Complex predictive eye pursuit in monkey: a model system for cerebellar studies of skilled movement

Smooth pursuit eye movements provide a good model system for cerebellar studies of complex motor control in monkeys. First, the pursuit system exhibits predictive control along complex trajectories and this control improves with training. Second, the flocculus/paraflocculus region of the cerebellum appears to generate this control. Lesions impair pursuit and neural activity patterns are closely related to eye motion during complex pursuit. Importantly, neural responses lead eye motion during predictive pursuit and lag eye motion during non-predictable target motions that require visual control. The idea that flocculus/paraflocculus predictive control is non-visual is also supported by a lack of correlation between neural activity and retinal image motion during pursuit. Third, biologically accurate neural network models of the flocculus/paraflocculus allow the exploration and testing of pursuit mechanisms. Our current model can generate predictive control without visual input in a manner that is compatible with the extensive experimental data available for this cerebellar system. Similar types of non-visual cerebellar control are likely to facilitate the wide range of other skilled movements that are observed.     

Correlated evolution of brain regions involved in producing and processing facial expressions in anthropoid primates

Anthropoid primates are distinguished from other mammals by having relatively large primary visual cortices (V1) and complex facial expressions. We present a comparative test of the hypothesis that facial expression processing coevolved with the expansion of V1 in anthropoids. Previously published data were analysed using phylogenetic comparative methods. The results of our study suggest a pattern of correlated evolution linking social group size, facial motor control and cortical visual processing in catarrhines, but not platyrrhines. Catarrhines that live in relatively large social groups tended to have relatively large facial motor nuclei, and relatively large primary visual cortices. We conclude that catarrhine brains are adapted for producing and processing complex facial displays.     

Nonsomatotopic Organization of the Higher Motor Centers in Octopus

Hyperredundant limbs with a virtually unlimited number of degrees of freedom (DOFs) pose a challenge for both biological and computational systems of motor control. In the flexible arms of the octopus, simplification strategies have evolved to reduce the number of controlled DOFs [1], [2] and [3]. Motor control in the octopus nervous system is hierarchically organized [4] and [5]. A relatively small central brain integrates a huge amount of visual and tactile information from the large optic lobes and the peripheral nervous system of the arms [6], [7], [8] and [9] and issues commands to lower motor centers controlling the elaborated neuromuscular system of the arms. This unique organization raises new questions on the organization of the octopus brain and whether and how it represents the rich movement repertoire. We developed a method of brain microstimulation in freely behaving animals and stimulated the higher motor centers—the basal lobes—thus inducing discrete and complex sets of movements. As stimulation strength increased, complex movements were recruited from basic components shared by different types of movement. We found no stimulation site where movements of a single arm or body part could be elicited. Discrete and complex components have no central topographical organization but are distributed over wide regions.     

Visuomotor transformation in the fly gaze stabilization system

For sensory signals to control an animal's behavior, they must first be transformed into a format appropriate for use by its motor systems. This fundamental problem is faced by all animals, including humans. Beyond simple reflexes, little is known about how such sensorimotor transformations take place. Here we describe how the outputs of a well-characterized population of fly visual interneurons, lobula plate tangential cells (LPTCs), are used by the animal's gaze-stabilizing neck motor system. The LPTCs respond to visual input arising from both self-rotations and translations of the fly. The neck motor system however is involved in gaze stabilization and thus mainly controls compensatory head rotations. We investigated how the neck motor system is able to selectively extract rotation information from the mixed responses of the LPTCs. We recorded extracellularly from fly neck motor neurons (NMNs) and mapped the directional preferences across their extended visual receptive fields. Our results suggest that-like the tangential cells-NMNs are tuned to panoramic retinal image shifts, or optic flow fields, which occur when the fly rotates about particular body axes. In many cases, tangential cells and motor neurons appear to be tuned to similar axes of rotation, resulting in a correlation between the coordinate systems the two neural populations employ. However, in contrast to the primarily monocular receptive fields of the tangential cells, most NMNs are sensitive to visual motion presented to either eye. This results in the NMNs being more selective for rotation than the LPTCs. Thus, the neck motor system increases its rotation selectivity by a comparatively simple mechanism: the integration of binocular visual motion information.     

Functional evaluation and rehabilitation engineering

Life is complex and all about movement, which allows us to interact with the environment and communicate with each other. The human nervous system is capable of performing a simultaneous and integrated control of 100-150 mechanical degrees of freedom of movement in the body via tensions generated by about 700 muscles. In its widest context, movement is carried out by a sensory motor system comprising multiple sensors (visual,auditory, and proprioceptive),multiple actuators (muscles acting on the skeletal system),and an intermediary processor that can be summarized as a multiple-input–multiple-output nonlinear dynamic time-varying control system. This grand control system is capable of responding with remarkable accuracy,speed, appropriateness,versatility, and adaptability to a wide spectrum of continuous and discrete stimuli and conditions and is certainly orders of magnitude more complex and sophisticated than the most advanced robotic systems currently available. In the last decades,a great deal of research has been carried out in the fields of functional evaluation of human performance and rehabilitation engineering. These fields combine knowledge, concepts, and methods from across many disciplines (e.g., biomechanics,neuroscience, and physiology), with the aim of developing apparatuses and methods fort he measurement and analysis of complex sensory motor performance and the ultimate goal of enhancing the execution of different tasks in both healthy people and persons with reduced capabilities from different causes (injury, disease, amputation,and neural degeneration).     

Saccadic motor planning by integrating visual information and pre-information on neural dynamic fields

A functional model of target selection in the saccadic system is presented, incorporating elements of visual processing, motor planning, and motor control. We address the integration of visual information with pre-information. which is provided by manipulating the probability that a target appears at a certain location. This integration is achieved within a dynamic representation of planned eye movement which is modeled through distributions of activation on a topographic field. Visual input evokes activation, which is also constrained by lateral interaction within the field and by preshaping input representing pre-information. The model describes target selection observable in paradigms in which visual goals are presented at more than one location. Specifically, we model the transition from averaging, where endpoints of first saccades fall between two visual target locations, to decision making, where endpoints of first saccades fall accurately onto one of two simultaneously presented visual targets. We make predictions about how metrical biases of first saccades are induced by pre-information about target locations acquired by learning. When coupled to a motor control stage, activation dynamics on the planning level contribute to stabilizing gaze under fixation conditions. The neurophysiological relevance of our functional model is discussed.     

Flexible Switching of Feedback Control Mechanisms Allows for Learning of Different Task Dynamics

To produce skilled movements, the brain flexibly adapts to different task requirements and movement contexts. Two core abilities underlie this flexibility. First, depending on the task, the motor system must rapidly switch the way it produces motor commands and how it corrects movements online, i.e. it switches between different (feedback) control policies. Second, it must also adapt to environmental changes for different tasks separately. Here we show these two abilities are related. In a bimanual movement task, we show that participants can switch on a movement-by-movement basis between two feedback control policies, depending only on a static visual cue. When this cue indicates that the hands control separate objects, reactions to force field perturbations of each arm are purely unilateral. In contrast, when the visual cue indicates a commonly controlled object, reactions are shared across hands. Participants are also able to learn different force fields associated with a visual cue. This is however only the case when the visual cue is associated with different feedback control policies. These results indicate that when the motor system can flexibly switch between different control policies, it is also able to adapt separately to the dynamics of different environmental contexts. In contrast, visual cues that are not associated with different control policies are not effective for learning different task dynamics.     

Fixational Eye Movements in the Earliest Stage of Metazoan Evolution

All known photoreceptor cells adapt to constant light stimuli, fading the retinal image when exposed to an immobile visual scene. Counter strategies are therefore necessary to prevent blindness, and in mammals this is accomplished by fixational eye movements. Cubomedusae occupy a key position for understanding the evolution of complex visual systems and their eyes are assumedly subject to the same adaptive problems as the vertebrate eye, but lack motor control of their visual system. The morphology of the visual system of cubomedusae ensures a constant orientation of the eyes and a clear division of the visual field, but thereby also a constant retinal image when exposed to stationary visual scenes. Here we show that bell contractions used for swimming in the medusae refresh the retinal image in the upper lens eye of Tripedalia cystophora. This strongly suggests that strategies comparable to fixational eye movements have evolved at the earliest metazoan stage to compensate for the intrinsic property of the photoreceptors. Since the timing and amplitude of the rhopalial movements concur with the spatial and temporal resolution of the eye it circumvents the need for post processing in the central nervous system to remove image blur.     

A hierarchical neural-network model for control and learning of voluntary movement

In order to control voluntary movements, the central nervous system (CNS) must solve the following three computational problems at different levels: the determination of a desired trajectory in the visual coordinates, the transformation of its coordinates to the body coordinates and the generation of motor command. Based on physiological knowledge and previous models, we propose a hierarchical neural network model which accounts for the generation of motor command. In our model the association cortex provides the motor cortex with the desired trajectory in the body coordinates, where the motor command is then calculated by means of long-loop sensory feedback. Within the spinocerebellum — magnocellular red nucleus system, an internal neural model of the dynamics of the musculoskeletal system is acquired with practice, because of the heterosynaptic plasticity, while monitoring the motor command and the results of movement. Internal feedback control with this dynamical model updates the motor command by predicting a possible error of movement. Within the cerebrocerebellum — parvocellular red nucleus system, an internal neural model of the inverse-dynamics of the musculo-skeletal system is acquired while monitoring the desired trajectory and the motor command. The inverse-dynamics model substitutes for other brain regions in the complex computation of the motor command. The dynamics and the inverse-dynamics models are realized by a parallel distributed neural network, which comprises many sub-systems computing various nonlinear transformations of input signals and a neuron with heterosynaptic plasticity (that is, changes of synaptic weights are assumed proportional to a product of two kinds of synaptic inputs). Control and learning performance of the model was investigated by computer simulation, in which a robotic manipulator was used as a controlled system, with the following results: (1) Both the dynamics and the inverse-dynamics models were acquired during control of movements. (2) As motor learning proceeded, the inverse-dynamics model gradually took the place of external feedback as the main controller. Concomitantly, overall control performance became much better. (3) Once the neural network model learned to control some movement, it could control quite different and faster movements. (4) The neural netowrk model worked well even when only very limited information about the fundamental dynamical structure of the controlled system was available. Consequently, the model not only accounts for the learning and control capability of the CNS, but also provides a promising parallel-distributed control scheme for a large-scale complex object whose dynamics are only partially known.     

Octopus vulgaris uses visual information to determine the location of its arm

Octopuses are intelligent, soft-bodied animals with keen senses that perform reliably in a variety of visual and tactile learning tasks. However, researchers have found them disappointing in that they consistently fail in operant tasks that require them to combine central nervous system reward information with visual and peripheral knowledge of the location of their arms. Wells claimed that in order to filter and integrate an abundance of multisensory inputs that might inform the animal of the position of a single arm, octopuses would need an exceptional computing mechanism, and "There is no evidence that such a system exists in Octopus, or in any other soft bodied animal." Recent electrophysiological experiments, which found no clear somatotopic organization in the higher motor centers, support this claim. We developed a three-choice maze that required an octopus to use a single arm to reach a visually marked goal compartment. Using this operant task, we show for the first time that Octopus vulgaris is capable of guiding a single arm in a complex movement to a location. Thus, we claim that octopuses can combine peripheral arm location information with visual input to control goal-directed complex movements.     

The role of motor learning in spatial adaptation near a tool

Some visual-tactile (bimodal) cells have visual receptive fields (vRFs) that overlap and extend moderately beyond the skin of the hand. Neurophysiological evidence suggests, however, that a vRF will grow to encompass a hand-held tool following active tool use but not after passive holding. Why does active tool use, and not passive holding, lead to spatial adaptation near a tool? We asked whether spatial adaptation could be the result of motor or visual experience with the tool, and we distinguished between these alternatives by isolating motor from visual experience with the tool. Participants learned to use a novel, weighted tool. The active training group received both motor and visual experience with the tool, the passive training group received visual experience with the tool, but no motor experience, and finally, a no-training control group received neither visual nor motor experience using the tool. After training, we used a cueing paradigm to measure how quickly participants detected targets, varying whether the tool was placed near or far from the target display. Only the active training group detected targets more quickly when the tool was placed near, rather than far, from the target display. This effect of tool location was not present for either the passive-training or control groups. These results suggest that motor learning influences how visual space around the tool is represented.     

A human motor control perspective to multiple manipulator modelling

This paper describes the aspects involved in modelling a multi-robot system from a human motor control perspective. The human motor control system has a hierarchical and decentralised structure, and building a control system for a multi-robot system that attains human features would require a decomposable model. Decomposition of a complex robotic system is difficult due to the interactions between the subsystems, so these have to be first separated before the system is modelled. The proposed method of separating the interconnections is applied with the aid of fuzzy modelling to derive a fully decomposable model of two manipulator robots handling a common object.     

Cortical control of grasp in non-human primates

The skilled use of the hand for grasping and manipulation of objects is a fundamental feature of the primate motor system. Grasping movements involve transforming the visual information about an object into a motor command appropriate for the coordinated activation of hand and finger muscles. The cerebral cortex and its descending projections to the spinal cord are known to play a crucial role for the control of grasp. Recent studies in non-human primates have provided some striking new insights into the respective contribution of the parietal and frontal motor cortical areas to the control of grasp. Also, new approaches allowed investigating the coupling of grasp-related activity in different cortical areas for the control of the descending motor command.     

Manuo-ocular coordination in target tracking. II. Comparing the model with human behavior

Several studies have shown that humans track a moving visual target with their eyes better if the movement of this target is directly controlled by the observer's hand. The improvement in performance has been attributed to coordination control between the arm motor system and the smooth pursuit (SP) system. In such a task, the SP system shows characteristics that differ from those observed during eye-alone tracking: latency (between the target-arm and the eye motion onsets) is shorter, maximum SP velocity is higher and the maximum target motion frequency at which the SP can function effectively is also higher. The aim of this article is to qualitatively evaluate the behavior of a dynamical model simulating the oculomotor system and the arm motor system when both are involved in tracking visual targets. The evaluation is essentially based on a comparison of the behavior of the model with the behavior of human subjects tracking visual targets under different conditions. The model has been introduced and quantitatively evaluated in a companion paper. The model is based on an exchange of internal information between the two sensorimotor systems, mediated by sensory signals (vision, arm muscle proprioception) and motor signals (arm motor command copy). The exchange is achieved by a specialized structure of the central nervous system, previously identified as a part of the cerebellum. Computer simulation of the model yielded results that fit the behavior of human subjects observed during previously reported experiments, both qualitatively and quantitatively. The parallelism between physiology and human behavior on the one hand, and structure and simulation of the model on the other hand, is discussed. Received: 6 March 1997 / Accepted in revised form: 15 July 1997     

Analogous mechanisms compensate for neural delays in the sensory and the motor pathways: evidence from motor flash-lag

Motor behaviors require animals to coordinate neural activity across different areas within their motor system. In particular, the significant processing delays within the motor system must somehow be compensated for. Internal models of the motor system, in particular the forward model, have emerged as important potential mechanisms for compensation. For motor responses directed at moving visual objects, there is, additionally, a problem of delays within the sensory pathways carrying crucial position information. The visual phenomenon known as the flash-lag effect has led to a motion-extrapolation model for compensation of sensory delays. In the flash-lag effect, observers see a flashed item colocalized with a moving item as lagging behind the moving item. Here, we explore the possibility that the internal forward model and the motion-extrapolation model are analogous mechanisms compensating for neural delays in the motor and the visual system, respectively. In total darkness, observers moved their right hand gripping a rod while a visual flash was presented at various positions in relation to the rod. When the flash was aligned with the rod, observers perceived it in a position lagging behind the instantaneous felt position of the invisible rod. These results suggest that compensation of neural delays for time-varying motor behavior parallels compensation of delays for time-varying visual stimulation.     

Multivariate EEG spectral analysis evidences the functional link between motor and visual cortex during integrative sensorimotor tasks

The identification of the networks connecting brain areas and the understanding of their role in executing complex tasks is a crucial issue in cognitive neuroscience. In this study, specific visuomotor tasks were devised to reveal the functional network underlying the cooperation process between visual and motor regions. Electroencephalography (EEG) data were recorded from twelve healthy subjects during a combined visuomotor task, which integrated precise grip motor commands with sensory visual feedback (VM). This condition was compared with control tasks involving pure motor action (M), pure visual perception (V) and visuomotor performance without feedback (V + M). Multivariate parametric cross-spectral analysis was applied to ten EEG derivations in each subject to assess changes in the oscillatory activity of the involved cortical regions and quantify their coupling. Spectral decomposition was applied to precisely and objectively determine the power associated with each oscillatory component of the spectrum, while surrogate data analysis was performed to assess the statistical significance of estimated coherence values. A significant decrease of the alpha and/or beta power in EEG spectra with respect to rest values was assumed as indicative of specific cortical area activation during task execution. Indeed alpha band coherence increased in proximity of task-involved areas, while it was suppressed or remained unchanged in other regions, suggesting the activation of a specific network for each task. According to our coherence analysis, a direct link between visual and motor areas was activated during V + M and VM tasks. The effect of visual feedback was evident in the beta band, where the increase of coherence was observed only during the VM task. Multivariate analysis suggested the presence of a functional link between motor and visual cortex subserving sensorimotor integration. Furthermore, network activation was related to the sum of single task (M and V) local effects in the alpha band, and to the presence of visual feedback in the beta band.     

Spatiotopic visual maps revealed by saccadic adaptation in humans

Saccadic adaptation [1] is a powerful experimental paradigm to probe the mechanisms of eye movement control and spatial vision, in which saccadic amplitudes change in response to false visual feedback. The adaptation occurs primarily in the motor system [2, 3], but there is also evidence for visual adaptation, depending on the size and the permanence of the postsaccadic error [4-7]. Here we confirm that adaptation has a strong visual component and show that the visual component of the adaptation is spatially selective in external, not retinal coordinates. Subjects performed?a memory-guided, double-saccade, outward-adaptation task designed to maximize visual adaptation and to dissociate the visual and motor corrections. When the memorized saccadic target was in the same position (in external space) as that used in the adaptation training, saccade targeting was strongly influenced by adaptation (even if not matched in retinal or cranial position), but when in the same retinal or cranial but different external spatial position, targeting was unaffected by adaptation, demonstrating unequivocal spatiotopic selectivity. These results point to the existence of a spatiotopic neural representation for eye movement control that adapts in response to saccade error signals.     

Effect of Visual Illusions of the Formation of Virtual Action Space

Interactions between visual and motor spaces were studied using tracking movements in a virtual action space paradigm. Disturbances of the regular structure of the visual space was done using computer modeling of the Hering visual illusion. The distribution of the cinematic characteristics of tracking movements in the coordinate system, associated with the target of the motor task, revealed the dominance of topological characteristics over metric characteristics during the formation of the motor field. Factors producing the appropriate topology were determined. It was found that motor space is more susceptible to these factors than visual field.     

Control of different electromyogram levels of M. abductor pollicis brevis by means of visual feedback in healthy subjects

We studied voluntary control of integrated electromyogram (IEMG) in the range of 20 +/- 5% and 40 +/- 5% of the IEMG of m. abductor pollicis brevis during its maximum voluntary contraction with and without visual feedback. Healthy subjects performed IEMG control with visual feedback in 5 trials; IEMG control with visual feedback in 5 trials for 5 days, and the reproduction of memorized IEMG value without visual feedback after 5 trials of IEMG under the visual control. The accuracy of IEMG control was estimated by the following parameters: time of IEMG being out of the required 10% range (ERROR); IEMG variability (VARIABILITY), and the bias of IEMG mean level (BIAS) during 30-sec trials. The IEMG control in the range of 20 +/- 5% with visual feedback improved in all subjects over the course of 5 trials. Within 5-day training, ERROR and VARIABILITY reduced on the first day only; during the last 4 days there was no accuracy increase. ERROR increased more than twice when the 20% IEMG level was reproduced without vision. The IEMG control in the range of 40 +/- 5% improved neither during 5 trials, nor during 5 days of training with visual feedback. ERROR increased for about 1.5 times when the 40% IEMG level was reproduced without vision. It was concluded that the motor system, particularly the motor cortex, could control the given level of muscle activity using the visual feedback.     

Evolution of the central complex in the arthropod brain with respect to the visual system

Modular midline neuropils, termed arcuate body (Chelicerata, Onychophora) or central body (Myriapoda, Crustacea, Insecta), are a prominent feature of the arthropod brain. In insects and crayfish, the central body is connected to a second midline-spanning neuropil, the protocerebral bridge. Both structures are collectively termed central complex. While some investigators have assumed that central and arcuate bodies are homologous, others have questioned this view. Stimulated by recent evidence for a role of the central complex in polarization vision and object recognition, the architectures of midline neuropils and their associations with the visual system were compared across panarthropods. In chelicerates and onychophorans, second-order neuropils subserving the median eyes are associated with the arcuate body. The central complex of decapods and insects, instead, receives indirect input from the lateral (compound) eye visual system, and connections with median eye (ocellar) projections are present. Together with other characters these data are consistent with a common origin of arcuate bodies and central complexes from an ancestral modular midline neuropil but, depending on the choice of characters, the protocerebral bridge or the central body shows closer affinity with the arcuate body. A possible common role of midline neuropils in azimuth-dependent sensory and motor tasks is discussed.