Model of a predatory stealth behaviour camouflaging motion

A computational model of a stealth strategy inspired by the apparent mating tactics of male hoverflies is presented. The stealth strategy (motion camouflage) paradoxically allows a predator to approach a moving prey in such a way that it appears to be a stationary object. In the model, the predators are controlled by neural sensorimotor systems that base their decisions on realistic levels of input information. They are shown to be able to employ motion camouflage to approach prey that move along both real hoverfly flight paths and artificially generated flight paths. The camouflaged approaches made demonstrate that the control systems have an ability to predict future prey movements. This is illustrated using two- and three-dimensional simulations.     

Dynamic updating of distributed neural representations using forward models

In this paper, we present a continuous attractor network model that we hypothesize will give some suggestion of the mechanisms underlying several neural processes such as velocity tuning to visual stimulus, sensory discrimination, sensorimotor transformations, motor control, motor imagery, and imitation. All of these processes share the fundamental characteristic of having to deal with the dynamic integration of motor and sensory variables in order to achieve accurate sensory prediction and/or discrimination. Such principles have already been described in the literature by other high-level modeling studies (Decety and Sommerville in Trends Cogn Sci 7:527–533, 2003; Oztop et al. in Neural Netw 19(3):254–271, 2006; Wolpert et al. in Philos Trans R Soc 358:593–602, 2003). With respect to these studies, our work is more concerned with biologically plausible neural dynamics at a population level. Indeed, we show that a relatively simple extension of the classical neural field models can endow these networks with additional dynamic properties for updating their internal representation using external commands. Moreover, an analysis of the interactions between our model and external inputs also shows interesting properties, which we argue are relevant for a better understanding of the neural processes of the brain.     

Deconstructing memory in Drosophila

Unlike most organ systems, which have evolved to maintain homeostasis, the brain has been selected to sense and adapt to environmental stimuli by constantly altering interactions in a gene network that functions within a larger neural network. This unique feature of the central nervous system provides a remarkable plasticity of behavior, but also makes experimental investigations challenging. Each experimental intervention ramifies through both gene and neural networks, resulting in unpredicted and sometimes confusing phenotypic adaptations. Experimental dissection of mechanisms underlying behavioral plasticity ultimately must accomplish an integration across many levels of biological organization, including genetic pathways acting within individual neurons, neural network interactions which feed back to gene function, and phenotypic observations at the behavioral level. This dissection will be more easily accomplished for model systems such as Drosophila, which, compared with mammals, have relatively simple and manipulable nervous systems and genomes. The evolutionary conservation of behavioral phenotype and the underlying gene function ensures that much of what we learn in such model systems will be relevant to human cognition. In this essay, we have not attempted to review the entire Drosophila memory field. Instead, we have tried to discuss particular findings that provide some level of intellectual synthesis across three levels of biological organization: behavior, neural circuitry and biochemical pathways. We have attempted to use this integrative approach to evaluate distinct mechanistic hypotheses, and to propose critical experiments that will advance this field.     

Sensorimotor control of navigation in arthropod and artificial systems

Arthropods exhibit highly efficient solutions to sensorimotor navigation problems. They thus provide a source of inspiration and ideas to robotics researchers. At the same time, attempting to re-engineer these mechanisms in robot hardware and software provides useful insights into how the natural systems might work. This paper reviews three examples of arthropod sensorimotor control systems that have been implemented and tested on robots. First we discuss visual control mechanisms of flies, such as the optomotor reflex and collision avoidance, that have been replicated in analog VLSI (very large scale integration) hardware and used to produce corrective behavior in robot vehicles. Then, we present a robot model of auditory localization in the cricket; and discuss integration of this behavior with the optomotor behavior previously described. Finally we present a model of olfactory search in the moth, which makes use of several sensory cues, and has also been tested using robot hardware. We discuss some of the similarities and differences of the solutions obtained.     

Sensorimotor Learning Biases Choice Behavior: A Learning Neural Field Model for Decision Making

According to a prominent view of sensorimotor processing in primates, selection and specification of possible actions are not sequential operations. Rather, a decision for an action emerges from competition between different movement plans, which are specified and selected in parallel. For action choices which are based on ambiguous sensory input, the frontoparietal sensorimotor areas are considered part of the common underlying neural substrate for selection and specification of action. These areas have been shown capable of encoding alternative spatial motor goals in parallel during movement planning, and show signatures of competitive value-based selection among these goals. Since the same network is also involved in learning sensorimotor associations, competitive action selection (decision making) should not only be driven by the sensory evidence and expected reward in favor of either action, but also by the subject''s learning history of different sensorimotor associations. Previous computational models of competitive neural decision making used predefined associations between sensory input and corresponding motor output. Such hard-wiring does not allow modeling of how decisions are influenced by sensorimotor learning or by changing reward contingencies. We present a dynamic neural field model which learns arbitrary sensorimotor associations with a reward-driven Hebbian learning algorithm. We show that the model accurately simulates the dynamics of action selection with different reward contingencies, as observed in monkey cortical recordings, and that it correctly predicted the pattern of choice errors in a control experiment. With our adaptive model we demonstrate how network plasticity, which is required for association learning and adaptation to new reward contingencies, can influence choice behavior. The field model provides an integrated and dynamic account for the operations of sensorimotor integration, working memory and action selection required for decision making in ambiguous choice situations.     

Brain–computer interfaces for dissecting cognitive processes underlying sensorimotor control

Sensorimotor control engages cognitive processes such as prediction, learning, and multisensory integration. Understanding the neural mechanisms underlying these cognitive processes with arm reaching is challenging because we currently record only a fraction of the relevant neurons, the arm has nonlinear dynamics, and multiple modalities of sensory feedback contribute to control. A brain–computer interface (BCI) is a well-defined sensorimotor loop with key simplifying advantages that address each of these challenges, while engaging similar cognitive processes. As a result, BCI is becoming recognized as a powerful tool for basic scientific studies of sensorimotor control. Here, we describe the benefits of BCI for basic scientific inquiries and review recent BCI studies that have uncovered new insights into the neural mechanisms underlying sensorimotor control.     

Risk-Sensitivity in Bayesian Sensorimotor Integration

Information processing in the nervous system during sensorimotor tasks with inherent uncertainty has been shown to be consistent with Bayesian integration. Bayes optimal decision-makers are, however, risk-neutral in the sense that they weigh all possibilities based on prior expectation and sensory evidence when they choose the action with highest expected value. In contrast, risk-sensitive decision-makers are sensitive to model uncertainty and bias their decision-making processes when they do inference over unobserved variables. In particular, they allow deviations from their probabilistic model in cases where this model makes imprecise predictions. Here we test for risk-sensitivity in a sensorimotor integration task where subjects exhibit Bayesian information integration when they infer the position of a target from noisy sensory feedback. When introducing a cost associated with subjects'' response, we found that subjects exhibited a characteristic bias towards low cost responses when their uncertainty was high. This result is in accordance with risk-sensitive decision-making processes that allow for deviations from Bayes optimal decision-making in the face of uncertainty. Our results suggest that both Bayesian integration and risk-sensitivity are important factors to understand sensorimotor integration in a quantitative fashion.     

Lateral Preferences as Possible Phenotypic Predictors of the Reserves of the Cardiovascular System and the Features of Sensorimotor Integration in Climbers

The lateral preferences of the hands, the reserves of the cardiovascular system and the features of sensorimotor integration in mountain climbers as possible predictors of adaptation to extreme factors of the external environment have been investigated. The subjects were 15 climbers of high class (men aged 25.5–62.8 years). We used the following methods: reflexometric technique ReBFB (complex sensorimotor reaction in the model of go/no-go, computer simulation according to Chernikov); the assessment of lateral preferences using the standard samples; the orthostatic test (with the recording of the heart rate and evaluation of adaptive reserves based on the indices of mathematical analysis of the cardiac rhythm in the transition period according to a technique by Riftin). Sensorimotor integration was performed under normal conditions and under normobaric hypoxia. All indicators were correlated with the age and proficiency of the climbers. We observed no statistically significant correlations between age and the reserves of the cardiovascular system in the group of climbers. Reflexometry in normoxic conditions showed an improvement in the orientation of subjects in the sensory flow as compared with the initial level. The sensorimotor integration (as a response of sensorimotor responses) was more stable during hypoxia as compared with the same indices in normoxia. Noticeable correlation was observed between the results of the humeral test and the reserves of the cardiovascular system defined by orthostatic hypotension: the reserves were higher in the subjects with a higher level of left-hand preference. The regulation of the autonomic nervous system is correlated with left-handedness, which results in more effective adaptation to the high altitude in left-handers. The parameter of handedness can be used as a phenotypic predictor of the level of the cardiovascular system reserves in climbers. It is suggested that the factor of hypoxia with small exposure has a stimulating effect on sensorimotor integration in climbers.     

Attentional selection in visual perception,memory and action: a quest for cross-domain integration

For decades, the cognitive and neural sciences have benefitted greatly from a separation of mind and brain into distinct functional domains. The tremendous success of this approach notwithstanding, it is self-evident that such a view is incomplete. Goal-directed behaviour of an organism requires the joint functioning of perception, memory and sensorimotor control. A prime candidate for achieving integration across these functional domains are attentional processes. Consequently, this Theme Issue brings together studies of attentional selection from many fields, both experimental and theoretical, that are united in their quest to find overreaching integrative principles of attention between perception, memory and action. In all domains, attention is understood as combination of competition and priority control (‘bias’), with the task as a decisive driving factor to ensure coherent goal-directed behaviour and cognition. Using vision as the predominant model system for attentional selection, many studies of this Theme Issue focus special emphasis on eye movements as a selection process that is both a fundamental action and serves a key function in perception. The Theme Issue spans a wide range of methods, from measuring human behaviour in the real word to recordings of single neurons in the non-human primate brain. We firmly believe that combining such a breadth in approaches is necessary not only for attentional selection, but also to take the next decisive step in all of the cognitive and neural sciences: to understand cognition and behaviour beyond isolated domains.     

A biophysical approach to the Spatial Function of eye movements extraocular proprioception and the vestibulo-ocular reflex

The eyeball and the extraocular muscles are used as a paradigm to design a linear spatial model of a single joint with a redundant set of muscles. On the basis of this model relations are derived between orientation, torque, motor commands, and proprioceptive signals. These relations show that the tenet underlying the tensorial interpretation of neural signals in sensorimotor systems does not have general validity. A mechanism is proposed to show how proprioception may play a role in optimizing the coordination of muscles during spatial tasks. Further, a new concept is suggested that allows one to predict the neural connectivities mediating the redundant spatial vestibulo-ocular reflex. This concept has the advantage of minimizing both sensorial error and motor effort.     

Perceiving time to collision activates the sensorimotor cortex

The survival of many animals hinges upon their ability to avoid collisions with other animals or objects, or to precisely control the timing of collisions. Optical expansion provides a compelling impression of object approach and in principle can provide the basis for judgments of time to collision (TTC) [1]. It has been demonstrated that pigeons [2] and houseflies [3] have neural systems that can initiate rapid coordinated actions on the basis of optical expansion. In the case of humans, the linkage between judgments of TTC and coordinated action has not been established at a cortical level. Using functional magnetic resonance imaging (fMRI), we identified superior-parietal and motor-cortex areas that are selectively active during perceptual TTC judgments, some of which are normally involved in producing reach-to-grasp responses. These activations could not be attributed to actual movement of participants. We demonstrate that networks involved in the computational problem of extracting TTC from expansion information have close correspondence with the sensorimotor systems that would be involved in preparing a timed motor response, such as catching a ball or avoiding collision.     

A neural network model for the intersensory coordination involved in goal-directed movements

A neural network model for a sensorimotor system, which was developed to simulate oriented movements in man, is presented. It is composed of a formal neural network comprising two layers: a sensory layer receiving and processing sensory inputs, and a motor layer driving a simulated arm. The sensory layer is an extension of the topological network previously proposed by Kohonen (1984). Two kinds of sensory modality, proprioceptive and exteroceptive, are used to define the arm position. Each sensory cell receives proprioceptive inputs provided by each arm-joint together with the exteroceptive inputs. This sensory layer is therefore a kind of associative layer which integrates two separate sensory signals relating to movement coding. It is connected to the motor layer by means of adaptive synapses which provide a physical link between a motor activity and its sensory consequences. After a learning period, the spatial map which emerges in the sensory layer clearly depends on the sensory inputs and an associative map of both the arm and the extra-personal space is built up if proprioceptive and exteroceptive signals are processed together. The sensorimotor transformations occuring in the junctions linking the sensory and motor layers are organized in such a manner that the simulated arm becomes able to reach towards and track a target in extra-personal space. Proprioception serves to determine the final arm posture adopted and to correct the ongoing movement in cases where changes in the target location occur. With a view to developing a sensorimotor control system with more realistic salient features, a robotic model was coupled with the formal neural network. This robotic implementation of our model shows the capacity of formal neural networks to control the displacement of mechanical devices.     

From 1D to 2D via 3D: dynamics of surface motion segmentation for ocular tracking in primates.

In primates, tracking eye movements help vision by stabilising onto the retinas the images of a moving object of interest. This sensorimotor transformation involves several stages of motion processing, from the local measurement of one-dimensional luminance changes up to the integration of first and higher-order local motion cues into a global two-dimensional motion immune to antagonistic motions arising from the surrounding. The dynamics of this surface motion segmentation is reflected into the various components of the tracking responses and its underlying neural mechanisms can be correlated with behaviour at both single-cell and population levels. I review a series of behavioural studies which demonstrate that the neural representation driving eye movements evolves over time from a fast vector average of the outputs of linear and non-linear spatio-temporal filtering to a progressive and slower accurate solution for global motion. Because of the sensitivity of earliest ocular following to binocular disparity, antagonistic visual motion from surfaces located at different depths are filtered out. Thus, global motion integration is restricted within the depth plane of the object to be tracked. Similar dynamics were found at the level of monkey extra-striate areas MT and MST and I suggest that several parallel pathways along the motion stream are involved albeit with different latencies to build-up this accurate surface motion representation. After 200-300 ms, most of the computational problems of early motion processing (aperture problem, motion integration, motion segmentation) are solved and the eye velocity matches the global object velocity to maintain a clear and steady retinal image.     

The role of auditory feedback in vocal learning and maintenance

Auditory experience is critical for the acquisition and maintenance of learned vocalizations in both humans and songbirds. Despite the central role of auditory feedback in vocal learning and maintenance, where and how auditory feedback affects neural circuits important to vocal control remain poorly understood. Recent studies of singing birds have uncovered neural mechanisms by which feedback perturbations affect vocal plasticity and also have identified feedback-sensitive neurons at or near sites of auditory and vocal motor interaction. Additionally, recent studies in marmosets have underscored that even in the absence of vocal learning, vocalization remains flexible in the face of changing acoustical environments, pointing to rapid interactions between auditory and vocal motor systems. Finally, recent studies show that a juvenile songbird's initial auditory experience of a song model has long-lasting effects on sensorimotor neurons important to vocalization, shedding light on how auditory memories and feedback interact to guide vocal learning.     

Human umbilical cord blood cells restore brain damage induced changes in rat somatosensory cortex

Intraperitoneal transplantation of human umbilical cord blood (hUCB) cells has been shown to reduce sensorimotor deficits after hypoxic ischemic brain injury in neonatal rats. However, the neuronal correlate of the functional recovery and how such a treatment enforces plastic remodelling at the level of neural processing remains elusive. Here we show by in-vivo recordings that hUCB cells have the capability of ameliorating the injury-related impairment of neural processing in primary somatosensory cortex. Intact cortical processing depends on a delicate balance of inhibitory and excitatory transmission, which is disturbed after injury. We found that the dimensions of cortical maps and receptive fields, which are significantly altered after injury, were largely restored. Additionally, the lesion induced hyperexcitability was no longer observed in hUCB treated animals as indicated by a paired-pulse behaviour resembling that observed in control animals. The beneficial effects on cortical processing were reflected in an almost complete recovery of sensorimotor behaviour. Our results demonstrate that hUCB cells reinstall the way central neurons process information by normalizing inhibitory and excitatory processes. We propose that the intermediate level of cortical processing will become relevant as a new stage to investigate efficacy and mechanisms of cell therapy in the treatment of brain injury.     

Optimal isn’t good enough

The notion that biological systems come to embody optimal solutions seems consistent with the competitive drive of evolution. It has been used to interpret many examples of sensorimotor behavior. It is attractive from the viewpoint of control engineers because it solves the redundancy problem by identifying the one optimal motor strategy out of many similarly acceptable possibilities. This perspective examines whether there is sufficient basis to apply the formal engineering tools of optimal control to a reductionist understanding of biological systems. For an experimental biologist, this translates into whether the theory of optimal control generates nontrivial and testable hypotheses that accurately predict novel phenomena, ideally at deeper levels of structure than the observable behavior. The methodology of optimal control is applicable when there is (i) a single, known cost function to be optimized, (ii) an invertible model of the plant, and (iii) simple noise interfering with optimal performance. None of these is likely to be true for biological organisms. Furthermore, their motivation is usually good-enough rather than globally optimal behavior. Even then, the performance of a biological organism is often much farther from optimal than the physical limits of its hardware because the brain is continuously testing the acceptable limits of performance as well as just performing the task. This perspective considers an alternative strategy called ??good-enough?? control, in which the organism uses trial-and-error learning to acquire a repertoire of sensorimotor behaviors that are known to be useful, but not necessarily optimal. This leads to a diversity of solutions that tends to confer robustness on the individual organism and its evolution. It is also more consistent with the capabilities of higher sensorimotor structures, such as cerebral cortex, which seems to be designed to classify and recall complex sets of information, thereby allowing the organism to learn from experience, rather than to compute new strategies online. Optimal control has been a useful metaphor for understanding some superficial aspects of motor psychophysics. Reductionists who want to understand the underlying neural mechanisms need to move on.     

Generalization in Adaptation to Stable and Unstable Dynamics

Humans skillfully manipulate objects and tools despite the inherent instability. In order to succeed at these tasks, the sensorimotor control system must build an internal representation of both the force and mechanical impedance. As it is not practical to either learn or store motor commands for every possible future action, the sensorimotor control system generalizes a control strategy for a range of movements based on learning performed over a set of movements. Here, we introduce a computational model for this learning and generalization, which specifies how to learn feedforward muscle activity in a function of the state space. Specifically, by incorporating co-activation as a function of error into the feedback command, we are able to derive an algorithm from a gradient descent minimization of motion error and effort, subject to maintaining a stability margin. This algorithm can be used to learn to coordinate any of a variety of motor primitives such as force fields, muscle synergies, physical models or artificial neural networks. This model for human learning and generalization is able to adapt to both stable and unstable dynamics, and provides a controller for generating efficient adaptive motor behavior in robots. Simulation results exhibit predictions consistent with all experiments on learning of novel dynamics requiring adaptation of force and impedance, and enable us to re-examine some of the previous interpretations of experiments on generalization.     

Effect of motor deprivation in early ontogeny on evoked potentials of the sensomotor and visual cortex in the rat

The influence of long-term (3 months) locomotor deprivation of rats in a month age, on the evoked potentials (EP) of the sensorimotor and the visual cortex was studied in conditions of presentation of single and paired stimuli. Changes were revealed in both cortical zones. An increase of peak latency of the initial positive EP phase in the sensorimotor cortex, and prolongation of the process of changes in excitability of neural elements, elicited by conditioning stimulus, was revealed both in the sensorimotor and the visual areas. The effect of deprivation on the dynamics of changes in neuronal systems excitability was greater in the visual evoked responses.     

Initial patterning of the central nervous system: how many organizers?

For three-quarters of a century, developmental biologists have been asking how the nervous system is specified as distinct from the rest of the ectoderm during early development, and how it becomes subdivided initially into distinct regions such as forebrain, midbrain, hindbrain and spinal cord. The two events of 'neural induction' and 'early neural patterning' seem to be intertwined, and many models have been put forward to explain how these processes work at a molecular level. Here I consider early neural patterning and discuss the evidence for and against the two most popular models proposed for its explanation: the idea that multiple signalling centres (organizers) are responsible for inducing different regions of the nervous system, and a model first articulated by Nieuwkoop that invokes two steps (activation/transformation) necessary for neural patterning. As recent evidence from several systems challenges both models, I propose a modification of Nieuwkoop's model that most easily accommodates both classical and more recent data, and end by outlining some possible directions for future research.