Neural control of three-dimensional eye and head movements

Although the eyes and head can potentially rotate about any three-dimensional axis during orienting gaze shifts, behavioral recordings have shown that certain lawful strategies--such as Listing's law and Donders' law--determine which axis is used for a particular sensory input. Here, we review recent advances in understanding the neuromuscular mechanisms for these laws, the neural mechanisms that control three-dimensional head posture, and the neural mechanisms that coordinate three-dimensional eye orientation with head motion. Finally, we consider how the brain copes with the perceptual consequences of these motor acts.     

Neural network estimation of balance control during locomotion

Gait patterns of the elderly are often adjusted to accommodate for reduced function in the balance control system and a general reduction in skeletal muscle strength. Recent studies have demonstrated that measures related to motion of whole body center of mass (COM) can distinguish elderly individuals with balance impairment from healthy peers. Accurate COM estimation requires a multiple-segment anthropometric model, which may restrict its broad application in assessment of dynamic instability. Although temporal-distance measures and electromyography have been used in evaluation of overall gait function and determination of gait dysfunction, no studies have examined the use of gait measurements in predicting COM motion during gait. The purpose of this study was to demonstrate the effectiveness of an artificial neural network (ANN) model in mapping gait measurements onto COM motion in the frontal plane. Data from 40 subjects of varied age and balance impairment were entered into a 3-layer feed-forward model with back-propagated error correction. Bootstrap re-sampling was used to enhance the generalization accuracy of the model, using 20 re-sampling trials. The ANN model required minimal processing time (5 epochs, with 20 hidden units) and accurately mapped COM motion (R-values up to 0.89). As training proportion and number of hidden units increased, so did model accuracy. Overall, this model appears to be effective as a mapping tool for estimating balance control during locomotion. With easily obtained gait measures as input and a simple, computationally efficient architecture, the model may prove useful in clinical scenarios where electromyography equipment exists.     

Neural network classification of myoelectric signal for prosthesis control

An alternate approach to deriving control for multidegree of freedom prosthetic arms is considered. By analyzing a single-channel myoelectric signal (MES), we can extract information that can be used to identify different contraction patterns in the upper arm. These contraction patterns are generated by subjects without previous training and are naturally associated with specific functions. Using a set of normalized MES spectral features, we can identify contraction patterns for four arm functions, specifically extension and flexion of the elbow and pronation and supination of the forearm. Performing identification independent of signal power is advantageous because this can then be used as a means for deriving proportional rate control for a prosthesis. An artificial neural network implementation is applied in the classification task. By using three single-layer perceptron networks, the MES is classified, with the spectral representations as input features. Trials performed on five subjects with normal limbs resulted in an average classification performance level of 85% for the four functions.     

Synergic analysis of upper limb target-reaching movements

The topological invariance and synergies of human movements are discussed through the analysis and comparison of upper-limb target-reaching tasks. Five subjects were asked to perform different target-reaching tasks with different indices of difficulty, and the movements were captured using a Vicon 3D motion analysis system.Topological invariance was observed in the trajectories of different task performances. After normalization, the trajectories of the arm tips had very close patterns for different target-reaching tasks. Synergy in the target-reaching movements of the upper limbs was also found among the different joint angles. The joint angles can be fitted using the same format of functions proposed in this study. The parameters in the function can be taken as a characteristic feature of target-reaching movement patterns. A target-reaching movement can be determined by these parameters and the start and end positions.     

Neural network control systems that learn to perform appropriately

Setting up a neural network with a learning algorithm that determines how it can best operate is an efficient way to formulate control systems for many engineering applications, and is often much more feasible than direct programming. This paper examines three important aspects of this approach: the details of the cost function that is used with the gradient descent learning algorithm, how the resulting system depends on the initial pre-learning connection weights, and how the resulting system depends on the pattern of learning rates chosen for the different components of the system. We explore these issues by explicit simulations of a toy model that is a simplified abstraction of part of the human oculomotor control system. This allows us to compare our system with that produced by human evolution and development. We can then go on to consider how we might improve on the human system and apply what we have learnt to control systems that have no human analogue.     

Neural network modeling and control of type 1 diabetes mellitus

This paper presents a developed and validated dynamic simulation model of type 1 diabetes, that simulates the progression of the disease and the two term controller that is responsible for the insulin released to stabilize the glucose level. The modeling and simulation of type 1 diabetes mellitus is based on an artificial neural network approach. The methodology builds upon an existing rich database on the progression of type 1 diabetes for a group of diabetic patients. The model was found to perform well at estimating the next glucose level over time without control. A neural controller that mimics the pancreas secretion of insulin into the body was also developed. This controller is of the two term type: one stage is responsible for short-term and the other for mid-term insulin delivery. It was found that the controller designed predicts an adequate amount of insulin that should be delivered into the body to obtain a normalization of the elevated glucose level. This helps to achieve the main objective of insulin therapy: to obtain an accurate estimate of the amount of insulin to be delivered in order to compensate for the increase in glucose concentration.     

Neural control of olfaction and tentacle movements by serotonin and dopamine in terrestrial snail

We investigated the role of serotonin (5HT) and dopamine (DA) in the regulation of olfactory system function and odor-evoked tentacle movements in the snail Helix. Preparations of the posterior tentacle (including sensory pad, tentacular ganglion and olfactory nerve) or central ganglia with attached posterior tentacles were exposed to cineole odorant and the evoked responses were affected by prior application of 5HT or DA or their precursors 5-hydroxytryptophan (5HTP) and l-DOPA, respectively. 5HT applications decreased cineole-evoked responses recorded in the olfactory nerve and hyperpolarized the identified tentacle retractor muscle motoneuron MtC3, while DA applications led to the opposite changes. 5HTP and l-DOPA modified MtC3 activity comparable to 5HT and DA action. DA was also found to decrease the amplitude of spontaneous local field potential oscillations in the procerebrum, a central olfactory structure. In vivo studies demonstrated that injection of 5HTP in freely moving snails reduced the tentacle withdrawal response to aversive ethyl acetate odorant, whereas the injection of l-DOPA increased responses to “neutral” cineole and aversive ethyl acetate odorants. Our data suggest that 5HT and DA affect the peripheral (sensory epithelium and tentacular ganglion), the central (procerebrum), and the single motor neuron (withdrawal motoneuron MtC3) level of the snail’s nervous system.     

Invited review: Neural network plasticity in respiratory control.

Respiratory network plasticity is a modification in respiratory control that persists longer than the stimuli that evoke it or that changes the behavior produced by the network. Different durations and patterns of hypoxia can induce different types of respiratory memories. Lateral pontine neurons are required for decreases in respiratory frequency that follow brief hypoxia. Changes in synchrony and firing rates of ventrolateral and midline medullary neurons may contribute to the long-term facilitation of breathing after brief intermittent hypoxia. Long-term changes in central respiratory motor control may occur after spinal cord injury, and the brain stem network implicated in the production of the respiratory rhythm could be reconfigured to produce the cough motor pattern. Preliminary analysis suggests that elements of brain stem respiratory neural networks respond differently to hypoxia and hypercapnia and interact with areas involved in cardiovascular control. Plasticity or alterations in these networks may contribute to the chronic upregulation of sympathetic nerve activity and hypertension in sleep apnea syndrome and may also be involved in sudden infant death syndrome.     

Basic functions of variability of simple pre-planned movements

A model of a pre-planned single joint movements performed without feedback is considered. Modifications of this movement result from transformation of a trajectory pattern f(t) in space and time. The control system adjusts the movement to concrete external conditions specifying values of the transform parameters before the movement performance. The preplanned movement is considered to be simple one, if the transform can be approximated by an affine transform of the movement space and time. In this case, the trajectory of the movement is x(t) = Af(t/ + s) +p, were A and 1/ are space and time scales, s and p are translations. The variability of movements is described by time profiles of variances and covariances of the trajectory x(t), velocity v(t), and acceleration a(t). It is assumed that the variability is defined only by parameters variations. From this assumption follows the main finding of this work: the variability time profiles can be expanded on a special system of basic functions corresponding to established movement parameters. Particularly, basic functions of variance time profiles, reflecting spatial and temporal scaling, are x ²(t) and t ² v ²(t) for trajectory, v ²(t) and (v(t) + t · a(t))² for velocity, and a ²(t) and (2a(t) +t · j(t))², where j(t) = d³ x(t)/dt ³, for acceleration. The variability of a model of a reaching movement was studied analytically. The model predicts certain peculiarities of the form of time profiles (e.g., the variance time profile of velocity is bi-modal, the one of acceleration is tri-modal, etc.). Experimental measurements confirmed predictions. Their consistence allows them to be considered invariant properties of reaching movement. A conclusion can be made, that reaching movement belongs to the type of simple preplanned movements. For a more complex movement, time profiles of variability are also measured and explained by the model of movements of this type. Thus, a movement can be attributed to the type of simple pre-planned ones by testing its variability.     

Planning movements in a simple redundant task

There are infinitely many different combinations of arm postures which will place the hand at the same point in space. Given this abundance, how is one configuration chosen over another? Two main hypotheses have been proposed to solve this problem. Postural models suggest that the posture adopted is purely determined by the desired hand position (known as Donders' law). Transport models suggest that the adopted posture depends on where the hand has moved from. A specific transport model, the minimum work model, has been proposed in which the adopted posture is the one that minimizes the amount of work required to move the hand to the new location. The postural model predicts that the posture will be independent of where the hand has moved from, whereas the transport models predict that the posture will depend on the previous posture. We have devised a simple redundant task-touching a target bar using a hand-held virtual stick-to examine these models. The results show that neither model alone can account for the data. We propose a control planning strategy in which there is a combined cost function that has both a postural term as well as a transport term.     

Neural mechanisms for the recognition of biological movements

The visual recognition of complex movements and actions is crucial for the survival of many species. It is important not only for communication and recognition at a distance, but also for the learning of complex motor actions by imitation. Movement recognition has been studied in psychophysical, neurophysiological and imaging experiments, and several cortical areas involved in it have been identified. We use a neurophysiologically plausible and quantitative model as a tool for organizing and making sense of the experimental data, despite their growing size and complexity. We review the main experimental findings and discuss possible neural mechanisms, and show that a learning-based, feedforward model provides a neurophysiologically plausible and consistent summary of many key experimental results.     

Cortical control of reaching movements

Recent studies provide further support for the hypothesis that spatial representations of limb position, target locations, and potential motor actions are expressed in the neuronal activity in parietal cortex. In contrast, precentral cortical activity more strongly expresses processes involved in the selection and execution of motor actions. As a general conceptual framework, these processes may be interpreted in terms of such formalisms as sensorimotor transformation and ‘internal models’.