The brain uses extrasomatic information to estimate limb displacement.

A fundamental problem faced by the brain is to estimate whether a touched object is rigidly attached to a ground reference or is movable. A simple solution to this problem would be for the brain to test whether pushing on the object with a limb is accompanied by limb displacement. The mere act of pushing excites large populations of mechanoreceptors, generating a sensory response that is only weakly sensitive to limb displacement if the movements are small, and thus can hardly be used to determine the mobility of the object. In the mechanical world, displacement or deformation of objects frequently co-occurs with microscopic fluctuations associated with the frictional sliding of surfaces in contact or with micro-failures inside an object. In this study,we provide compelling evidence that the brain relies on these microscopic mechanical events to estimate the displacement of the limb in contact with an object, and hence the mobility of the touched object. We show that when pressing with a finger on a stiff surface, fluctuations that resemble the mechanical response of granular solids provoke a sensation of limb displacement. Our findings suggest that when acting on an external object, prior knowledge about the sensory consequences of interacting with the object contributes to proprioception.

Comparison of interfinger connection matrix computation techniques.

A hypothesis was proposed that the central nervous system controls force production by the fingers through hypothetical neural commands. The neural commands are scaled between values of 0 to 1, indicating no intentional force production or maximal voluntary contraction (MVC) force production, respectively. A matrix of interfinger connections transforms neural commands into finger forces. Two methods have been proposed to compute the interfinger connection matrix. The first method uses only single finger MVC trials and multiplies the interfinger connection matrix by a gain factor. The second method uses a neural network model based on experimental data. The performance of the two methods was compared on the MVC data and on a data set of submaximal forces, collected over a range of total forces and moments of force. The methods were compared in terms of (1) ability to predict finger forces, (2) accuracy of neural command reconstruction, and (3) preserved planarity of force data for submaximal force production task. Both methods did a reasonable job of predicting the total force in multifinger MVC trials; however, the neural network model performed better in regards to all other criteria. Overall, the results indicate that for modeling multifinger interaction the neural network method is preferable.

Analytical and numerical analysis of inverse optimization problems: conditions of uniqueness and computational methods.

One of the key problems of motor control is the redundancy problem, in particular how the central nervous system (CNS) chooses an action out of infinitely many possible. A promising way to address this question is to assume that the choice is made based on optimization of a certain cost function. A number of cost functions have been proposed in the literature to explain performance in different motor tasks: from force sharing in grasping to path planning in walking. However, the problem of uniqueness of the cost function(s) was not addressed until recently. In this article, we analyze two methods of finding additive cost functions in inverse optimization problems with linear constraints, so-called linear-additive inverse optimization problems. These methods are based on the Uniqueness Theorem for inverse optimization problems that we proved recently (Terekhov et al., J Math Biol 61(3):423-453, 2010). Using synthetic data, we show that both methods allow for determining the cost function. We analyze the influence of noise on the both methods. Finally, we show how a violation of the conditions of the Uniqueness Theorem may lead to incorrect solutions of the inverse optimization problem.

An analytical approach to the problem of inverse optimization with additive objective functions: an application to human prehension.

We consider the problem of what is being optimized in human actions with respect to various aspects of human movements and different motor tasks. From the mathematical point of view this problem consists of finding an unknown objective function given the values at which it reaches its minimum. This problem is called the inverse optimization problem. Until now the main approach to this problems has been the cut-and-try method, which consists of introducing an objective function and checking how it reflects the experimental data. Using this approach, different objective functions have been proposed for the same motor action. In the current paper we focus on inverse optimization problems with additive objective functions and linear constraints. Such problems are typical in human movement science. The problem of muscle (or finger) force sharing is an example. For such problems we obtain sufficient conditions for uniqueness and propose a method for determining the objective functions. To illustrate our method we analyze the problem of force sharing among the fingers in a grasping task. We estimate the objective function from the experimental data and show that it can predict the force-sharing pattern for a vast range of external forces and torques applied to the grasped object. The resulting objective function is quadratic with essentially non-zero linear terms.

Why Early Tactile Speech Aids May Have Failed: No Perceptual Integration of Tactile and Auditory Signals.

Tactile speech aids, though extensively studied in the 1980's and 1990's, never became a commercial success. A hypothesis to explain this failure might be that it is difficult to obtain true perceptual integration of a tactile signal with information from auditory speech: exploitation of tactile cues from a tactile aid might require cognitive effort and so prevent speech understanding at the high rates typical of everyday speech. To test this hypothesis, we attempted to create true perceptual integration of tactile with auditory information in what might be considered the simplest situation encountered by a hearing-impaired listener. We created an auditory continuum between the syllables /BA/ and /VA/, and trained participants to associate /BA/ to one tactile stimulus and /VA/ to another tactile stimulus. After training, we tested if auditory discrimination along the continuum between the two syllables could be biased by incongruent tactile stimulation. We found that such a bias occurred only when the tactile stimulus was above, but not when it was below its previously measured tactile discrimination threshold. Such a pattern is compatible with the idea that the effect is due to a cognitive or decisional strategy, rather than to truly perceptual integration. We therefore ran a further study (Experiment 2), where we created a tactile version of the McGurk effect. We extensively trained two Subjects over 6 days to associate four recorded auditory syllables with four corresponding apparent motion tactile patterns. In a subsequent test, we presented stimulation that was either congruent or incongruent with the learnt association, and asked Subjects to report the syllable they perceived. We found no analog to the McGurk effect, suggesting that the tactile stimulation was not being perceptually integrated with the auditory syllable. These findings strengthen our hypothesis according to which tactile aids failed because integration of tactile cues with auditory speech occurred at a cognitive or decisional level, rather than truly at a perceptual level.

Spatio-temporal skin strain distributions evoke low variability spike responses in cuneate neurons.

A common method to explore the somatosensory function of the brain is to relate skin stimuli to neurophysiological recordings. However, interaction with the skin involves complex mechanical effects. Variability in mechanically induced spike responses is likely to be due in part to mechanical variability of the transformation of stimuli into spiking patterns in the primary sensors located in the skin. This source of variability greatly hampers detailed investigations of the response of the brain to different types of mechanical stimuli. A novel stimulation technique designed to minimize the uncertainty in the strain distributions induced in the skin was applied to evoke responses in single neurons in the cat. We show that exposure to specific spatio-temporal stimuli induced highly reproducible spike responses in the cells of the cuneate nucleus, which represents the first stage of integration of peripheral inputs to the brain. Using precisely controlled spatio-temporal stimuli, we also show that cuneate neurons, as a whole, were selectively sensitive to the spatial and to the temporal aspects of the stimuli. We conclude that the present skin stimulation technique based on localized differential tractions greatly reduces response variability that is exogenous to the information processing of the brain and hence paves the way for substantially more detailed investigations of the brain's somatosensory system.

Segregation of tactile input features in neurons of the cuneate nucleus.

Our tactile perception of external objects depends on skin-object interactions. The mechanics of contact dictates the existence of fundamental spatiotemporal input features-contact initiation and cessation, slip, and rolling contact-that originate from the fact that solid objects do not interpenetrate. However, it is unknown whether these features are represented within the brain. We used a novel haptic interface to deliver such inputs to the glabrous skin of finger/digit pads and recorded from neurons of the cuneate nucleus (the brain's first level of tactile processing) in the cat. Surprisingly, despite having similar receptive fields and response properties, each cuneate neuron responded to a unique combination of these inputs. Hence, distinct haptic input features are encoded already at subcortical processing stages. This organization maps skin-object interactions into rich representations provided to higher cortical levels and may call for a re-evaluation of our current understanding of the brain's somatosensory systems.

Optimization and variability of motor behavior in multifinger tasks: what variables does the brain use?

The neural control of movement has been described using different sets of elemental variables. Two possible sets of elemental variables have been suggested for finger pressing tasks: the forces of individual fingers and the finger commands (also called finger modes or central commands). The authors analyzed which of the 2 sets of the elemental variables is more likely used in the optimization of the finger force sharing and which set is used for the stabilization of performance. They used two recently developed techniques-the analytical inverse optimization (ANIO) and the uncontrolled manifold (UCM) analysis-to evaluate each set of elemental variables with respect to both aspects of performance. The results of the UCM analysis favored the finger commands as the elemental variables used for performance stabilization, while ANIO worked equally well on both sets of elemental variables. A simple scheme is suggested as to how the CNS could optimize a cost function dependent on the finger forces, but for the sake of facilitation of the feed forward control it substitutes the original cost function by a cost function, which is convenient to optimize in the space of finger commands.

Reconstruction of the unknown optimization cost functions from experimental recordings during static multi-finger prehension.

The goal of the research is to reconstruct the unknown cost (objective) function(s) presumably used by the neural controller for sharing the total force among individual fingers in multifinger prehension. The cost function was determined from experimental data by applying the recently developed Analytical Inverse Optimization (ANIO) method (Terekhov et al. 2010). The core of the ANIO method is the Theorem of Uniqueness that specifies conditions for unique (with some restrictions) estimation of the objective functions. In the experiment, subjects (n = 8) grasped an instrumented handle and maintained it at rest in the air with various external torques, loads, and target grasping forces applied to the object. The experimental data recorded from 80 trials showed a tendency to lie on a 2-dimensional hyperplane in the 4-dimensional finger-force space. Because the constraints in each trial were different, such a propensity is a manifestation of a neural mechanism (not the task mechanics). In agreement with the Lagrange principle for the inverse optimization, the plane of experimental observations was close to the plane resulting from the direct optimization. The latter plane was determined using the ANIO method. The unknown cost function was reconstructed successfully for each performer, as well as for the group data. The cost functions were found to be quadratic with nonzero linear terms. The cost functions obtained with the ANIO method yielded more accurate results than other optimization methods. The ANIO method has an evident potential for addressing the problem of optimization in motor control.

Minimal adhesion surface area in tangentially loaded digital contacts.

The stick-to-slip transition of a fingertip in contact with a planar surface does not occur instantaneously. As the tangential load increases, portions of the skin adhere while others slip, giving rise to an evolution of the contact state, termed partial slip. We develop a quasi-static model that predicts that if the coefficient of kinetic friction is larger than the coefficient of static friction, then the stuck surface area diminishes as the tangential load increases until reaching a 'minimal adhesion surface area' where it vanishes abruptly. This phenomenon was observed in recently measured finger-slip image data (André et al., 2011) that were processed by an optic flow detection algorithm. We examined the results of 10 trials. Four of them exhibited the minimal adhesion surface area phenomenon, four of them did not, and two were inconclusive.

Intersegmental coordination during human locomotion: does planar covariation of elevation angles reflect central constraints?

To study intersegmental coordination in humans performing different locomotor tasks (backward, normal, fast walking, and running), we analyzed the spatiotemporal patterns of both elevation and joint angles bilaterally in the sagittal plane. In particular, we determined the origins of the planar covariation of foot, shank, and thigh elevation angles. This planar constraint is observable in the three-dimensional space defined by these three angles and corresponds to the plane described by the three time-varying elevation angle variables over each step cycle. Previous studies showed that this relation between elevation angles constrains lower limb coordination in various experimental situations. We demonstrate here that this planar covariation mainly arises from the strong correlation between foot and shank elevation angles, with thigh angle independently contributing to the pattern of intersegmental covariation. We conclude that the planar covariation of elevation angles does not reflect central constraints, as previously suggested. An alternative approach for analyzing the patterns of coordination of both elevation and joint (hip, knee, and ankle) angles is used, based on temporal cross-correlation and phase relationships between pairs of kinematic variables. We describe the changes in the pattern of intersegmental coordination that are associated with the changes of locomotor modes and locomotor speeds. We provide some evidence for a distinct control of thigh motion and discuss the respective contributions of passive mechanical factors and of active (arising from neural control) factors to the formation and the regulation of the locomotor pattern throughout the gait cycle.