Rhythmic Movement of a Pair of One-Link Arms: Coordination by Intermittent Control

This paper considers the coordination and control of periodic movements of a pair of one-link arms. The system consists of two one-link arms each controlled by two muscle-like actuators. The muscle-like actuators are activated by simulated neural inputs. The model is simple enough to analyze, yet it embodies many aspects of human arms. Three attributes of the rhythmic coordinated movement of two arms, namely frequency, magnitude, and relative phase, are the only inputs to the controller. The controller uses mild co-activation and primarily activates the agonist. The effects of transmission delays, present in the reflex loop of physiological systems, also are modeled. The results of this research indicate the feasibility of controlling oscillatory body movements with short periods of activation. The result of many simulations, by varying the frequency or amplitude of the movement, indicate that the apparent lack of a simple relationship between neural control and desired behavior of the system should not be mistaken as evidence for the absence of a causal relationship between the activation patterns of the muscles and the desired behavior. Simulations of this system show stable oscillations at different frequencies and magnitudes even with additive noise and changes in the system mass.     

Control of a one-link arm by burst signal generators

 The focus of this paper is the study of stability and point-to-point movement of a one-link arm. The sagittal arm has two musculotendon actuators, two neural oscillators that generate burst signals as motoneuron inputs, and spindles and Golgi tendon organs for extrinsic reflex feedbacks. It is shown that coactivation leads to intrinsic position and velocity feedback, and that the tendons introduce intrinsic force and rate of force feedback. In addition, the integrating effects of the tendons are studied when the actuator is constructed from a large number of identical fibers that are excited by alpha signals whose arrival times at the fiber are randomly distributed. Each of the musculotendon actuators receives two input signals – a burst signal analogous to alpha inputs and a conventional analogue signal that represents fusimotor input to the spindles. The process of combining burst signals and conventional analogue signals is studied. Simulation results show that the movement of the system with burst signals as inputs has overshoot and speed similar to the system with analogue signals. Received: 30 May 1994/Accepted in revised form: 13 January 1995     

A hierarchical neural-network model for control and learning of voluntary movement

In order to control voluntary movements, the central nervous system (CNS) must solve the following three computational problems at different levels: the determination of a desired trajectory in the visual coordinates, the transformation of its coordinates to the body coordinates and the generation of motor command. Based on physiological knowledge and previous models, we propose a hierarchical neural network model which accounts for the generation of motor command. In our model the association cortex provides the motor cortex with the desired trajectory in the body coordinates, where the motor command is then calculated by means of long-loop sensory feedback. Within the spinocerebellum — magnocellular red nucleus system, an internal neural model of the dynamics of the musculoskeletal system is acquired with practice, because of the heterosynaptic plasticity, while monitoring the motor command and the results of movement. Internal feedback control with this dynamical model updates the motor command by predicting a possible error of movement. Within the cerebrocerebellum — parvocellular red nucleus system, an internal neural model of the inverse-dynamics of the musculo-skeletal system is acquired while monitoring the desired trajectory and the motor command. The inverse-dynamics model substitutes for other brain regions in the complex computation of the motor command. The dynamics and the inverse-dynamics models are realized by a parallel distributed neural network, which comprises many sub-systems computing various nonlinear transformations of input signals and a neuron with heterosynaptic plasticity (that is, changes of synaptic weights are assumed proportional to a product of two kinds of synaptic inputs). Control and learning performance of the model was investigated by computer simulation, in which a robotic manipulator was used as a controlled system, with the following results: (1) Both the dynamics and the inverse-dynamics models were acquired during control of movements. (2) As motor learning proceeded, the inverse-dynamics model gradually took the place of external feedback as the main controller. Concomitantly, overall control performance became much better. (3) Once the neural network model learned to control some movement, it could control quite different and faster movements. (4) The neural netowrk model worked well even when only very limited information about the fundamental dynamical structure of the controlled system was available. Consequently, the model not only accounts for the learning and control capability of the CNS, but also provides a promising parallel-distributed control scheme for a large-scale complex object whose dynamics are only partially known.     

Are interlimb interactions disturbed in patients with Parkinson’s disease? A study under unloading conditions

During natural human locomotion, neural connections are activated that are typical of regulation of the quadrupedal walking. The interaction between the neural networks generating rhythmic movements of the upper and lower limbs depends on tonic state of each of these networks regulated by motor signals from the brain. Distortion of these signals in patients with Parkinson’s disease (PD) may lead to disruption of the interlimb interactions. We examined the effect of movements of the limbs of one girdle on the parameters of the motor activity of another limb girdle at their joint cyclic movements under the conditions of arm and leg unloading in 17 patients with PD and 16 healthy subjects. We have shown that, in patients, the effect of voluntary and passive movements of arms, as well as the active movement of the distal parts of arms, on the voluntary movement of legs is weak, while in healthy subjects, the effect of arm movements on the parameters of voluntary stepping is significant. The effect of arm movements on the activation of the involuntary stepping by vibrational stimulation of-legs in patients was absent, while in healthy subjects, the motor activity of arms increased the possibility of involuntary rhythmic movements activation. Differences in the effect of leg movements on the rhythmic movements of arms were found in both patients and healthy subjects. The interlimb interaction appeared after drug administration. However, the effect of the drug was not sufficient for the recovery of normal state of the neural networks in patients. In PD patients, neural networks generating stepping rhythm have an increased tonic activity, which prevents the activation and appearance of involuntary rhythmic movements facilitating the effects of arms on legs.     

Concurrent adaptation of force and impedance in the redundant muscle system

This article examines the validity of a model to explain how humans learn to perform movements in environments with novel dynamics, including unstable dynamics typical of tool use. In this model, a simple rule specifies how the activation of each muscle is adapted from one movement to the next. Simulations of multijoint arm movements with a neuromuscular plant that incorporates neural delays, reflexes, and signal-dependent noise, demonstrate that the controller is able to compensate for changing internal or environment dynamics and noise properties. The computational model adapts by learning both the appropriate forces and required limb impedance to compensate precisely for forces and instabilities in arbitrary directions with patterns similar to those observed in motor learning experiments. It learns to regulate reciprocal activation and co-activation in a redundant muscle system during repeated movements without requiring any explicit transformation from hand to muscle space. Independent error-driven change in the activation of each muscle results in a coordinated control of the redundant muscle system and in a behavior that reduces instability, systematic error, and energy.     

Activation of interlimb interactions increases the motor output in legs of healthy subjects: Study under the conditions of arm and leg unloading

The effect of arm movements and movements of individual arm joints on the electrophysiological and kinematic characteristics of voluntary and vibration-triggered stepping-like leg movements was studied under the conditions of horizontal support of the upper and lower limbs. The horizontal support of arms provided a significant increase in the rate of activation of locomotor automatism by noninvasive impact on tonic sensory inputs. The addition of active arm movements during involuntary stepping-like leg movements led to an increase in the EMG activity of hip muscles and was accompanied by an increase in the amplitude of hip and shin movements. The movement of the shoulder joints led to an increase in the activity of hip muscles and was accompanied by an increase in the amplitude of hip and shin movements. Passive arm movements had the same effect on induced leg movements. The movement of the shoulder joints led to an increase in the activity of hip muscles and an increase in the amplitude of movements of knee and hip joints. At the same time, the movement of forearms and wrists had a similar facilitating effect on the physiological and kinematic characteristics of rhythmic stepping-like movements, but influenced the distal segments of legs to a greater extent. Under the conditions of subthreshold vibration of leg muscles, voluntary arm movements led to activation of involuntary rhythmic stepping movements. During voluntary leg movements, the addition of arm movements had a significantly smaller impact on the parameters of rhythmic stepping than during involuntary leg movements. Thus, the simultaneous movements of the upper and lower limbs are an effective method of activation of neural networks connecting the rhythm generators of arms and legs. Under the conditions of arm and leg unloading, the interactions between the cervical and lumbosacral segments of the spinal cord seem to play the major role in the impact of arm movements on the patterns of leg movements. The described methods of activation of interlimb interactions can be used in the rehabilitation of post-stroke patients and patients with spinal cord injuries, Parkinson’s disease, and other neurological diseases.     

Analysis of an optimal control model of multi-joint arm movements

 In this paper, we propose a model of biological motor control for generation of goal-directed multi-joint arm movements, and study the formation of muscle control inputs and invariant kinematic features of movements. The model has a hierarchical structure that can determine the control inputs for a set of redundant muscles without any inverse computation. Calculation of motor commands is divided into two stages, each of which performs a transformation of motor commands from one coordinate system to another. At the first level, a central controller in the brain accepts instructions from higher centers, which represent the motor goal in the Cartesian space. The controller computes joint equilibrium trajectories and excitation signals according to a minimum effort criterion. At the second level, a neural network in the spinal cord translates the excitation signals and equilibrium trajectories into control commands to three pairs of antagonist muscles which are redundant for a two-joint arm. No inverse computation is required in the determination of individual muscle commands. The minimum effort controller can produce arm movements whose dynamic and kinematic features are similar to those of voluntary arm movements. For fast movements, the hand approaches a target position along a near-straight path with a smooth bell-shaped velocity. The equilibrium trajectories in X and Y show an ‘N’ shape, but the end-point equilibrium path zigzags around the hand path. Joint movements are not always smooth. Joint reversal is found in movements in some directions. The excitation signals have a triphasic (or biphasic) pulse pattern, which leads to stereotyped triphasic (or biphasic) bursts in muscle control inputs, and a dynamically modulated joint stiffness. There is a fixed sequence of muscle activation from proximal muscles to distal muscles. The order is preserved in all movements. For slow movements, it is shown that a constant joint stiffness is necessary to produce a smooth movement with a bell-shaped velocity. Scaled movements can be reproduced by varying the constraints on the maximal level of excitation signals according to the speed of movement. When the inertial parameters of the arm are altered, movement trajectories can be kept invariant by adjusting the pulse height values, showing the ability to adapt to load changes. These results agree with a wide range of experimental observations on human voluntary movements. Received: 4 December 1995 / Accepted in revised form: 17 September 1996     

Cortical mechanisms of smooth eye movements revealed by dynamic covariations of neural and behavioral responses

Neural activity in the frontal eye fields controls smooth pursuit eye movements, but the relationship between single neuron responses, cortical population responses, and eye movements is not well understood. We describe an approach to dynamically link trial-to-trial fluctuations in neural responses to parallel variations in pursuit and demonstrate that individual neurons predict eye velocity fluctuations at particular moments during the course of behavior, while the population of neurons collectively tiles the entire duration of the movement. The analysis also reveals the strength of correlations in the eye movement predictions derived from pairs of simultaneously recorded neurons and suggests a simple model of cortical processing. These findings constrain the primate cortical code for movement, suggesting that either a few neurons are sufficient to drive pursuit at any given time or that many neurons operate collectively at each moment with remarkably little variation added to motor command signals downstream from the cortex.     

Analysis of a muscular control system in human movements

In this work, we have studied a muscular control system under experimental conditions for analyzing the dynamic behavior of individual muscles and theoretical considerations for elucidating its control strategy. Movement of human limbs is achieved by joint torques and each torque is specified as the sum of torques generated by muscle forces. The behavior of individual muscles is controlled by the neural input which is estimated by means of an electromyogram (EMG). In this study, the EMGs for a flexor and an extensor are measured in elbow joint movements and the dynamic behavior of individual muscles is analyzed. As a result, it is verified that both a flexor and an extensor are activated throughout the entire movement and that the activation of muscles is controlled above a specific limit independent of the hand-held load. Subsequently, a system model for simulating elbow joint movements is developed which includes the muscle dynamic relationship between the neural input and the isometric force. The minimum limit of muscle activation that has been confirmed in experiments is provided as a constraint of the neural input and the criterion is defined by a derivative of the isometric force of individual muscles. The optimal trajectories formulated under these conditions are quantitatively compared with the experimentally observed trajectories, and the control strategy of a muscular control system is studied. Finally, a muscular control system in multi-joint arm movements is discussed with regard to the comparative analysis between observed and optimal trajectories. Received: 7 April 1999 / Accepted in revised form: 27 July 1999     

A coupled oscillator model of disordered interlimb coordination in patients with Parkinson's disease

Coordination between the left and right limbs during cyclic movements, which can be characterized by the amplitude of each limb's oscillatory movement and relative phase, is impaired in patients with Parkinson's disease (PD). A pedaling exercise on an ergometer in a recent clinical study revealed several types of coordination disorder in PD patients. These include an irregular and burst-like amplitude modulation with intermittent changes in its relative phase, a typical sign of chaotic behavior in nonlinear dynamical systems. This clinical observation leads us to hypothesize that emergence of the rhythmic motor behaviors might be concerned with nonlinearity of an underlying dynamical system. In order to gain insight into this hypothesis, we consider a simple hard-wired central pattern generator model consisting of two identical oscillators connected by reciprocal inhibition. In the model, each oscillator acts as a neural half-center controlling movement of a single limb, either left or right, and receives a control input modeling a flow of descending signals from higher motor centers. When these two control inputs are tonic-constant and identical, the model has left-right symmetry and basically exhibits ordered coordination with an alternating periodic oscillation. We show that, depending on the intensities of these two control inputs and on the difference between them that introduces asymmetry into the model, the model can reproduce several behaviors observed in the clinical study. Bifurcation analysis of the model clarifies two possible mechanisms for the generation of disordered coordination in the model: one is the spontaneous symmetry-breaking bifurcation in the model with the left-right symmetry. The other is related to the degree of asymmetry reflecting the difference between the two control inputs. Finally, clinical implications by the model's dynamics are briefly discussed.     

Mutual influences of upper and lower extrimities during cyclic movements

The possibility of muscle activation of passive arm during its cyclic movements, imposed by active movements of contralateral arm or by experimenter was studied, as well as the influence of lower extremities cyclic movements onto arm muscles activity. In addition to that the activity of legs muscles was estimated in dependence on motor task condition for arms. Ten healthy supine subjects carried out opposite movements of arms with and without stepping-like movements of both legs. The experiment included three conditions for arm movements: 1) the active movements of both arms; 2) the active movements of one arm, when other entirely passive arm participated in the movement by force; 3) passive arm movement caused by experimenter. In the condition 2) additional load on active arm was applied (30 N and 60 N). In all three conditions the experiment was carried out with arms movements only or together with legs movements. The capability of passive moving arm muscles activation depended on increasing afferent inflow from muscles of contralateral arm was demonstrated. Emerging electrical activity was modulated in the arms movements cycle and depended on the degree of active arm loading. During combined active movements of arms and legs the reduction of activity in the flexor muscles of shoulder and forearm was observed. Concomitant arms movements increased the magnitude ofelectromiographic bursts during passive stepping-like movements in the most of recorded muscles, and the same increasing was only observed in biceps femoris and tibialis anterior muscles during active legs movement. The increasing of loading of one arm caused essential augmentation of EMG-activity in the majority of recording legs muscles. The data obtained are the additional proof of existence of functionally significant neuronal interaction both between arms and between upper and lower extremities, which is evidently depend on the intraspinal neuronal connections.     

A parameter-space search algorithm tested on a Hodgkin–Huxley model

We demonstrate a parameter-space search algorithm using a computational model of a single-compartment neuron with conductance-based Hodgkin-Huxley dynamics. To classify bursting (the desired behavior), we use a simple cost function whose inputs are derived from the frequency content of the neural output. Our method involves the repeated use of a stochastic gradient descent-type algorithm to locate parameter values that allow the neural model to produce bursting within a specified tolerance. We demonstrate good results, including those showing that the utility of our algorithm improves as the pre-defined allowable parameter ranges increase and that the initial approach to our method is computationally efficient.     

Reconstructing muscle activation during normal walking: a comparison of symbolic and connectionist machine learning techniques

One symbolic (rule-based inductive learning) and one connectionist (neural network) machine learning technique were used to reconstruct muscle activation patterns from kinematic data measured during normal human walking at several speeds. The activation patterns (or desired outputs) consisted of surface electromyographic (EMG) signals from the semitendinosus and vastus medialis muscles. The inputs consisted of flexion and extension angles measured at the hip and knee of the ipsilateral leg, their first and second derivatives, and bilateral foot contact information. The training set consisted of data from six trials, at two different speeds. The testing set consisted of data from two additional trials (one at each speed), which were not in the training set. It was possible to reconstruct the muscular activation at both speeds using both techniques. Timing of the reconstructed signals was accurate. The integrated value of the activation bursts was less accurate. The neural network gave a continuous output, whereas the rule-based inductive learning rule tree gave a quantised activation level. The advantage of rule-based inductive learning was that the rules used were both explicit and comprehensible, whilst the rules used by the neural network were implicit within its structure and not easily comprehended. The neural network was able to reconstruct the activation patterns of both muscles from one network, whereas two separate rule sets were needed for the rule-based technique. It is concluded that machine learning techniques, in comparison to explicit inverse muscular skeletal models, show good promise in modelling nearly cyclic movements such as locomotion at varying walking speeds. However, they do not provide insight into the biomechanics of the system, because they are not based on the biomechanical structure of the system.     

Interlimb interactions during cyclic in-phase and antiphase movements of arms and legs and their dependence on afferent influences

Coordinated arm and leg movements imply neural interactions between the rhythmic generators of the upper and lower extremities. In ten healthy subjects in the lying position, activity of the muscles of the upper and lower extremities was recorded during separate and joint cyclic movements of the arms and legs with different phase relationships between the movements of the limbs and under various conditions of the motor task. Antiphase active arm movements were characterized by higher muscle activity than during the inphase mode. The muscle activity during passive arm movements imposed by the experimentalist was significantly lower than muscle activity during passive arm movements imposed by the other arm. When loading one arm, the muscle activity in the other, passively moving, arm increased independently from the synergy of arm movements. During a motor task implementing joint antiphase movements of both upper and lower extremities, compared to a motor task implementing their joint in-phase movements, we observed a significant increase in activity in the biceps brahii muscle, the tibialis anterior muscle, and the biceps femoris muscle. Loading of arms in these motor tasks has been accompanied by increased activity in some leg muscles. An increase in the frequency of rhythmic movements resulted in a significant growth of the muscle activity of the arms and legs during their cooperative movements with a greater rate of rise in the flexor muscle activity of the arms and legs during joint antiphase movements. Thus, both the spatial organization of movements and the type of afferent influences are significant factors of interlimb interactions, which, in turn, determine the type of neural interconnections that are involved in movement regulation.     

Trunk Muscle Activation at the Initiation and Braking of Bilateral Shoulder Flexion Movements of Different Amplitudes

The aim of this study was to investigate if trunk muscle activation patterns during rapid bilateral shoulder flexions are affected by movement amplitude. Eleven healthy males performed shoulder flexion movements starting from a position with arms along sides (0°) to either 45°, 90° or 180°. EMG was measured bilaterally from transversus abdominis (TrA), obliquus internus (OI) with intra-muscular electrodes, and from rectus abdominis (RA), erector spinae (ES) and deltoideus with surface electrodes. 3D kinematics was recorded and inverse dynamics was used to calculate the reactive linear forces and torque about the shoulders and the linear and angular impulses. The sequencing of trunk muscle onsets at the initiation of arm movements was the same across movement amplitudes with ES as the first muscle activated, followed by TrA, RA and OI. All arm movements induced a flexion angular impulse about the shoulders during acceleration that was reversed during deceleration. Increased movement amplitude led to shortened onset latencies of the abdominal muscles and increased level of activation in TrA and ES. The activation magnitude of TrA was similar in acceleration and deceleration where the other muscles were specific to acceleration or deceleration. The findings show that arm movements need to be standardized when used as a method to evaluate trunk muscle activation patterns and that inclusion of the deceleration of the arms in the analysis allow the study of the relationship between trunk muscle activation and direction of perturbing torque during one and the same arm movement.     

Movement curvature planning through force field internal models

Human motion studies have focused primarily on modeling straight point-to-point reaching movements. However, many goal-directed reaching movements, such as movements directed towards oneself, are not straight but rather follow highly curved trajectories. These movements are particularly interesting to study since they are essential in our everyday life, appear early in development and are routinely used to assess movement deficits following brain lesions. We argue that curved and straight-line reaching movements are generated by a unique neural controller and that the observed curvature of the movement is the result of an active control strategy that follows the geometry of one’s body, for instance to avoid trajectories that would hit the body or yield postures close to the joint limits. We present a mathematical model that accounts for such an active control strategy and show that the model reproduces with high accuracy the kinematic features of human data during unconstrained reaching movements directed toward the head. The model consists of a nonlinear dynamical system with a single stable attractor at the target. Embodiment-related task constraints are expressed as a force field that acts on the dynamical system. Finally, we discuss the biological plausibility and neural correlates of the model’s parameters and suggest that embodiment should be considered as a main cause for movement trajectory curvature.     

Biomechanical mechanism for transitions in phase and frequency of arm and leg swing during walking

As humans increase walking speed, there are concurrent transitions in the frequency ratio between arm and leg movements from 2:1 to 1:1 and in the phase relationship between the movements of the two arms from in-phase to out-of-phase. Superharmonic resonance of a pendulum with monofrequency excitation had been proposed as a potential model for this phenomenon. In this study, an alternative model of paired pendulums with multiple-frequency excitations is explored. It was predicted that the occurrence of the concurrent transitions was a function of (1) changes in the magnitude ratio of shoulder accelerations at step and stride frequencies that accompany changes in walking speed and (2) proximity of these frequencies to the natural resonance frequencies of the arms modeled as a pair of passive pendulums. Model predictions were compared with data collected from 14 healthy young subjects who were instructed to walk on a treadmill. Walking speeds were manipulated between 0.18 and 1.52 m/s in steps of 0.22 m/s. Kinematic data for the arms and shoulders were collected using a 3D motion analysis system, and simulations were conducted in which the movements of a double-pendulum system excited by the accelerations at the suspension point were analyzed to determine the extent to which the arms acted as passive pendulums. It was confirmed that the acceleration waveforms at the shoulder are composed primarily of stride and step frequency components. Between the shoulders, the stride frequency components were out-of-phase, while the step frequency components were in-phase. The amplitude ratio of the acceleration waveform components at the step and stride frequencies changed as a function of walking speed and were associated with the occurrence of the transitions. Simulation results using these summed components as excitatory inputs to the double-pendulum system were in agreement with actual transitions in 80% of the cases. The potential role of state-dependent active muscle contraction at shoulder joints on the occurrence of the transitions was discussed. Due to the tendency of arm movements to stay in the vicinity of their primary resonance frequency, these active muscle forces were hypothesized to function as escapements that created limit cycle oscillations at the shoulders resonant frequency.     

Resonant frequencies of arms and legs identify different walking patterns

The present study is aimed at investigating changes in the coordination of arm and leg movements in young healthy subjects. It was hypothesized that with changes in walking velocity there is a change in frequency and phase coupling between the arms and the legs. In addition, it was hypothesized that the preferred frequencies of the different coordination patterns can be predicted on the basis of the resonant frequencies of arms and legs with a simple pendulum model. The kinematics of arms and legs during treadmill walking in seven healthy subjects were recorded with accelerometers in the sagittal plane at a wide range of different velocities (i.e., 0.3-1. 3m/s). Power spectral analyses revealed a statistically significant change in the frequency relation between arms and legs, i.e., within the velocity range 0.3-0.7m/s arm movement frequencies were dominantly synchronized with the step frequency, whereas from 0.8m/s onwards arm frequencies were locked onto stride frequency. Significant effects of walking speed on mean relative phase between leg and arm movements were found. All limb pairs showed a significantly more stable coordination pattern from 0.8 to 1.0m/s onwards. Results from the pendulum modelling demonstrated that for most subjects at low-velocity preferred movement frequencies of the arms are predicted by the resonant frequencies of individual arms (about 0.98Hz), whereas at higher velocities these are predicted on the basis of the resonant frequencies of the individual legs (about 0.85Hz). The results support the above-mentioned hypotheses, and suggest that different patterns of coordination, as shown by changes in frequency coupling and phase relations, can exist within the human walking mode.     

The emergence of patterned movement during late embryogenesis of Drosophila

Larval behavioral patterns arise in a gradual fashion during late embryogenesis as the innervation of the somatic musculature and connectivity within the central nervous system develops. In this paper, we describe in a quantitative manner the maturation of behavioral patterns. Early movements are locally restricted "twitches" of the body wall, involving single segments or parts of segments. These twitches occur at a low frequency and have low amplitude, reflecting weak muscle contractions. Towards later stages twitches increase in frequency and amplitude and become integrated into coordinated movements of multiple segments. Most noticeable among these is the peristaltic wave of longitudinal segmental contractions by which the larva moves forward or backward. Besides becoming more complex as development proceeds, embryonic movements also acquire a pronounced rhythm. Thus, late embryonic movements occur in bursts, with phases of frequent movement separated by phases of no movement at all; early movements show no such periodicity. These data will serve as a baseline for future studies that address the function of embryonic lethal genes controlling neuronal connectivity and larval behavior. We have analyzed behavioral abnormalities in two embryonic lethal mutations with severe neural defects, tailless (tll), which lacks the protocerebrum, and glial cells missing (gcm), in which glial cells are absent. Our results reveal prominent alterations in embryonic motility for both of these mutations, indicating that the protocerebrum and glial cells play a crucial role in the neural mechanism controlling larval movement in Drosophila.     

Learning the optimal control of coordinated eye and head movements

Various optimality principles have been proposed to explain the characteristics of coordinated eye and head movements during visual orienting behavior. At the same time, researchers have suggested several neural models to underly the generation of saccades, but these do not include online learning as a mechanism of optimization. Here, we suggest an open-loop neural controller with a local adaptation mechanism that minimizes a proposed cost function. Simulations show that the characteristics of coordinated eye and head movements generated by this model match the experimental data in many aspects, including the relationship between amplitude, duration and peak velocity in head-restrained and the relative contribution of eye and head to the total gaze shift in head-free conditions. Our model is a first step towards bringing together an optimality principle and an incremental local learning mechanism into a unified control scheme for coordinated eye and head movements.