how sensory regions communicate with association areas and vice versa. This article explores how top-down signals originating from association areas may be used to process and transform bottom-up representations originating from sensory areas of the brain. Simple models of networks containing topographically organised ensembles of neurons used to integrate and process information are described. The different models can be used to process information in a variety of different ways that could be used as the starting point for a variety of cognitive operations, in particular the extraction of abstract information from sensory representations.     

An inter-segmental network model and its use in elucidating gait-switches in the stick insect

Animal locomotion requires highly coordinated working of the segmental neuronal networks that control the limb movements. Experiments have shown that sensory signals originating from the extremities play a pivotal role in controlling locomotion patterns by acting on central networks. Based on the results from stick insect locomotion, we constructed an inter-segmental model comprising local networks for all three legs, i.e. for the pro-, meso- and meta-thorax, their inter-connections and the main sensory inputs modifying their activities. In the model, the local networks are uniform, and each of them consists of a central pattern generator (CPG) providing the rhythmic oscillation for the protractor-retractor motor systems, the corresponding motoneurons (MNs), and local inhibitory interneurons (IINs) between the CPGs and the MNs. Between segments, the CPGs are connected cyclically by both excitatory and inhibitory pathways that are modulated by the aforementioned sensory inputs. Simulations done with our network model showed that it was capable of reproducing basic patterns of locomotion such as those occurring during tri- and tetrapod gaits. The model further revealed a number of elementary neuronal processes (e.g. synaptic inhibition, or changing the synaptic drive at specific neurons) that in the simulations were necessary, and in their entirety sufficient, to bring about a transition from one type of gait to another. The main result of this simulation study is that exactly the same mechanism underlies the transition between the two types of gait irrespective of the direction of the change. Moreover, the model suggests that the majority of these processes can be attributed to direct sensory influences, and changes are required only in centrally controlled synaptic drives to the CPGs.     

Electromyographic characterization of walking behavior initiated spontaneously in crayfish

Crayfish initiate walking behavior not only reflexively in response to external stimuli but also spontaneously in the absence of any specific stimulus. In order to analyze the initiation mechanism underlying these different types of walking, we made simultaneous electromyographic (EMG) recordings from thoracic legs when animals initiated walking, either reflexively or spontaneously, and video recorded their movements synchronously with the EMG recording. Two different stimuli, mechanical and chemical, were used to reflexively induce walking. A non-rhythmic, sustained activation of leg muscles was found to precede the behavioral initiation of either type of walking. The duration of this non-rhythmic muscle activation was significantly longer in the spontaneously initiated walking than in the mechanical stimulus-evoked walking, although no difference was observed between the spontaneous and chemical stimulus-evoked walking. EMG recordings from all eight legs revealed that their non-rhythmic muscle activation occurred almost simultaneously prior to initiation of rhythmical stepping movements. When an animal was suspended without a leg substratum, the timing of muscle activation was more variable among the legs than in the free condition on the substratum. When the circumesophageal commissures were both severed to eliminate signals descending from the brain to the thoracic ganglia, the bilaterally coordinated rhythmic burst activity was not observed in the walking legs. These findings suggest that the spontaneous initiation of walking behavior requires sensory feedback signals from leg proprioceptors, subserved by a different descending activation mechanism from that for stimulus-driven initiation of walking.     

Differential control of temporal and spatial aspects of cockroach leg coordination

Ensembles of neuronal networks and sensory pathways participate in controlling the kinematic and dynamic parameters of animal movement necessary to achieve motor coordination. Determining the relative contribution of proprioceptive feedback is essential for understanding how animals sustain stable, coordinated locomotion in complex natural environments. Here, we focus on the role of chordotonal organs (COs), proprioceptors found in insect legs, in the spatial and temporal regulation of walking. We compare gait parameters of intact cockroaches (Periplaneta americana) and sensory-impaired ones, injected with pymetrozine, a chemical previously shown to abolish CO function in locusts. We verify that afferent CO activity in pymetrozine-treated cockroaches is inhibited, and analyze the effect of this sensory deprivation on inter-leg coordination. We find significant changes in tarsi placement and leg path trajectories after pymetrozine treatment. Leg touchdown accuracy, measured from relative tarsi positions of adjacent legs, is reduced in treated animals. Interestingly, despite poorer spatial coordination in both stance and swing, temporal properties of the gait remain largely the same as in the intact preparations, apart from changes in ipsilateral phase differences between front and middle legs. These findings provide insights into the role of COs in insect gait control and establish pymetrozine as a useful tool for further studies of insect locomotion.     

Vibration signals from the FT joint can induce phase transitions in both directions in motoneuron pools of the stick insect walking system

The influence of vibratory signals from the femoral chordotonal organ fCO on the activities of muscles and motoneurons in the three main leg joints of the stick insect leg, i.e., the thoraco-coxal (TC) joint, the coxa-trochanteral (CT) joint, and the femur-tibia (FT) joint, was investigated when the animal was in the active behavioral state. Vibration stimuli induced a switch in motor activity (phase transition), for example, in the FT joint motor activity switched from flexor tibiae to extensor tibiae or vice versa. Similarly, fCO vibration induced phase transitions in both directions between the motoneuron pools of the TC joint and the CT joint. There was no correlation between the directions of phase transition in different joints. Vibration stimuli presented during simultaneous fCO elongation terminated the reflex reversal motor pattern in the FT joint prematurely by activating extensor and inactivating flexor tibiae motoneurons. In legs with freely moving tibia, fCO vibration promoted phase transitions in tibial movement. Furthermore, ground vibration promoted stance-swing transitions as long as the leg was not close to its anterior extreme position during stepping. Our results provide evidence that, in the active behavioral state of the stick insect, vibration signals can access the rhythm generating or bistable networks of the three main leg joints and can promote phase transitions in motor activity in both directions. The results substantiate earlier findings on the modular structure of the single-leg walking pattern generator and indicate a new mechanism of how sensory influence can contribute to the synchronization of phase transitions in adjacent leg joints independent of the walking direction.     

Dissecting neural circuits for multisensory integration and crossmodal processing

We rely on rich and complex sensory information to perceive and understand our environment. Our multisensory experience of the world depends on the brain''s remarkable ability to combine signals across sensory systems. Behavioural, neurophysiological and neuroimaging experiments have established principles of multisensory integration and candidate neural mechanisms. Here we review how targeted manipulation of neural activity using invasive and non-invasive neuromodulation techniques have advanced our understanding of multisensory processing. Neuromodulation studies have provided detailed characterizations of brain networks causally involved in multisensory integration. Despite substantial progress, important questions regarding multisensory networks remain unanswered. Critically, experimental approaches will need to be combined with theory in order to understand how distributed activity across multisensory networks collectively supports perception.     

Contribution of sensory feedback to ongoing ankle extensor activity during the stance phase of walking

Numerous investigations over the past 15 years have demonstrated that sensory feedback plays a critical role in establishing the timing and magnitude of muscle activity during walking. Here we review recent studies reporting that sensory feedback makes a substantial contribution to the activation of extensor motoneurons during the stance phase. Quantitative analysis of the effects of loading and unloading ankle extensor muscles during walking on a horizontal surface has shown that sensory feedback can increase the activity of ankle extensor muscles by up to 60%. There is currently some uncertainty about which sensory receptors are responsible for this enhancement of extensor activity, but likely candidates are the secondary spindle endings in the ankle extensors of humans and the Golgi tendon organs in the ankle extensors of humans and cats. Two important issues arise from the finding that sensory feedback from the leg regulates the magnitude of extensor activity. The first is the extent to which differences in the magnitude of activity in extensor muscles during different locomotor tasks can be directly attributed to changes in the magnitude of sensory signals, and the second is whether the enhancement of extensor activity is determined primarily by feedback from a specific group of receptors or from numerous groups of receptors distributed throughout the leg. Limitations of current experimental strategies prevent a straightforward empirical resolution of these issues. A potentially fruitful approach in the immediate future is to develop models of the known and hypothesized neuronal networks controlling motoneuronal activity, and use these simulations to control forward dynamic models of the musculo-skeletal system. These simulations would help understand how sensory signals are modified with a change in locomotor task and, in conjunction with physiological experiments, establish the extent to which these modifications can account for changes in the magnitude of motoneuronal activity.     

From Sensory Signals to Modality-Independent Conceptual Representations: A Probabilistic Language of Thought Approach

People learn modality-independent, conceptual representations from modality-specific sensory signals. Here, we hypothesize that any system that accomplishes this feat will include three components: a representational language for characterizing modality-independent representations, a set of sensory-specific forward models for mapping from modality-independent representations to sensory signals, and an inference algorithm for inverting forward models—that is, an algorithm for using sensory signals to infer modality-independent representations. To evaluate this hypothesis, we instantiate it in the form of a computational model that learns object shape representations from visual and/or haptic signals. The model uses a probabilistic grammar to characterize modality-independent representations of object shape, uses a computer graphics toolkit and a human hand simulator to map from object representations to visual and haptic features, respectively, and uses a Bayesian inference algorithm to infer modality-independent object representations from visual and/or haptic signals. Simulation results show that the model infers identical object representations when an object is viewed, grasped, or both. That is, the model’s percepts are modality invariant. We also report the results of an experiment in which different subjects rated the similarity of pairs of objects in different sensory conditions, and show that the model provides a very accurate account of subjects’ ratings. Conceptually, this research significantly contributes to our understanding of modality invariance, an important type of perceptual constancy, by demonstrating how modality-independent representations can be acquired and used. Methodologically, it provides an important contribution to cognitive modeling, particularly an emerging probabilistic language-of-thought approach, by showing how symbolic and statistical approaches can be combined in order to understand aspects of human perception.     

Attention controls multisensory perception via two distinct mechanisms at different levels of the cortical hierarchy

To form a percept of the multisensory world, the brain needs to integrate signals from common sources weighted by their reliabilities and segregate those from independent sources. Previously, we have shown that anterior parietal cortices combine sensory signals into representations that take into account the signals’ causal structure (i.e., common versus independent sources) and their sensory reliabilities as predicted by Bayesian causal inference. The current study asks to what extent and how attentional mechanisms can actively control how sensory signals are combined for perceptual inference. In a pre- and postcueing paradigm, we presented observers with audiovisual signals at variable spatial disparities. Observers were precued to attend to auditory or visual modalities prior to stimulus presentation and postcued to report their perceived auditory or visual location. Combining psychophysics, functional magnetic resonance imaging (fMRI), and Bayesian modelling, we demonstrate that the brain moulds multisensory inference via two distinct mechanisms. Prestimulus attention to vision enhances the reliability and influence of visual inputs on spatial representations in visual and posterior parietal cortices. Poststimulus report determines how parietal cortices flexibly combine sensory estimates into spatial representations consistent with Bayesian causal inference. Our results show that distinct neural mechanisms control how signals are combined for perceptual inference at different levels of the cortical hierarchy.

A combination of psychophysics, computational modelling and fMRI reveals novel insights into how the brain controls the binding of information across the senses, such as the voice and lip movements of a speaker.     

Differential Delta expression underlies the diversity of sensory organ patterns among the legs of the Drosophila adult

Many studies have shown that morphological diversity among homologous animal structures is generated by the homeotic (Hox) genes. However, the mechanisms through which Hox genes specify particular morphological features are not fully understood. We have addressed this issue by investigating how diverse sensory organ patterns are formed among the legs of the Drosophila melanogaster adult. The Drosophila adult has one pair of legs on each of its three thoracic segments (the T1-T3 segments). Although homologous, legs from different segments have distinct morphological features. Our focus is on the formation of diverse patterns of small mechanosensory bristles or microchaetae (mCs) among the legs. On T2 legs, the mCs are organized into a series of longitudinal rows (L-rows) precisely positioned along the leg circumference. The L-rows are observed on all three pairs of legs, but additional and novel pattern elements are found on T1 and T3 legs. For example, at specific positions on T1 and T3 legs, some mCs are organized into transverse rows (T-rows). Our studies indicate that the T-rows on T1 and T3 legs are established as a result of Hox gene modulation of the pathway for patterning the L-row mC bristles. Our findings suggest that the Hox genes, Sex combs reduced (Scr) and Ultrabithorax (Ubx), establish differential expression of the proneural gene achaete (ac) by modifying expression of the ac prepattern regulator, Delta (Dl), in T1 and T3 legs, respectively. This study identifies Dl as a potential link between Hox genes and the sensory organ patterning hierarchy, providing insight into the connection between Hox gene function and the formation of specific morphological features.     

Lessons for circuit function from large insects: towards understanding the neural basis of motor flexibility

Motor behaviors result from information processing that occurs in multiple neural networks acting at all levels from the initial selection of the behavior to its final generation. A long-standing research interest is how single neural networks can help generate different motor behaviors, that is, the origin of motor flexibility. Modern experimental techniques allow studying neural network activity during the production of multiple motor behaviors. Recent data provide strong evidence that the neural networks controlling insect legs are individually modified in task-dependent and finely tuned fashions. Understanding the mechanistic basis of these neural network modifications will be of particular interest in the upcoming years.     

Sensory organs of the thoracic legs of the moth Manduca sexta

Summary The thoracic legs of the moth Manduca sexta acquire a new form and develop a new complement of sensory organs and muscles during metamorphosis from larva to adult. Because of our interest in the reorganization of neural circuitry and the acquisition of new behaviors during metamorphosis, we are characterizing sensory elements of larval and adult legs so that we may determine the contribution of new sensory inputs to the changes in behaviors. Here we describe the sensory structures of adult legs using scanning electron microscopy to view the external sensilla and cobalt staining to examine innervation by underlying sensory neurons. We find that, in contrast to larval legs, the adult legs are covered with a diverse array of sensilla. All three pairs of thoracic legs contain scattered, singly innervated scalelike sensilla. Campaniform sensilla occur singly or in clusters near joints. Hair plates, consisting of numerous singly innervated hairs, are also present near joints. Other more specialized sensilla occur on distal leg segments. These include singly innervated spines, two additional classes of singly innervated hairs, and three classes of multiply innervated sensilla. Internal sensory organs include chordotonal organs, subgenual organs, and multipolar joint receptors.     

Pollution going multimodal: the complex impact of the human-altered sensory environment on animal perception and performance

Anthropogenic sensory pollution is affecting ecosystems worldwide. Human actions generate acoustic noise, emanate artificial light and emit chemical substances. All of these pollutants are known to affect animals. Most studies on anthropogenic pollution address the impact of pollutants in unimodal sensory domains. High levels of anthropogenic noise, for example, have been shown to interfere with acoustic signals and cues. However, animals rely on multiple senses, and pollutants often co-occur. Thus, a full ecological assessment of the impact of anthropogenic activities requires a multimodal approach. We describe how sensory pollutants can co-occur and how covariance among pollutants may differ from natural situations. We review how animals combine information that arrives at their sensory systems through different modalities and outline how sensory conditions can interfere with multimodal perception. Finally, we describe how sensory pollutants can affect the perception, behaviour and endocrinology of animals within and across sensory modalities. We conclude that sensory pollution can affect animals in complex ways due to interactions among sensory stimuli, neural processing and behavioural and endocrinal feedback. We call for more empirical data on covariance among sensory conditions, for instance, data on correlated levels in noise and light pollution. Furthermore, we encourage researchers to test animal responses to a full-factorial set of sensory pollutants in the presence or the absence of ecologically important signals and cues. We realize that such approach is often time and energy consuming, but we think this is the only way to fully understand the multimodal impact of sensory pollution on animal performance and perception.     

Central nervous projection patterns of trichobothria and other cuticular sensilla in the wandering spider Cupiennius salei (Arachnida,Araneae)

Summary Central projections of mechano-and chemoreceptors on the legs and pedipalps of the wandering spider Cupiennius salei were traced by anterograde cobalt fills. The primary afferent fibres from trichobothria, tactile hairs, lyriform organs and contact chemoreceptive hairs enter the leg ganglia and pedipalpal ganglia ventrally. On their way through these ganglia there is very little arborization. The main areas of arborization are in the sensory longitudinal tracts in the suboesophageal nervous mass. The central projections of all mechano-and chemoreceptors examined show somatotopic organization. Sensilla located proximally on the legs are represented in dorsally located sensory longitudinal tracts, whereas those located on distal leg segments enter more ventral tracts. The afferent fibres of receptors of identifical modality on a specific segment of all legs and of the pedipalps overlap in the same tracts. No indication for a tonotopic arrangement of the trichobothrial afferences was found, which might have been associated with the mechanical frequency tuning of the trichobothria known from other experiments. The convergence of the projections of different types of receptors in the sensory longitudinal tracts is considered to be an anatomical basis for their functional interaction in behaviour. Both the convergence of the projections of receptors from the same segment of different legs and the somatotopy are connectivity patterns possibly associated with the orientation of the spiders towards mechanical or chemical cues.     

Spinal and sensory neuromodulation of spinal neuronal networks in humans

The present experiments were designed to gain additionally insight into how the spinal networks process direct spinal stimulation and peripheral sensory inputs to control posture and locomotor movements. We have developed a plantar pressure stimulation system that can deliver naturalistic postural and gait-related patterns of pressure to the soles of the feet to simulate standing and walking, thereby activating and/or modulating the automated spinal circuitry responsible for standing and locomotion. In the present study we compare the patterns of activation among selected motor pools and the kinematic consequences of these activation patterns in response to patterned heel-to-toe mechanical stimulation of the soles of the feet, and/or transcutaneous electrical spinal stimulation, for postural and locomotion regulation. The studies were performed in healthy individuals (n = 12) as well as in subjects (n = 2) with motor complete spinal cord injury. We found that plantar pressure stimulation and/or spinal stimulation can effectively facilitate locomotor output in the subjects placed with their legs in gravity neutral position. We have shown synergistic effects of combining sensory and spinal cord stimulation, suggesting that the two networks are different, but complementary. Also we provide evidence that plantar stimulation could serve as a novel neuro-rehabilitation tool alone or as part of a multi-modal approach to restoring motor function after complete paralysis due to SCI.     

Spontaneous prediction error generation in schizophrenia

Goal-directed human behavior is enabled by hierarchically-organized neural systems that process executive commands associated with higher brain areas in response to sensory and motor signals from lower brain areas. Psychiatric diseases and psychotic conditions are postulated to involve disturbances in these hierarchical network interactions, but the mechanism for how aberrant disease signals are generated in networks, and a systems-level framework linking disease signals to specific psychiatric symptoms remains undetermined. In this study, we show that neural networks containing schizophrenia-like deficits can spontaneously generate uncompensated error signals with properties that explain psychiatric disease symptoms, including fictive perception, altered sense of self, and unpredictable behavior. To distinguish dysfunction at the behavioral versus network level, we monitored the interactive behavior of a humanoid robot driven by the network. Mild perturbations in network connectivity resulted in the spontaneous appearance of uncompensated prediction errors and altered interactions within the network without external changes in behavior, correlating to the fictive sensations and agency experienced by episodic disease patients. In contrast, more severe deficits resulted in unstable network dynamics resulting in overt changes in behavior similar to those observed in chronic disease patients. These findings demonstrate that prediction error disequilibrium may represent an intrinsic property of schizophrenic brain networks reporting the severity and variability of disease symptoms. Moreover, these results support a systems-level model for psychiatric disease that features the spontaneous generation of maladaptive signals in hierarchical neural networks.