Incremental embodied chaotic exploration of self-organized motor behaviors with proprioceptor adaptation

This paper presents a general and fully dynamic embodied artificial neural system, which incrementally explores and learns motor behaviors through an integrated combination of chaotic search and reflex learning. The former uses adaptive bifurcation to exploit the intrinsic chaotic dynamics arising from neuro-body-environment interactions, while the latter is based around proprioceptor adaptation. The overall iterative search process formed from this combination is shown to have a close relationship to evolutionary methods. The architecture developed here allows realtime goal-directed exploration and learning of the possible motor patterns (e.g., for locomotion) of embodied systems of arbitrary morphology. Examples of its successful application to a simple biomechanical model, a simulated swimming robot, and a simulated quadruped robot are given. The tractability of the biomechanical systems allows detailed analysis of the overall dynamics of the search process. This analysis sheds light on the strong parallels with evolutionary search

Stepping Responses to Treadmill Perturbations vary with Severity of Motor Deficits in Human SCI

In this study, we investigated the responses to tread perturbations during human stepping on a treadmill. Our approach was to test the effects of perturbations to a single leg using a split-belt treadmill in healthy participants and in participants with varying severity of spinal cord injury (SCI). We recruited 11 people with incomplete SCI and 5 noninjured participants. As participants walked on an instrumented treadmill, the belt on one side was stopped or accelerated briefly during mid to late stance. A majority of participants initiated an unnecessary swing when the treadmill was stopped in mid stance, although the likelihood of initiating a step was decreased in participants with more severe SCI. Accelerating or decelerating one belt of the treadmill during stance altered the characteristics of swing. We observed delayed swing initiation when the belt was decelerated (i.e. the hip was in a more flexed position at time of swing) and advanced swing initiation with acceleration (i.e. hip extended at swing initiation). Further, the timing and leg posture of heel strike appeared to remain constant, reflected by a sagittal plane hip angle at heel strike that remained the same regardless of the perturbation. In summary, our results supported the current understanding of the role of sensory feedback and central drive in the control of stepping in participants with incomplete SCI and noninjured participants. In particular, the observation of unnecessary swing during a stop perturbation highlights the interdependence of central and sensory drive in walking control

Remembering Forward: Neural Correlates of Memory and Prediction in Human Motor Adaptation

We used functional MR imaging (FMRI), a robotic manipulandum and systems identification techniques to examine neural correlates of predictive compensation for spring-like loads during goal-directed wrist movements in neurologically-intact humans. Although load changed unpredictably from one trial to the next, subjects nevertheless used sensorimotor memories from recent movements to predict and compensate upcoming loads. Prediction enabled subjects to adapt performance so that the task was accomplished with minimum effort. Population analyses of functional images revealed a distributed, bilateral network of cortical and subcortical activity supporting predictive load compensation during visual target capture. Cortical regions – including prefrontal, parietal and hippocampal cortices – exhibited trial-by-trial fluctuations in BOLD signal consistent with the storage and recall of sensorimotor memories or “states” important for spatial working memory. Bilateral activations in associative regions of the striatum demonstrated temporal correlation with the magnitude of kinematic performance error (a signal that could drive reward-optimizing reinforcement learning and the prospective scaling of previously learned motor programs). BOLD signal correlations with load prediction were observed in the cerebellar cortex and red nuclei (consistent with the idea that these structures generate adaptive fusimotor signals facilitating cancelation of expected proprioceptive feedback, as required for conditional feedback adjustments to ongoing motor commands and feedback error learning). Analysis of single subject images revealed that predictive activity was at least as likely to be observed in more than one of these neural systems as in just one. We conclude therefore that motor adaptation is mediated by predictive compensations supported by multiple, distributed, cortical and subcortical structures

Transcranial magnetic stimulation over sensorimotor cortex disrupts anticipatory reflex gain modulation for skilled action

Skilled interactions with new environments require flexible changes to the transformation from somatosensory signals to motor outputs. Transcortical reflex gains are known to be modulated according to task and environmental dynamics, but the mechanism of this modulation remains unclear. We examined reflex organization in the sensorimotor cortex. Subjects performed point- to- point arm movements into predictable force fields. When a small perturbation was applied just before the arm encountered the force field, reflex responses in the shoulder muscles changed according to the upcoming force field direction, indicating anticipatory reflex gain modulation. However, when a transcranial magnetic stimulation (TMS) was applied before the reflex response to such perturbations so that the silent period caused by TMS overlapped the reflex processing period, this modulation was abolished, while the reflex itself remained. Loss of reflex gain modulation could not be explained by reduced reflex amplitudes nor by peripheral effects of TMS on the muscles themselves. Instead, we suggest that TMS disrupted interneuronal networks in the sensorimotor cortex, which contribute to reflex gain modulation rather than reflex generation. We suggest that these networks normally provide the adaptability of rapid sensorimotor reflex responses by regulating reflex gains according to the current dynamical environment

Respiratory, postural and spatio-kinetic motor stabilization, internal models, top-down timed motor coordination and expanded cerebello-cerebral circuitry: a review

Human dexterity, bipedality, and song/speech vocalization in Homo are reviewed within a motor evolution perspective in regard to 

(i) brain expansion in cerebello-cerebral circuitry, 
(ii) enhanced predictive internal modeling of body kinematics, body kinetics and action organization, 
(iii) motor mastery due to prolonged practice, 
(iv) task-determined top-down, and accurately timed feedforward motor adjustment of multiple-body/artifact elements, and 
(v) reduction in automatic preflex/spinal reflex mechanisms that would otherwise restrict such top-down processes. 

Dual-task interference and developmental neuroimaging research argues that such internal modeling based motor capabilities are concomitant with the evolution of 
(vi) enhanced attentional, executive function and other high-level cognitive processes, and that 
(vii) these provide dexterity, bipedality and vocalization with effector nonspecific neural resources. 

The possibility is also raised that such neural resources could 
(viii) underlie human internal model based nonmotor cognitions. 
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New Trends in Neuromechanics and Motor Rehabilitation

Neuromechanics has been used to identify optimal rehabilitation protocols that successfully improve motor deficits in various populations, such as elderly people and individuals with neurological diseases (e.g., stroke, Parkinson’s disease, and essential tremor). By investigating structural and functional changes in the central and peripheral nervous systems based on neuromechanical theories and findings, we can expand our knowledge regarding underlying neurophysiological mechanisms and specific motor impairment patterns before and after therapies to further develop new training programs (e.g., non-invasive brain stimulation). Thus, the aim of this Special Issue is to present the main contributions of researchers and rehabilitation specialists in biomechanics, motor control, neurophysiology, neuroscience, and rehabilitation science. The current collection provides new neuromechanical approaches addressing theoretical, methodological, and practical topics for facilitating motor recovery progress

Benchmarking Cerebellar Control

Cerebellar models have long been advocated as viable models for robot dynamics control. Building on an increasing insight in and knowledge of the biological cerebellum, many models have been greatly refined, of which some computational models have emerged with useful properties with respect to robot dynamics control. Looking at the application side, however, there is a totally different picture. Not only is there not one robot on the market which uses anything remotely connected with cerebellar control, but even in research labs most testbeds for cerebellar models are restricted to toy problems. Such applications hardly ever exceed the complexity of a 2 DoF simulated robot arm; a task which is hardly representative for the field of robotics, or relates to realistic applications. In order to bring the amalgamation of the two fields forwards, we advocate the use of a set of robotics benchmarks, on which existing and new computational cerebellar models can be comparatively tested. It is clear that the traditional approach to solve robotics dynamics loses ground with the advancing complexity of robotic structures; there is a desire for adaptive methods which can compete as traditional control methods do for traditional robots. In this paper we try to lay down the successes and problems in the fields of cerebellar modelling as well as robot dynamics control. By analyzing the common ground, a set of benchmarks is suggested which may serve as typical robot applications for cerebellar models

Development of human locomotion

Neural control of locomotion in human adults involves the generation of a small set of basic patterned commands directed to the leg muscles. The commands are generated sequentially in time during each step by neural networks located in the spinal cord, called Central Pattern Generators. This review outlines recent advances in understanding how motor commands are expressed at different stages of human development. Similar commands are found in several other vertebrates, indicating that locomotion development follows common principles of organization of the control networks. Movements show a high degree of flexibility at all stages of development, which is instrumental for learning and exploration of variable interactions with the environment