Neural Control of Movement

Industrial control systems are nowadays exposed in environments with rapid and unstable parameter changes and uses measuring equipments with critical output sensitivity. In the case of thermal gas analyzer, measurement errors are contributed by temperature, gas flow, and pressure. Error compensation is a key problem for these control systems. In recent years, it has been proven in the literature that artificial neural network (ANN) is a reliable and low cost solution to manage errors. Among all the algorithms of ANN, the back propagation is commonly used because of its simplicity and learning methodology is easy to realize. However, it has two notable drawbacks: (a) it is likely to run into local minimum, and (b) convergence is slow. Thermal conductivity gas analyzer often works in adverse surroundings, which requires fast and accurate measurements. Therefore, a strong learning network is needed. This paper proposes a novel thermal gas analyzer using adaptive neuro-fuzzy inference system. The effectiveness and validity of the proposed method is verified by simulation studies using MATLAB. Fuzzy membership rules are created to allow regulation of learning parameters. Further, the fuzzy adaptive network model is constructed to train large data samples while the high precision compensation of sensor error is realized by the improved flow. Simulation results reveal that the convergence speed and output accuracy is improved and the learning parameters in thermal gas analyzer are automatically corrected by the proposed method in comparison with the back propagation algorithm of artificial neural network.

Считается, что ключевую роль в соматических практиках играет осознавание движений. Однако, в выразительной двигательной импровизации сознательный контроль движений является сбивающим фактором.

Опыт же показывает, что соматические практики развивают способность к импровизации.

Теория Бернштейна даёт ключ к разрешению этого противоречия, если рассмотреть движения с ведущим уровнем смыслов E.

DNNs (Deep Neural Networks) are widely employed in advanced applications including image and audio processing. Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are two types of DNNs that have been popular for industrial applications in recent years. RNNs are well-suited to time variation problems due to their recursive structure. RNNs are ideally suited for temporal variation concerns due to their recursive structure, whereas CNNs are often employed in computer vision applications such as object recognition. Despite the fact that CNNs and RNNs are both DNNs, their implementation differs significantly. Recurrent Neural Networks (RNN) can be used to solve the sequence to sequence problem when both the input and output have sequential structures. There are always some unseen links between the structures. The traditional RNN technique, on the other hand, has trouble examining the links between the sequences appropriately. This survey introduces some attentionbased RNN models, applications, types, and DNN algorithms that can focus on different features of the input for each output item in order to investigate and exploit the implicit links between the input and output items.

It is well known that the classic image compression techniques such as JPEG and MPEG have serious limitations at high compression rate, the decompressed image gets really fuzzy or indistinguishable. To overcome this problem, artificial neural networks ANNs techniques are used. This paper presents a neural network based technique that may be applied to data compression. This paper breaks down large images into smaller windows and eliminates redundant information. Finally, the technique uses a neural network trained by direct solution methods. Conventional techniques such as Huffman coding and the Shannon Fano method, LZ Method, Run Length Method, LZ-77 are discussed as well as more recent methods for the compression of data presents a neural network based technique that
may be applied to data compression. The proposed technique and images. Intelligent methods for data compression are reviewed including the use of Back propagation and Kohonen neural networks. The proposed method includes steps to break down large images into smaller windows for Lossless image compression/ decompression processes. Results obtained with proposed technique leads to better compression ratio at the same time preserving the image
quality.

The existing choices of Activation Functions of a deep learning neural network are majorly based on personal human judgments, biases, experiences and little quantitative skills, thus, neither generated from the training data, testing data nor emanated from the referenced AI-MI-Purified Data Set. In my previous paper, Jameel's ANNAF Stochastic Criterion and Lemma for selecting stochastic activation functions were proposed, however, the objective of this paper is to propose Definite Rules, not Trial and Error called "Jameel's ANNAF Deterministic Criterion and Lemma" for the choice of advanced optimized Activation Functions. This is the only paper that first applied "proposed Jameel's ANNAF Deterministic Criterion" to proposed about Two-Thousand Two-Hundred and Twenty Four, 2224 Advanced Activation Functions (mostly Deterministic) EMANATED from our AI SAMPLE DATA for the successful conduct of Deep Learning Artificial Neural Network. THREE out of which were Rated excellent Activation Functions for "Temperature vs Conductance" Deep Learning Artificial Neural Network. However, one can still find more candidates out of the remaining 2221 Activation Functions using the proposed criterion. The bottom line is that Advanced Deep Learning Artificial Neural Networks would depend on AI DATA, TIME CHANGE and the AREA OF APPLICATION.

In this paper we propose a kernel adaptive filtering (KAF) approach to repair lesions via microstimulation in a biomimetic spiking neural network of sensorimotor cortex. The fundamental challenge of designing neuroprosthetics and brain machine interfaces (BMIs) is the decoding of electrical activity of neurons and behavior. For injured or damaged brain, intracranial stimulation has the potential to modulate neural activity to match meaningful and natural response or behavior. In order to estimate the optimal microstimulation sequences, we construct an inverse model of the target system. However, to obtain sufficient data to learn the inverse, the neural system much be stimulated or probed repeatedly. For real brains, this is especially challenging and often unfeasible. Here, we demonstrate that by applying KAF to a biomimetic brain and realistic virtual musculoskeletal model, we are able to repair simulated lesion and drive the virtual arm to perform the correct motor task.

Resumen:
Introducción. El creciente interés por los déficit cognitivos que presentan los pacientes con enfermedad de Parkinson ha dado lugar a la proliferación de trabajos sobre este tema durante los últimos años. En el presente artículo se revisa la alteración visuoespacial en dichos pacientes, que habitualmente se relaciona con otros trastornos como los que afectan a la planificación, la secuenciación, la atención o los procesos de la memoria. Desarrollo. Se exponen los hallazgos más relevantes que apuntan a que la existencia de esas
alteraciones visuoespaciales que muestran los pacientes con enfermedad de Parkinson no son siempre secundarias a otros déficit cognitivos de tipo frontal, como se ha afirmado en los últimos años, sino que pueden deberse a trastornos en otros puntos anteriores del proceso perceptivo (como consecuencia de la deficiencia dopaminérgica en el circuito basal-talámico-cortical). Para ello, se clasifican los trastornos visuoperceptivos en función de su localización cerebral: desde la retina hasta el núcleo geniculado lateral, la corteza visual y la corteza extraestriada, y, finalmente, la corteza frontal y prefrontal. Conclusiones. Se propone la presente clasificación de los trastornos según la localización de los mismos en aras de una selección objetiva de la muestra y de las pruebas neuropsicológicas utilizadas en los estudios. Es deseable, a este respecto, un mayor consenso entre los investigadores a la hora de diseñar las investigaciones que versen sobre alteraciones visuoespaciales en los pacientes con enfermedad de Parkinson.
Palabras clave:
Corteza visual. Enfermedad de Parkinson. Neuropsicología.
Percepción visual. Trastornos cognitivos. Trastornos
perceptivos.

Abstract:
Introduction. The growing interest in the cognitive impairment shown by patients with Parkinson’s disease has led to a wealth of research in this line over recent years. In this paper we review the visuospatial alterations in these patients, which are usually linked to other disorders such as those affecting planning, sequencing, attention or mnemonic processes. Development. We report the most relevant findings, which suggest that the existence of these visuospatial disorders shown by patients with Parkinson’s disease are not always secondary to other frontal-type cognitive impairments, as has been claimed in recent years. Instead, they may be due to disorders in other anterior points of the perceptive process (as a result of dopaminergic deficits in basal-thalamic-cortical circuits). Thus, visuoperceptual disorders are classified according to their location in the brain: from the retina to the lateral geniculate nucleus, the visual cortex and the extrastriate cortex and, finally, the frontal and prefrontal cortex. Conclusions. We propose this classification of the disorders according to their location to aid in achieving an objective selection of the sample and of the neuropsychological tests used in studies. In this regard, we consider that there should be a higher degree of agreement among researchers when it comes to designing research projects that deal with visuospatial disorders in patients with Parkinson’s disease.
Key words:
Cognitive disorders. Neuropsychology. Parkinson’s disease. Perceptive disorders. Visual cortex. Visual perception.

Daily motor tasks are often redundant. Humans are known to adopt motor strategies which consist of a stereotypical selection of specific postures for a given task. Such natural strategies are also known to be perturbed. An exoskeleton, specifically designed to minimally perturb motor strategies, was used to study the effects of ergonomic factors and mechanical impedance on human motor strategies during redundant tasks. Special attention is devoted to wrist robots since the human wrist, together with the hand, is involved in most manipulation tasks. To comply with kinematic constraints, ergonomic considerations are introduced at an early stage of design of exoskeleton, matching the biomechanical constraints imposed by human anatomy. Due to inter-subject anatomical differences, subject-specific kinematic models are determined to be used in the design of exoskeletons. The effects of kinematic compatibility on motor strategies are assessed and solutions from literature are adopted to avoid over-constrained configurations. Finally, a low-inertia wrist exoskeleton is devised and tested to be compatible with both biomechanical and neural constraints during pointing tasks.

The wide range of shape variations for handwritten digits requires an adequate representation of thediscriminating features for classification. For the recognition of characters or numerals requires pixel valuesof a normalized raster image and proper features to reach very good classification rate. This paper primarily concerns the problem of isolated handwritten numeral recognition of English scripts.Multilayer Perceptron(MLP) classifier is used for classification. The principalcontributions presented here are preprocessing, feature extraction and multilayer perceptron (MLP) classifiers.The strength of our approach is efficient feature extraction and the comprehensive classification scheme due to which, we have been able to achieve a recognition rate of 95.6, better than the previous approaches.

Nowadays, planning the process of electricity consumption demand is one of the keys success factors for the development of countries. Due to the importance of electricity, countries have greatly paid attention to the prediction of electricity consumption. Electricity consumption prediction is a major problem for the power sector; an efficient prediction will help electrical companies to take the right decisions and to optimize their supply strategies for their work. In this paper, we proposed a model that is used to predict the future electricity consumption depending on the previous consumption. This model provides companies and authorities to know the future information about the electricity consumption, so they can organize their distribution and make suitable plans to maintain the stability in the delivery and distribution of electricity. We aim to create a model that will be able to study the previous electricity consumption patterns and use this data to predict the future electricity consumption. The system analyzes the collected data of electricity consumption of the previous years, then byusing the mean value for each day and the use of Multilayer Feed-Forward with Backpropagation Neural Networks (MFFNNBP) as a tool to predict the future electricity consumption in Palestine. The data used in this paper depends on data collection of months and years. Finally, this proposed model conducts a systematic process with the aim of determining the future electricity consumption in Palestine. The proposed application and the result in this paper are developed in order to contribute to the improvement of the current energy planning tools in Palestine. The experimental results show that the model performs good results of prediction, with low Mean Square Error (MSE).

Deep neural network as a part of deep learning algorithm is a state-of-the-art approach to find higher level representations of input data which has been introduced to many practical and challenging learning problems successfully. The primary goal of deep learning is to use large data to help solving a given task on machine learning. We propose an methodology for image de-noising project defined by this model and conduct training a large image database to get the experimental output. The result shows the robustness and efficient our our algorithm.

While Alexander technique (AT) teachers have been reported to stand up by shifting weight gradually as they incline the trunk forward, healthy untrained (HU) adults appear unable to rise in this way. This study examines the hypothesis that HU have difficulty rising smoothly, and that this difficulty relates to reported differences in postural stiffness between groups. A wide range of movement durations (1-8 s) and anteroposterior foot placements were studied under the instruction to rise at a uniform rate. Before seat-off (SO) there were clear and profound performance differences between groups, particularly for slower movements, that could not be explained by strength differences. For each movement duration, HU used approximately twice the forward center-of-mass (CoM) velocity and vertical feet-loading rate as AT. For slow movements, HU violated task instruction by abruptly speeding up and rapidly shifting weight just before SO. In contrast, AT shifted weight gradually while smooth...

During multi-digit grasping both local and nonlocal digit force responses occur in response to changes in texture at selected digits depending on the grasp configuration. However, the extent to which the specific patterns of force distribution depend on the requirement to hold the object against gravity remains to be determined. In the present study, we examined whether grasp force sharing patterns are invariant when the constraint of maintaining the object orientation vertical against gravity is removed. We used changes in object texture to elicit force changes at single digits during two grasping tasks with different behavioral contexts. One task entailed holding an object against gravity (object hold [OH]). A second (force production [FP]) task consisted of generating lifting forces on an object clamped to the tabletop that were matched to those used during OH. Unlike OH, the FP task lacks the behavioral consequences associated with erroneous sharing of normal and tangential digit forces, e.g., object tilt. Ten subjects lifted and simulated lifting an instrumented object measuring grasping normal and vertical tangential forces at all five digits when the textures were uniformly high-friction sandpaper or low-friction rayon and when one digit contacted a different frictional texture than the other four digits. We found that in both tasks texture changes at individual digits elicited force changes at that digit as well as other digits. However, the specific pattern of force distribution changes differed during OH compared to FP. While subjects modulate the normal and tangential digit forces to different degrees depending on object texture and the grasping task, they ignore the requirement of moment equilibrium when this has no consequences on object orientation (FP task). These findings indicate that multi-digit force responses to texture revealed by previous studies are not obligatory and suggest that the behavioral context of a task should be considered when inferring general principles of multi-digit force coordination.

Previous studies have suggested that imitators can reproduce known gestures shown by a model using a semantic, indirect route, and novel gestures using a sublexical, direct route. In the present study we aimed at testing the validity of such a dual-route model of action imitation. Patients with either left-brain damage (LBD) or right-brain damage (RBD) were tested on an action imitation task. Actions were either meaningful (n = 20) or meaningless (n = 20), and were presented in an intermingled list and, on a different day, in separate lists. We predicted that, in the mixed condition, patients would use a direct route to imitate meaningful and meaningless actions, as it allows the imitation of both action types. In the blocked condition, patients were expected to select the semantic route for meaningful actions and the direct route for meaningless actions. As hypothesized, none of the 32 patients showed dissociations between imitation of meaningful and meaningless actions in the mixed presentation. In contrast, eight patients showed a dissociation between imitation of meaningful actions and imitation of meaningless actions in the blocked presentation. Moreover, two of these patients showed a classical double dissociation between the imitation of the two action types. Results were interpreted in support of the validity of a dual-route model for explaining action imitation. We argue that the decrease in imitation of meaningful actions, relative to meaningless actions, is caused by a damage of the semantic route, and that the decline in imitation of meaningless actions, relative to meaningful actions, is produced by a breakdown of the direct route. The brain areas that were lesioned in all six LBD patients who showed a dissociation were in the superior temporal gyrus and the angular gyrus, whereas the two RBD subjects had common lesions of the pallidum and of the putamen. The brain structures affected in our patients with selective apraxia are consistent with those reported before in other neuropsychological reports. They are also in agreement with areas found activated in imaging studies in which the neural mechanisms underlying imitation were examined.

Jute fiber-reinforced melamine composites (16–35% fiber) were prepared by hot press at 125°C for 10 min at 8 MT pressure. Tensile strength, tensile modulus, bending strength, bending modulus and impact strength of the composites (16% fiber) were found to be 44 MPa, 532 MPa, 112 MPa, 1.4 GPa and 13 kJ/m2, respectively. Effect of gamma radiation on the composites was investigated. Water uptake properties of the irradiated composites were found to improve significantly. Interfacial properties of the composites were investigated by SEM and were revealed that fiber matrix adhesion was quite good.

Static roll head tilt induces bias in the trajectory of upper limb voluntary movements. The aim of the experiment was to investigate whether this bias is dependant on the perception of body configuration rather than on its actual configuration. We used the 'return' phenomenon as a method to produce dissociation between perceived and actual head tilt. Static roll head tilt in supine subjects was sustained for 15 min during which subjects were periodically required to estimate verbally the tilt of their head respective to their trunk and draw, with their right index finger, straight lines aligned with their trunk. After 15 min, subjects' head were realigned with the trunk, and subjects continued to give verbal estimate of head position and perform the motor task. Results showed that the initial angular deviation of the lines in the direction opposite to head tilt gradually diminished. The adaptation was noticeable within the first 3-5 min of tilt and subsequently diminished. Verbal estimates confirmed the return phenomenon, i.e. subjects perceived their head as slowly returning towards its neutral position after a few minutes of sustained tilt. When realigned with the trunk, subjects experienced the illusion that their head was tilted in the opposite direction to the initial head tilt and a line deviation in the opposite direction to those made on initial exposure was observed (after-effect). These results indicate that the angular deviation in motor production observed in condition of static head tilt were largely related to the perceived body configuration and therefore favour the hypothesis that the conscious perception of body configuration plays a key role in organising sensorimotor tasks.

Object Difficulty with step initiation, called “start hesitation,” is related to gait bradykinesia and is an early hallmark of gait freezing in Parkinson disease (PD). Authors of this study investigated the effects of deep brain stimulation (DBS) and levodopa on step initiation before and 6 months after DBS surgery in 29 patients with PD who were randomized to either the bilateral subthalamic nucleus (STN) or globus pallidus internus (GPi) as the DBS site. Methods The authors measured the amplitude and duration of anticipatory postural adjustments (APAs), the feed-forward postural preparation that precedes the onset of voluntary step initiation, based on center-of-pressure displacements on a force plate. They also measured the length and velocity of the first step using a motion analysis system to study kinematics. Some of the patients (22) were from a large, multicenter, double-blind clinical trial, and all patients in the study (29, PD-DBS group) were randomized to DBS in either t...

Electrospinning is a polymer processing technique that produces fibrous structures comparable to the extracellular matrix of many tissues. Electrospinning, however, has been severely limited in its tissue engineering capabilities because this technique has produced few three-dimensional structures. Sintering of electrospun materials provides a method to fabricate unique architectures and allow much larger structures to be made. Electrospun mats were sintered into strips and cylinders, and their tensile and compressive mechanical properties were measured. In addition, electrospun materials with salt pores (salt embedded within the material and then leached out) were fabricated to improve porosity of the electrospun materials for tissue engineering scaffolds. Sintered electrospun poly(d,l-lactide) and poly(l-lactide) (PDLA/PLLA) materials have higher tensile mechanical properties (modulus: 72.3 MPa, yield: 960 kPa) compared to unsintered PLLA (modulus: 40.36 MPa, yield: 675.5 kPa). Electrospun PDLA/PLLA cylinders with and without salt-leached pores had compressive moduli of 6.69 and 26.86 MPa, respectively, and compressive yields of 1.36 and 0.56 MPa, respectively. Sintering of electrospun materials is a novel technique that improves electrospinning application in tissue engineering by increasing the size and types of electrospun structures that can be fabricated.

In this paper we propose a kernel adaptive filtering (KAF) approach to repair lesions via microstimulation in a biomimetic spiking neural network of sensorimotor cortex. The fundamental challenge of designing neuroprosthetics and brain machine interfaces (BMIs) is the decoding of electrical activity of neurons and behavior. For injured or damaged brain, intracranial stimulation has the potential to modulate neural activity to match meaningful and natural response or behavior. In order to estimate the optimal microstimulation sequences, we construct an inverse model of the target system. However, to obtain sufficient data to learn the inverse, the neural system much be stimulated or probed repeatedly. For real brains, this is especially challenging and often unfeasible. Here, we demonstrate that by applying KAF to a biomimetic brain and realistic virtual musculoskeletal model, we are able to repair simulated lesion and drive the virtual arm to perform the correct motor task.

This paper presents a detailed description of the design, fabrication and mechanical characterization of 3D microelectrode arrays (MEA) that comprise high aspect-ratio shafts and different penetrating lengths of electrodes (from 3 mm to 4 mm). The array’s design
relies only on a bulk silicon substrate dicing saw technology. The encapsulation process is accomplished by a medical epoxy resin and platinum is used as the transduction layer between the probe and neural tissue. The probe’s mechanical behaviour can significantly affect the neural tissue during implantation time. Thus, we measured the MEA maximum insertion force in an agar gel phantom and a porcine cadaver brain. Successful 3D MEA were produced with shafts of 3 mm, 3.5 mm and 4 mm in length. At a speed of 180 mm min−1, the MEA show maximum penetrating forces per electrode of 2.65 mN and 12.5 mN for agar and brain tissue, respectively. A simple and reproducible fabrication method was demonstrated, capable of producing longer penetrating shafts than previously reported arrays using the same fabrication technology. Furthermore, shafts with sharp tips were achieved in the fabrication process simply by using a V-shaped blade.

Static roll head tilt induces bias in the trajectory of upper limb voluntary movements. The aim of the experiment was to investigate whether this bias is dependant on the perception of body configuration rather than on its actual configuration. We used the ‘return’ phenomenon as a method to produce dissociation between perceived and actual head tilt. Static roll head tilt in supine subjects was sustained for 15 min during which subjects were periodically required to estimate verbally the tilt of their head respective to their trunk and draw, with their right index finger, straight lines aligned with their trunk. After 15 min, subjects’ head were realigned with the trunk, and subjects continued to give verbal estimate of head position and perform the motor task. Results showed that the initial angular deviation of the lines in the direction opposite to head tilt gradually diminished. The adaptation was noticeable within the first 3–5 min of tilt and subsequently diminished. Verbal estimates confirmed the return phenomenon, i.e. subjects perceived their head as slowly returning towards its neutral position after a few minutes of sustained tilt. When realigned with the trunk, subjects experienced the illusion that their head was tilted in the opposite direction to the initial head tilt and a line deviation in the opposite direction to those made on initial exposure was observed (after-effect). These results indicate that the angular deviation in motor production observed in condition of static head tilt were largely related to the perceived body configuration and therefore favour the hypothesis that the conscious perception of body configuration plays a key role in organising sensorimotor tasks.

The field of brain-machine interfaces (BMIs) has made great advances in recent years, converting thought to movement, with some of the most successful implementations measuring directly from the motor cortex. However, the ability to record from additional regions of the brain could potentially improve flexibility and robustness of use. In addition, BMIs of the future will benefit from integrating kinesthesia into the control loop. Here, we examine whether changes in passively induced forefinger movement amplitude is represented in different regions than forefinger velocity using an MR compatible robotic manipulandum. Using functional magnetic resonance imaging (fMRI), five healthy participants were exposed to combinations of forefinger movement amplitude and velocity in a factorial design followed by an epoch-based analysis. We found that primary and secondary somatosensory regions were activated, as well as cingulate motor area, putamen and cerebellum, with greater activity from changes in velocity compared to changes in amplitude. This represents the first investigation into whole brain response to passive movement kinematics. In addition to informing BMIs, these results have implications towards neural correlates of robotic rehabilitation.

This paper describes a two-degrees-of-freedom haptic interface to investigate the brain mechanisms of human motor control, which is capable of safely and gently interacting with human arm motion during functional magnetic resonance imaging (fMRI). A hydrostatic transmission separates the interface into a master and an MR compatible slave system, allowing the placement of all interfering components outside the electromagnetic shield of the MR room. The transmission mirrors force and motion of the master actuators on the slave system placed close to the MR scanner. The parallel architecture takes advantage of the linear MR compatible actuators and allows human subjects to perform reaching movements comfortably in the small workspace limited by the dimensions of the MR scanner and the biomechanics of the arm. The kinematic structure of the slave interface was optimized with respect to the available space and types of movements to be investigated. Materials were chosen based on their MR compatibility, their stiffness and weight. The interaction force with the subject is measured over two optical force sensors, located close to the output of the interface. Two shielded optoelectronic encoders measure the extension of the slave hydraulic pistons. Detailed tests demonstrated the fMRI compatibility even during movement of the interface.

McIsaac TL, Fuglevand AJ. Motor-unit synchrony within and across compartments of the human flexor digitorum superficialis. . An interesting feature of the muscular organization of the human hand is that the main flexors and extensors of the fingers are compartmentalized and give rise to multiple parallel tendons that insert onto all the fingers. Previous studies of motor-unit synchrony in extensor digitorum and flexor digitorum profundus indicated that synaptic input to motor neurons supplying these multitendoned muscles is not uniformly distributed across the entire pool of motor neurons but instead appears to be partially segregated to supply subsets of motor neurons that innervate different muscular compartments. Little is known, however, about the organization of the synaptic inputs to the motor neurons supplying another multitendoned finger muscle, the flexor digitorum superficialis (FDS). Therefore in this study, we estimated the extent of divergence of last-order inputs to FDS motor neurons by measuring the degree of short-term synchrony among motor units within and across compartments of FDS. The degree of synchrony for motor-unit pairs within the same digit compartment was nearly twofold that of pairs of motor units in adjacent compartments and more than fourfold that of pairs in nonadjacent compartments. Therefore like other multitendoned muscles of the hand, last-order synaptic inputs to motor neurons supplying the FDS appear to primarily supply subsets of motor neurons innervating specific finger compartments. Such an organization presumably enables differential activation of separate compartments to facilitate independent movements of the fingers.

A software tool has been developed to support the objective diagnosis of patients with Parkinson's Disease (PD). Patients completed hand exercises using a personal computer mouse and data has been gathered for further studies. We have analyzed different parameters and suggest using a particular parameter vector containing median and standard deviation values for tracking the daily changes in PD patients' status. Using the classification abilities of self-organizing feature maps (SOFM) we were able to provide support for the diagnostic process.

The theoretical framework of coordination dynamics posits complementary neural mechanisms to maintain complex behavioral patterns under circumstances that may render them unstable and to voluntarily switch between behaviors if changing internal or external conditions so demand. A candidate neural structure known to play a role in both the selection and maintenance of intentional behavior is the basal ganglia. Here, we use functional magnetic resonance imaging to explore the role of basal ganglia in intentional switching between bimanual coordination patterns that are known to differ in their stability as a function of movement rate. Key measures of pattern dynamics and switching were used to map behavior onto the associated neural circuitry to determine the relation between specific behavioral variables and activated brain areas. Results show that putamen activity is highly sensitive to pattern stability: greater activity was observed in bilateral putamen when subjects were required to switch from a more to a less stable pattern than vice versa. Since putamen activity correlated with pattern stability both before and during the switching process, its role may be to select desired actions and inhibit competing ones through parametric modulation of the intrinsic dynamics. Though compatible with recent computational models of basal ganglia function, our results further suggest that pattern stability determines how the basal ganglia efficiently and successfully select among response alternatives.

Here we apply a control theoretic view of movement to the behavior of human locomotion with the goal of using perturbations to learn about subtask control. Controlling one's speed and maintaining upright posture are two critical subtasks, or underlying functions, of human locomotion. How the nervous system simultaneously controls these two subtasks was investigated in this study. Continuous visual and mechanical perturbations were applied concurrently to subjects (n = 20) as probes to investigate these two subtasks during treadmill walking. Novel application of harmonic transfer function (HTF) analysis to human motor behavior was used, and these HTFs were converted to the time-domain based representation of phase-dependent impulse response functions (ϕIRFs). These ϕIRFs were used to identify the mapping from perturbation inputs to kinematic and electromyographic (EMG) outputs throughout the phases of the gait cycle. Mechanical perturbations caused an initial, passive change in t...

Here we apply a control theoretic view of movement to the behavior of human locomotion with the goal of using perturbations to learn about subtask control. Controlling one's speed and maintaining upright posture are two critical subtasks, or underlying functions, of human locomotion. How the nervous system simultaneously controls these two subtasks was investigated in this study. Continuous visual and mechanical perturbations were applied concurrently to subjects (n = 20) as probes to investigate these two subtasks during treadmill walking. Novel application of harmonic transfer function (HTF) analysis to human motor behavior was used, and these HTFs were converted to the time-domain based representation of phase-dependent impulse response functions (φIRFs). These φIRFs were used to identify the mapping from perturbation inputs to kinematic and electromyographic (EMG) outputs throughout the phases of the gait cycle. Mechanical perturbations caused an initial, passive change in t...

The locomotor gait in limbed animals is defined by the left-right leg coordination and locomotor speed. Coordination between left and right neural activities in the spinal cord controlling left and right legs is provided by commissural interneurons (CINs). Several CIN types have been genetically identified, including the excitatory V3 and excitatory and inhibitory V0 types. Recent studies demonstrated that genetic elimination of all V0 CINs caused switching from a normal left-right alternating activity to a left-right synchronized "hopping" pattern. Furthermore, ablation of only the inhibitory V0 CINs (V0 D subtype) resulted in a lack of leftright alternation at low locomotor frequencies and retaining this alternation at high frequencies, whereas selective ablation of the excitatory V0 neurons (V0 V subtype) maintained the left-right alternation at low frequencies and switched to a hopping pattern at high frequencies. To analyze these findings, we developed a simplified mathematical model of neural circuits consisting of four pacemaker neurons representing left and right, flexor and extensor rhythm-generating centers interacting via commissural pathways representing V3, V0 D , and V0 V CINs. The locomotor frequency was controlled by a parameter defining the excitation of neurons and commissural pathways mimicking the effects of N-methyl-D-aspartate on locomotor frequency in isolated rodent spinal cord preparations. The model demonstrated a typical left-right alternating pattern under control conditions, switching to a hopping activity at any frequency after removing both V0 connections, a synchronized pattern at low frequencies with alternation at high frequencies after removing only V0 D connections, and an alternating pattern at low frequencies with hopping at high frequencies after removing only V0 V connections. We used bifurcation theory and fast-slow decomposition methods to analyze network behavior in the above regimes and transitions between them. The model reproduced, and suggested explanation for, a series of experimental phenomena and generated predictions available for experimental testing.

The functional significance and mechanisms of short-term motor compensations to peripheral nerve injury are still debated. Denervation of selected ankle extensors in animals results in locomotor changes that may be mediated by increased proprioceptive feedback and central drive. These changes have been suggested to permit preservation of global kinematic characteristics (leg length and leg orientation trajectory) of the hindlimb during stance. The peak ankle joint moment is also preserved immediately after denervation of several ankle extensors in the cat, suggesting that the animal's response to peripheral nerve injury may also be aimed at preserving ankle mechanical output. We tested this hypothesis by comparing joint moments and power patterns during walking before and after denervation of soleus (SO) and lateral gastrocnemius (LG) muscles. Hindlimb kinematics, ground reaction forces and EMG of selected muscles were recorded during level, downslope (-50%), and upslope (50%) walking before and 1-3 weeks after nerve denervation. During the first three weeks after injury, no EMG activity in SO and LG was observed. Denervation resulted in increased activity of the intact medial gastrocnemius and plantaris muscles, greater ankle dorsiflexion, smaller knee flexion, and the preservation of the peak ankle moment during stance. Surprisingly, ankle positive power generated in the propulsion phase of stance was increased (up to 50%) after denervation in all walking conditions (p<0.05). The obtained results suggest that the short-term motor compensation to denervation of LG and SO may allow for preservation of mechanical output at the ankle. The additional mechanical energy generated at the ankle during propulsion can result, in part, from increased activity of intact synergists, the use of passive tissues around the ankle and by the tendon action of ankle two-joint muscles and crural fascia.