A Synergistic Behavior Underpins Human Hand Grasping Force Control During Environmental Constraint Exploitation

Despite the complex nature of human hands, neuroscientific studies suggested a simplified kinematic control underpinning motion generation, resulting in principal joint angle co-variation patterns, usually called postural hand synergies. Such a low dimensional description was observed in common grasping tasks, and was proven to be preserved also for grasps performed by exploiting the external environment (e.g., picking up a key by sliding it on a table). In this paper, we extend this analysis to the force domain. To do so, we performed experiments with six subjects, who were asked to grasp objects from a flat surface while force/torque measures were acquired at fingertip level through wearable sensors. The set of objects was chosen so that participants were forced to interact with the table to achieve a successful grasp. Principal component analysis was applied to force measurements to investigate the existence of co-variation schemes, i.e. a synergistic behavior. Results show that one principal component explains most of the hand force distribution. Applications to clinical assessment and robotic sensing are finally discussed

Astrocyte function from information processing to cognition and cognitive impairment.

Astrocytes serve important roles that affect recruitment and function of neurons at the local and network levels. Here we review the contributions of astrocyte signaling to synaptic plasticity, neuronal network oscillations, and memory function. The roles played by astrocytes are not fully understood, but astrocytes seem to contribute to memory consolidation and seem to mediate the effects of vigilance and arousal on memory performance. Understanding the role of astrocytes in cognitive processes may also advance our understanding of how these processes go awry in pathological conditions. Indeed, abnormal astrocytic signaling can cause or contribute to synaptic and network imbalances, leading to cognitive impairment. We discuss evidence for this from animal models of Alzheimer's disease and multiple sclerosis and from animal studies of sleep deprivation and drug abuse and addiction. Understanding the emerging roles of astrocytes in cognitive function and dysfunction will open up a large array of new therapeutic opportunities

Neural representations of sensorimotor memory- and digit position-based load force adjustments before the onset of dexterous object manipulation

Anticipatory load forces for dexterous object manipulation in humans are modulated based on visual object properties cues, sensorimotor memories of previous experiences with the object, and, when digit positioning varies from trial-to-trial, the integrating of this sensed variability with force modulation. Studies of the neural representations encoding these anticipatory mechanisms have not considered these mechanisms separately from each other or from feedback mechanisms emerging after lift onset. Representational similarity analyses of fMRI data were used to identify neural representations of sensorimotor memories and the sensing and integration of digit position. Cortical activity and movement kinematics were measured as 20 human subjects (11 women) minimized tilt of a symmetrically-shaped object with a concealed asymmetric center of mass (CoM, left- and right-sided) - this required generating compensatory torques in opposite directions which, without helpful visual CoM cues, relied primarily on sensorimotor memories of the same object and CoM. Digit position was constrained or unconstrained, the latter of which required modulating forces beyond what can be recalled from sensorimotor memories to compensate for digit position variability. Ventral premotor (PMv), somatosensory, and cerebellar lobule regions (CrusII, VIIIa) were sensitive to anticipatory behaviors that reflect sensorimotor memory content, as shown by larger voxel pattern differences for unmatched than matched CoM conditions. Cerebellar lobule I-IV, Broca area 44 and PMv showed greater voxel pattern differences for unconstrained than constrained grasping, which suggests their sensitivity to monitor the online coincidence of planned and actual digit positions and correct for a mismatch by force modulation.Michelle Marneweck, Deborah A. Barany, Marco Santello and Scott T. Grafto

A new method for extracting conodonts and radiolarians from chert with NaOH solution

Microfossils are important components of sedi- mentary rocks used for palaeontological, biostratigraphic, palaeoenvironmental and palaeoclimatic investigations. They are usually extracted from rocks using an acid solution, which might vary depending on the embedding rock lithology. Here we propose a new method using common NaOH (sodium hydroxide; soda) to digest cherts (micro- and cryptocrystalline quartz) instead of the standard technique based on HF (hydrofluoric acid). This new method allows the collection of undamaged specimens of different kinds of microfossils, such as conodonts, radiolarians, teeth and dermal scales, the miner- ology of which is still preserved (e.g. biogenic apatite in cono- donts). The use of soda is thus recommended, as it is less dangerous, less expensive, and it better preserves the extracted microfossils both in shape and mineralogy

The human central nervous system transmits common synaptic inputs to distinct motor neuron pools during non-synergistic digit actions

KEY POINTS: Neural connectivity between distinct motor neuronal modules in the spinal cord is classically studied through electrical stimulation or multi-muscle EMG recordings. We quantified the strength of correlation in the activity of two distinct populations of motor neurons innervating the thenar and first dorsal interosseous muscles during tasks that required the two hand muscles to exert matched or un-matched forces in different directions. We show that when the two hand muscles are concurrently activated, synaptic input to the two motor neuron pools is shared across all frequency bandwidths (representing cortical and spinal input) associated with force control. The observed connectivity indicates that motor neuron pools receive common input even when digit actions do not belong to a common behavioural repertoire. ABSTRACT: Neural connectivity between distinct motor neuronal modules in the spinal cord is classically studied through electrical stimulation or multi-muscle EMG recordings. Here we quantify the strength of correlation in the activity of two distinct populations of motor neurons innervating the thenar and first dorsal interosseous muscles in humans during voluntary contractions. To remove confounds associated with previous studies, we used a task that required the two hand muscles to exert matched or un-matched forces in different directions. Despite the force production task consisting of uncommon digit force coordination patterns, we found that synaptic input to motor neurons is shared across all frequency bands, reflecting cortical and spinal inputs associated with force control. The coherence between discharge timings of the two pools of motor neurons was significant at the delta (0-5 Hz), alpha (5-15 Hz) and beta (15-35 Hz) bands (P < 0.05). These results suggest that correlated input to motor neurons of two hand muscles can occur even during tasks not belonging to a common behavioural repertoire and despite lack of common innervation. Moreover, we show that the extraction of activity from motor neurons during voluntary force control removes cross-talk associated with global EMG recordings, thus allowing direct in vivo interrogation of spinal motor neuron activity

Haptic SLAM: An Ideal Observer Model for Bayesian Inference of Object Shape and Hand Pose from Contact Dynamics

Dynamic tactile exploration enables humans to seamlessly estimate the shape of objects and distinguish them from one another in the complete absence of visual information. Such a blind tactile exploration allows integrating information of the hand pose and contacts on the skin to form a coherent representation of the object shape. A principled way to understand the underlying neural computations of human haptic perception is through normative modelling. We propose a Bayesian perceptual model for recursive integration of noisy proprioceptive hand pose with noisy skin–object contacts. The model simultaneously forms an optimal estimate of the true hand pose and a representation of the explored shape in an object–centred coordinate system. A classification algorithm can, thus, be applied in order to distinguish among different objects solely based on the similarity of their representations. This enables the comparison, in real–time, of the shape of an object identified by human subjects with the shape of the same object predicted by our model using motion capture data. Therefore, our work provides a framework for a principled study of human haptic exploration of complex objects

A low-dimensional representation of arm movements and hand grip forces in post-stroke individuals

Characterizing post-stroke impairments in the sensorimotor control of arm and hand is essential to better understand altered mechanisms of movement generation. Herein, we used a decomposition algorithm to characterize impairments in end-effector velocity and hand grip force data collected from an instrumented functional task in 83 healthy control and 27 chronic post-stroke individuals with mild-to-moderate impairments. According to kinematic and kinetic raw data, post-stroke individuals showed reduced functional performance during all task phases. After applying the decomposition algorithm, we observed that the behavioural data from healthy controls relies on a low-dimensional representation and demonstrated that this representation is mostly preserved post-stroke. Further, it emerged that reduced functional performance post-stroke correlates to an abnormal variance distribution of the behavioural representation, except when reducing hand grip forces. This suggests that the behavioural repertoire in these post-stroke individuals is mostly preserved, thereby pointing towards therapeutic strategies that optimize movement quality and the reduction of grip forces to improve performance of daily life activities post-stroke

Robots that can adapt like animals

As robots leave the controlled environments of factories to autonomously function in more complex, natural environments, they will have to respond to the inevitable fact that they will become damaged. However, while animals can quickly adapt to a wide variety of injuries, current robots cannot "think outside the box" to find a compensatory behavior when damaged: they are limited to their pre-specified self-sensing abilities, can diagnose only anticipated failure modes, and require a pre-programmed contingency plan for every type of potential damage, an impracticality for complex robots. Here we introduce an intelligent trial and error algorithm that allows robots to adapt to damage in less than two minutes, without requiring self-diagnosis or pre-specified contingency plans. Before deployment, a robot exploits a novel algorithm to create a detailed map of the space of high-performing behaviors: This map represents the robot's intuitions about what behaviors it can perform and their value. If the robot is damaged, it uses these intuitions to guide a trial-and-error learning algorithm that conducts intelligent experiments to rapidly discover a compensatory behavior that works in spite of the damage. Experiments reveal successful adaptations for a legged robot injured in five different ways, including damaged, broken, and missing legs, and for a robotic arm with joints broken in 14 different ways. This new technique will enable more robust, effective, autonomous robots, and suggests principles that animals may use to adapt to injury

Reducing Versatile Bat Wing Conformations to a 1-DoF Machine

Recent works have shown success in mimicking the flapping flight of bats on the robotic platform Bat Bot (B2). This robot has only five actuators but retains the ability to flap and fold-unfold its wings in flight. However, this bat-like robot has been unable to perform folding-unfolding of its wings within the period of a wingbeat cycle, about 100 ms. The DC motors operating the spindle mechanisms cannot attain this folding speed. Biological bats rely on this periodic folding of their wings during the upstroke of the wingbeat cycle. It reduces the moment of inertia of the wings and limits the negative lift generated during the upstroke. Thus, we consider it important to achieve wing folding during the upstroke. A mechanism was designed to couple the flapping cycle to the folding cycle of the robot. We then use biological data to further optimize the mechanism such that the kinematic synergies of the robot best match those of a biological bat. This ensures that folding is performed at the correct point in the wingbeat cycle