A neural model of cerebellar learning for arm movement control: cortico-spino-cerebellar dynamics

Bioinspired Control Architecture for Adaptive and Resilient Navigation of Unmanned Underwater Vehicle in Monitoring Missions of Submerged Aquatic Vegetation Meadows

Submerged aquatic vegetation plays a fundamental role as a habitat for the biodiversity of marine species. To carry out the research and monitoring of submerged aquatic vegetation more efficiently and accurately, it is important to use advanced technologies such as underwater robots. However, when conducting underwater missions to capture photographs and videos near submerged aquatic vegetation meadows, algae can become entangled in the propellers and cause vehicle failure. In this context, a neurobiologically inspired control architecture is proposed for the control of unmanned underwater vehicles with redundant thrusters. The proposed control architecture learns to control the underwater robot in a non-stationary environment and combines the associative learning method and vector associative map learning to generate transformations between the spatial and velocity coordinates in the robot actuator. The experimental results obtained show that the proposed control architecture exhibits notable resilience capabilities while maintaining its operation in the face of thruster failures. In the discussion of the results obtained, the importance of the proposed control architecture is highlighted in the context of the monitoring and conservation of underwater vegetation meadows. Its resilience, robustness, and adaptability capabilities make it an effective tool to face challenges and meet mission objectives in such critical environments.

Impact of motor task conditions on end-point kinematics and trunk movements during goal-directed arm reach

Task conditions significantly impact human motor control. We investigated how task type, difficulty, and constraints influence the kinematics of goal-directed arm reaching. Non-disabled young adults performed two distinct goal-directed arm reaching tasks: pointing and picking up an object with chopsticks. These tasks were carried out under various conditions, including constrained and unconstrained elbow extension and two different task difficulties. We collected kinematic data using a 3-D motion capture system and analyzed the effects of different task conditions on kinematic variables using linear mixed-effects regression analysis. Our findings revealed statistically significant differences in kinematics between the two tasks. Arm reaching during the picking-up task was slower and exhibited jerkier movements compared to the pointing task. Additionally, when arm reaching was performed with constrained elbow extension, it led to slower and jerkier movements, with an increased involvement of trunk movements compared to the unconstrained condition. These findings show that complex manipulative motor tasks requiring higher hand dexterity necessitate feedback-based control of arm reaching, but simple pointing tasks requiring less hand dexterity do not. In conclusion, our study sheds light on the influence of task conditions on goal-directed arm reaching kinematics and provides valuable insights into the motor control strategies involved in different tasks.

The spinal cord facilitates cerebellar upper limb motor learning and control; inputs from neuromusculoskeletal simulation

Complex interactions between brain regions and the spinal cord (SC) govern body motion, which is ultimately driven by muscle activation. Motor planning or learning are mainly conducted at higher brain regions, whilst the SC acts as a brain-muscle gateway and as a motor control centre providing fast reflexes and muscle activity regulation. Thus, higher brain areas need to cope with the SC as an inherent and evolutionary older part of the body dynamics. Here, we address the question of how SC dynamics affects motor learning within the cerebellum; in particular, does the SC facilitate cerebellar motor learning or constitute a biological constraint? We provide an exploratory framework by integrating biologically plausible cerebellar and SC computational models in a musculoskeletal upper limb control loop. The cerebellar model, equipped with the main form of cerebellar plasticity, provides motor adaptation; whilst the SC model implements stretch reflex and reciprocal inhibition between antagonist muscles. The resulting spino-cerebellar model is tested performing a set of upper limb motor tasks, including external perturbation studies. A cerebellar model, lacking the implemented SC model and directly controlling the simulated muscles, was also tested in the same. The performances of the spino-cerebellar and cerebellar models were then compared, thus allowing directly addressing the SC influence on cerebellar motor adaptation and learning, and on handling external motor perturbations. Performance was assessed in both joint and muscle space, and compared with kinematic and EMG recordings from healthy participants. The differences in cerebellar synaptic adaptation between both models were also studied. We conclude that the SC facilitates cerebellar motor learning; when the SC circuits are in the loop, faster convergence in motor learning is achieved with simpler cerebellar synaptic weight distributions. The SC is also found to improve robustness against external perturbations, by better reproducing and modulating muscle cocontraction patterns.

Reaching and Grasping Movements in Parkinson's Disease: A Review

Parkinson's disease (PD) is known to affect the brain motor circuits involving the basal ganglia (BG) and to induce, among other signs, general slowness and paucity of movements. In upper limb movements, PD patients show a systematic prolongation of movement duration while maintaining a sufficient level of endpoint accuracy. PD appears to cause impairments not only in movement execution, but also in movement initiation and planning, as revealed by abnormal preparatory activity of motor-related brain areas. Grasping movement is affected as well, particularly in the coordination of the hand aperture with the transport phase. In the last fifty years, numerous behavioral studies attempted to clarify the mechanisms underlying these anomalies, speculating on the plausible role that the BG-thalamo-cortical circuitry may play in normal and pathological motor control. Still, many questions remain open, especially concerning the management of the speed-accuracy tradeoff and the online feedback control. In this review, we summarize the literature results on reaching and grasping in parkinsonian patients. We analyze the relevant hypotheses on the origins of dysfunction, by focusing on the motor control aspects involved in the different movement phases and the corresponding role played by the BG. We conclude with an insight into the innovative stimulation techniques and computational models recently proposed, which might be helpful in further clarifying the mechanisms through which PD affects reaching and grasping movements.

Aquatic Competencies and Drowning Prevention in Children 2–4 Years: A Systematic Review

Aquatic competencies have been proposed as a prevention strategy for children aged 2–4 years who are over-represented in drowning statistics. For this recommendation to be made, exploration of the connection between aquatic competencies and drowning is required. This review critically analyzed studies exploring aquatic competencies and their effect on drowning and/or injury severity in children 2–4 years. English language peer-reviewed literature up to 31 July 2019 was searched and the PRISMA process utilized. Data were extracted from twelve studies that fulfilled the inclusion criteria. Findings from this study included that aquatic competencies were not found to increase risk of drowning and demonstrated children aged 2–4 years are capable of developing age-appropriate aquatic competencies. Age-appropriate aquatic competencies extracted were propulsion/locomotion, flotation/buoyancy, water familiarization, submersion and water exits. The acquisition of these competencies holds benefit for the prevention of drowning. No evidence was found relating to injury severity. There was limited exploration of the relationship between aquatic competencies attainment and age-related developmental readiness. The review highlights the need for consistent measures of exposure, clarity around skills acquisition, better age-specific data (2 years vs. 3 years vs. 4 years), studies with larger sample sizes, further exploration of the dose–response relationship and consistent skill level testing across age groups. Further investigation is required to establish the efficacy of aquatic competencies as a drowning prevention intervention, as well as exploring the relationship between aquatic competencies and age-related developmental readiness. In conclusion, early evidence suggests aquatic competencies can help to reduce drowning.

Desirability, availability, credit assignment, category learning, and attention: Cognitive-emotional and working memory dynamics of orbitofrontal, ventrolateral, and dorsolateral prefrontal cortices

BACKGROUND

The prefrontal cortices play an essential role in cognitive-emotional and working memory processes through interactions with multiple brain regions.

METHODS

This article further develops a unified neural architecture that explains many recent and classical data about prefrontal function and makes testable predictions.

RESULTS

Prefrontal properties of desirability, availability, credit assignment, category learning, and feature-based attention are explained. These properties arise through interactions of orbitofrontal, ventrolateral prefrontal, and dorsolateral prefrontal cortices with the inferotemporal cortex, perirhinal cortex, parahippocampal cortices; ventral bank of the principal sulcus, ventral prearcuate gyrus, frontal eye fields, hippocampus, amygdala, basal ganglia, hypothalamus, and visual cortical areas V1, V2, V3A, V4, middle temporal cortex, medial superior temporal area, lateral intraparietal cortex, and posterior parietal cortex. Model explanations also include how the value of visual objects and events is computed, which objects and events cause desired consequences and which may be ignored as predictively irrelevant, and how to plan and act to realise these consequences, including how to selectively filter expected versus unexpected events, leading to movements towards, and conscious perception of, expected events. Modelled processes include reinforcement learning and incentive motivational learning; object and spatial working memory dynamics; and category learning, including the learning of object categories, value categories, object-value categories, and sequence categories, or list chunks.

CONCLUSION

This article hereby proposes a unified neural theory of prefrontal cortex and its functions.

A neural model of normal and abnormal learning and memory consolidation: adaptively timed conditioning, hippocampus, amnesia, neurotrophins, and consciousness

How do the hippocampus and amygdala interact with thalamocortical systems to regulate cognitive and cognitive-emotional learning? Why do lesions of thalamus, amygdala, hippocampus, and cortex have differential effects depending on the phase of learning when they occur? In particular, why is the hippocampus typically needed for trace conditioning, but not delay conditioning, and what do the exceptions reveal? Why do amygdala lesions made before or immediately after training decelerate conditioning while those made later do not? Why do thalamic or sensory cortical lesions degrade trace conditioning more than delay conditioning? Why do hippocampal lesions during trace conditioning experiments degrade recent but not temporally remote learning? Why do orbitofrontal cortical lesions degrade temporally remote but not recent or post-lesion learning? How is temporally graded amnesia caused by ablation of prefrontal cortex after memory consolidation? How are attention and consciousness linked during conditioning? How do neurotrophins, notably brain-derived neurotrophic factor (BDNF), influence memory formation and consolidation? Is there a common output path for learned performance? A neural model proposes a unified answer to these questions that overcome problems of alternative memory models.

Plasticity of Sensorimotor Networks: Multiple Overlapping Mechanisms

Redundancy is an important feature of the motor system, as abundant degrees of freedom are prominent at every level of organization across the central and peripheral nervous systems, and musculoskeletal system. This basic feature results in a system that is both flexible and robust, and which can be sustainably adapted through plasticity mechanisms in response to intrinsic organismal changes and dynamic environments. While much early work of motor system organization has focused on synaptic-based plasticity processes that are driven via experience, recent investigations of neuron-glia interactions, epigenetic mechanisms and large-scale network dynamics have revealed a plethora of plasticity mechanisms that support motor system organization across multiple, overlapping spatial and temporal scales. Furthermore, an important role of these mechanisms is the regulation of intrinsic variability. Here, we review several of these mechanisms and discuss their potential role in neurorehabilitation.

Hand-in-hand advances in biomedical engineering and sensorimotor restoration

BACKGROUND

Living in a multisensory world entails the continuous sensory processing of environmental information in order to enact appropriate motor routines. The interaction between our body and our brain is the crucial factor for achieving such sensorimotor integration ability. Several clinical conditions dramatically affect the constant body-brain exchange, but the latest developments in biomedical engineering provide promising solutions for overcoming this communication breakdown.

NEW METHOD

The ultimate technological developments succeeded in transforming neuronal electrical activity into computational input for robotic devices, giving birth to the era of the so-called brain-machine interfaces. Combining rehabilitation robotics and experimental neuroscience the rise of brain-machine interfaces into clinical protocols provided the technological solution for bypassing the neural disconnection and restore sensorimotor function.

RESULTS

Based on these advances, the recovery of sensorimotor functionality is progressively becoming a concrete reality. However, despite the success of several recent techniques, some open issues still need to be addressed.

COMPARISON WITH EXISTING METHOD(S)

Typical interventions for sensorimotor deficits include pharmaceutical treatments and manual/robotic assistance in passive movements. These procedures achieve symptoms relief but their applicability to more severe disconnection pathologies is limited (e.g. spinal cord injury or amputation).

CONCLUSIONS

Here we review how state-of-the-art solutions in biomedical engineering are continuously increasing expectances in sensorimotor rehabilitation, as well as the current challenges especially with regards to the translation of the signals from brain-machine interfaces into sensory feedback and the incorporation of brain-machine interfaces into daily activities.

A spiking network model of cerebellar Purkinje cells and molecular layer interneurons exhibiting irregular firing

While the anatomy of the cerebellar microcircuit is well-studied, how it implements cerebellar function is not understood. A number of models have been proposed to describe this mechanism but few emphasize the role of the vast network Purkinje cells (PKJs) form with the molecular layer interneurons (MLIs)—the stellate and basket cells. We propose a model of the MLI-PKJ network composed of simple spiking neurons incorporating the major anatomical and physiological features. In computer simulations, the model reproduces the irregular firing patterns observed in PKJs and MLIs in vitro and a shift toward faster, more regular firing patterns when inhibitory synaptic currents are blocked. In the model, the time between PKJ spikes is shown to be proportional to the amount of feedforward inhibition from an MLI on average. The two key elements of the model are: (1) spontaneously active PKJs and MLIs due to an endogenous depolarizing current, and (2) adherence to known anatomical connectivity along a parasagittal strip of cerebellar cortex. We propose this model to extend previous spiking network models of the cerebellum and for further computational investigation into the role of irregular firing and MLIs in cerebellar learning and function.

Physiology of clinical dysfunction of the cerebellum

Experimental and theoretical research into cerebellar function has begun to converge toward understanding the cerebellum as a "controller" in the engineering sense. The purpose of a controller is to convert high-level intent commands and information describing the current state of a system into low-level control signals suitable for maintaining or changing system behavior. The cerebellar subsystem appears to play this role for parts of the body and other parts of the brain. As with engineering controllers, fundamental functions include stabilization at a fixed posture or state, adjustment of movement or transition amplitude, facilitation of movement/transition speed and crispness of launch and braking, improvement of resistance to disturbances, coordination of control across multiple degrees of freedom, and assistance with estimation and/or prediction of current and future system states. As with adaptive engineering controllers, the cerebellar subsystem also readily tunes itself over time. At a more detailed level, many of the specific actions of cerebellar circuits can be understood in terms of proportional (P), integrator-like (I), and differentiator-like (D) signal processing which are fundamental components of many engineering control systems. This chapter presents an integrated, mechanistic view of ataxia, tremor, and several cerebellar oculomotor signs in terms of PID control and the neural centers that appear to subserve these functions. It also suggests the manner in which impairments in motor learning, perception, and cognition that are associated with cerebellar dysfunction may be viewed from a similar perspective.

DOES ABNORMAL SPINAL RECIPROCAL INHIBITION LEAD TO CO-CONTRACTION OF ANTAGONIST MOTOR UNITS? A MODELING STUDY

It is suggested that co-contraction of antagonist motor units perhaps due to abnormal disynaptic I a reciprocal inhibition is responsible for Parkinsonian rigidity. A neural model of Parkinson's disease bradykinesia is extended to incorporate the effects of spindle feedback on key cortical cells and examine the effects of dopamine depletion on spinal activities. Simulation results show that although reciprocal inhibition is reduced in DA depleted case, it doesn't lead to co-contraction of antagonist motor neurons. Implications to Parkinsonian rigidity are discussed.

ADAPTIVE CEREBELLAR SPIKING MODEL EMBEDDED IN THE CONTROL LOOP: CONTEXT SWITCHING AND ROBUSTNESS AGAINST NOISE

This work evaluates the capability of a spiking cerebellar model embedded in different loop architectures (recurrent, forward, and forward&recurrent) to control a robotic arm (three degrees of freedom) using a biologically-inspired approach. The implemented spiking network relies on synaptic plasticity (long-term potentiation and long-term depression) to adapt and cope with perturbations in the manipulation scenario: changes in dynamics and kinematics of the simulated robot. Furthermore, the effect of several degrees of noise in the cerebellar input pathway (mossy fibers) was assessed depending on the employed control architecture. The implemented cerebellar model managed to adapt in the three control architectures to different dynamics and kinematics providing corrective actions for more accurate movements. According to the obtained results, coupling both control architectures (forward&recurrent) provides benefits of the two of them and leads to a higher robustness against noise.

Cerebellarlike corrective model inference engine for manipulation tasks

This paper presents how a simple cerebellumlike architecture can infer corrective models in the framework of a control task when manipulating objects that significantly affect the dynamics model of the system. The main motivation of this paper is to evaluate a simplified bio-mimetic approach in the framework of a manipulation task. More concretely, the paper focuses on how the model inference process takes place within a feedforward control loop based on the cerebellar structure and on how these internal models are built up by means of biologically plausible synaptic adaptation mechanisms. This kind of investigation may provide clues on how biology achieves accurate control of non-stiff-joint robot with low-power actuators which involve controlling systems with high inertial components. This paper studies how a basic temporal-correlation kernel including long-term depression (LTD) and a constant long-term potentiation (LTP) at parallel fiber-Purkinje cell synapses can effectively infer corrective models. We evaluate how this spike-timing-dependent plasticity correlates sensorimotor activity arriving through the parallel fibers with teaching signals (dependent on error estimates) arriving through the climbing fibers from the inferior olive. This paper addresses the study of how these LTD and LTP components need to be well balanced with each other to achieve accurate learning. This is of interest to evaluate the relevant role of homeostatic mechanisms in biological systems where adaptation occurs in a distributed manner. Furthermore, we illustrate how the temporal-correlation kernel can also work in the presence of transmission delays in sensorimotor pathways. We use a cerebellumlike spiking neural network which stores the corrective models as well-structured weight patterns distributed among the parallel fibers to Purkinje cell connections.

Cortical network modeling for inverse kinematic computation of an anthropomorphic finger

The performance of reaching movements to visual targets requires complex kinematic mechanisms such as redundant, multijointed, anthropomorphic actuators and thus is a difficult problem since the relationship between sensory and motor coordinates is highly nonlinear. In this article, we present a neural model able to learn the inverse kinematics of a simulated anthropomorphic robot finger (ShadowHand™ finger) having four degrees of freedom while performing 3D reaching movements. The results revealed that this neural model was able to control accurately and robustly the finger when performing single 3D reaching movements as well as more complex patterns of motion while generating kinematics comparable to those observed in human. The long term goal of this research is to design a bio-mimetic controller providing adaptive, robust and flexible control of dexterous robotic/prosthetics hands.

Performance limitations from delay in human and mechanical motor control

We discuss natural limitations on motor performance caused by the time delay required for feedback signals to propagate within the human body or mechanical control systems. By considering a very simple delayed linear servomechanism model, we show there exists a best possible speed-accuracy trade-off similar to Fitts' law that cannot be exceeded when delay is present. This is strictly a delay effect and does not occur for the ideal case of instantaneous feedback. We then examine the performance of the vector integration to endpoint (VITE) circuit as a model of human movement and show that when this circuit is generalized to include delayed feedback the performance may not exceed that of the servomechanism with an equal delay. We suggest the existence of such a limitation may be a ubiquitous consequence of delay in motor control with the implication that the index of performance in Fitts' law cannot arbitrarily large.

A real-time spiking cerebellum model for learning robot control

We describe a neural network model of the cerebellum based on integrate-and-fire spiking neurons with conductance-based synapses. The neuron characteristics are derived from our earlier detailed models of the different cerebellar neurons. We tested the cerebellum model in a real-time control application with a robotic platform. Delays were introduced in the different sensorimotor pathways according to the biological system. The main plasticity in the cerebellar model is a spike-timing dependent plasticity (STDP) at the parallel fiber to Purkinje cell connections. This STDP is driven by the inferior olive (IO) activity, which encodes an error signal using a novel probabilistic low frequency model. We demonstrate the cerebellar model in a robot control system using a target-reaching task. We test whether the system learns to reach different target positions in a non-destructive way, therefore abstracting a general dynamics model. To test the system's ability to self-adapt to different dynamical situations, we present results obtained after changing the dynamics of the robotic platform significantly (its friction and load). The experimental results show that the cerebellar-based system is able to adapt dynamically to different contexts.

SOVEREIGN: An autonomous neural system for incrementally learning planned action sequences to navigate towards a rewarded goal

How do reactive and planned behaviors interact in real time? How are sequences of such behaviors released at appropriate times during autonomous navigation to realize valued goals? Controllers for both animals and mobile robots, or animats, need reactive mechanisms for exploration, and learned plans to reach goal objects once an environment becomes familiar. The SOVEREIGN (Self-Organizing, Vision, Expectation, Recognition, Emotion, Intelligent, Goal-oriented Navigation) animat model embodies these capabilities, and is tested in a 3D virtual reality environment. SOVEREIGN includes several interacting subsystems which model complementary properties of cortical What and Where processing streams and which clarify similarities between mechanisms for navigation and arm movement control. As the animat explores an environment, visual inputs are processed by networks that are sensitive to visual form and motion in the What and Where streams, respectively. Position-invariant and size-invariant recognition categories are learned by real-time incremental learning in the What stream. Estimates of target position relative to the animat are computed in the Where stream, and can activate approach movements toward the target. Motion cues from animat locomotion can elicit head-orienting movements to bring a new target into view. Approach and orienting movements are alternately performed during animat navigation. Cumulative estimates of each movement are derived from interacting proprioceptive and visual cues. Movement sequences are stored within a motor working memory. Sequences of visual categories are stored in a sensory working memory. These working memories trigger learning of sensory and motor sequence categories, or plans, which together control planned movements. Predictively effective chunk combinations are selectively enhanced via reinforcement learning when the animat is rewarded. Selected planning chunks effect a gradual transition from variable reactive exploratory movements to efficient goal-oriented planned movement sequences. Volitional signals gate interactions between model subsystems and the release of overt behaviors. The model can control different motor sequences under different motivational states and learns more efficient sequences to rewarded goals as exploration proceeds.

Reward prediction error computation in the pedunculopontine tegmental nucleus neurons

In this article, we address the role of neuronal activity in the pathways of the brainstem-midbrain circuit in reward and the basis for believing that this circuit provides advantages over previous reinforcement learning theory. Several lines of evidence support the reward-based learning theory proposing that midbrain dopamine (DA) neurons send a teaching signal (the reward prediction error signal) to control synaptic plasticity of the projection area. However, the underlying mechanism of where and how the reward prediction error signal is computed still remains unclear. Since the pedunculopontine tegmental nucleus (PPTN) in the brainstem is one of the strongest excitatory input sources to DA neurons, we hypothesized that the PPTN may play an important role in activating DA neurons and reinforcement learning by relaying necessary signals for reward prediction error computation to DA neurons. To investigate the involvement of the PPTN neurons in computation of reward prediction error, we used a visually guided saccade task (VGST) during recording of neuronal activity in monkeys. Here, we predict that PPTN neurons may relay the excitatory component of tonic reward prediction and phasic primary reward signals, and derive a new computational theory of the reward prediction error in DA neurons.

Neural dynamics of autistic behaviors: cognitive, emotional, and timing substrates

What brain mechanisms underlie autism, and how do they give rise to autistic behavioral symptoms? This article describes a neural model, called the Imbalanced Spectrally Timed Adaptive Resonance Theory (iSTART) model, that proposes how cognitive, emotional, timing, and motor processes that involve brain regions such as the prefrontal and temporal cortex, amygdala, hippocampus, and cerebellum may interact to create and perpetuate autistic symptoms. These model processes were originally developed to explain data concerning how the brain controls normal behaviors. The iSTART model shows how autistic behavioral symptoms may arise from prescribed breakdowns in these brain processes, notably a combination of underaroused emotional depression in the amygdala and related affective brain regions, learning of hyperspecific recognition categories in the temporal and prefrontal cortices, and breakdowns of adaptively timed attentional and motor circuits in the hippocampal system and cerebellum. The model clarifies how malfunctions in a subset of these mechanisms can, through a systemwide vicious circle of environmentally mediated feedback, cause and maintain problems with them all.

Relearning toward motor recovery in stroke, spinal cord injury, and cerebral palsy: a cognitive neural systems perspective

In stroke, spinal cord injury, and cerebral palsy there is denervation of target neuron centers, which are self-organizing maps (SOMs) within the neuraxis. Compensatory reinnervation occurs within those SOMs by acquiring synaptic sprouts from whatever neurons in the neighborhood. Such reorganizations are more often maladaptive than beneficial. Motor recovery, if any appears, is incomplete and compromised. Cognitive systems studies indicate that motor paralysis is due to loss of learning <--> recall balance in those compensated SOMs, which had been locked into a stability <--> plasticity dilemma. Treatment/rehabilitation should aim therefore to first restore this learning related balance. The use of botulinum toxin as a neuromotor relearning tool to improve motor recovery is discussed.

A neural network model of Parkinson's disease bradykinesia

Parkinson's disease (PD) is caused by dopamine (DA) depletion consequent to cell degeneration in the substantia nigra pars compacta (SNc) and the ventral tegmental area (VTA). Although computational analyses of PD have focused on DA depletion in DA-recipient parts of the basal ganglia, there is also extensive DAergic innervation of the frontal and parietal cortex as well as the spinal cord. To understand PD bradykinesia, a comprehensive network model is needed to study how patterns of DA depletion at key cellular sites in the basal ganglia, cortex and spinal cord contribute to disordered neuronal and spinal cord activity and other PD symptoms. We extend a basal ganglia-cortico-spinal circuit for control of voluntary arm movements by incorporating DAergic innervation of cells in the cortical and spinal components of the circuit. The resultant model simulates successfully several of the main reported effects of DA depletion on neuronal, electromyographic (EMG), and movement parameters of PD bradykinesia.

Coupling of hand and foot voluntary oscillations in patients suffering cerebellar ataxia: different effect of lateral or medial lesions on coordination

Motor coordination has been investigated in seven ataxic patients who underwent surgery of the cerebellar hemisphere (4) or of the vermis-paravermis region (3). Subjects, tested ipsilaterally to the lesion, were asked to couple in-phase rhythmic oscillations of the prone hand and the ipsilateral foot for at least 10 s. The oscillation frequency, paced by a metronome, ranged 0.8-3 Hz. Hand and foot angular displacements were measured by a potentiometric technique; EMG from Extensor Carpi Radialis and Tibialis Anterior was recorded by surface electrodes. The phase-relations between the hand and foot movements, as well as between the onsets of motor commands, were calculated. For each of the limbs the frequency-response curve was estimated by plotting the mean phase values between the onset of the motor command and the onset of the related movement. The experiment was repeated with the same schedule after a strong artificial increase of the hand inertial momentum (15 g m(2)). In the unloaded condition, all patients failed to achieve a hand-foot synchrony (0 degrees ), the hand movement showing a net phase-lag. In four hemispheric and one vermian patients (group 1) this lag progressively grew with frequency up to 110 degrees , in the other two vermian patients (group 2) the hand lag kept almost constant ( approximately 45 degrees ). Group 1 subjects were unable to adequate the delay between the motor commands to the increase in frequency, as instead did group 2 subjects, although this was insufficient to produce movement synchrony. Subjects reacted to hand loading with different strategies. In group 1, due to the net increase of hand inertia, movement synchrony required a strong advance of the hand motor command. Patients succeeded in this, but because of their inability to compensate for changes in frequency, they still produced a progressive lag between movements. In group 2, loading strongly increased the hand dynamic stiffness while it slightly lowered that of the foot, resulting in a rather small difference between mechanical properties of the limbs. Thus, compensation required only a slight anticipatory activation of the hand motor command. Patients failed to do so, however they were able to adjust the command delay to the required frequency and produced a constant hand lag. Their main motor handicap was found to to be the incapability of judging the hand lag as a lack of synchrony. These results seems to indicate that the cerebellum must be involved both in measuring the time difference between hand and foot movements and in weighting this delay in function of the oscillation frequency. These two processes may be confined to the vermis-paravermis region and to the hemisphere, respectively.

From parallel sequence representations to calligraphic control: a conspiracy of neural circuits

Calligraphic writing presents many challenges for motor control, including: learning and recall of stroke sequences; critical timing of stroke onsets and durations; fine control of grip and contact forces; and letterform invariance under size scaling, which entails fine control of stroke directions and amplitudes during recruitment and derecruitment of musculoskeletal degrees of freedom. Experimental and computational studies in behavioral neuroscience have progressed toward explaining the learning, planning, and control exercised in tasks that share features with calligraphic writing and drawing. This article highlights component operations ranging from parallel sequence representations to fine force control. Treated in succession are: competitive queuing models of sequence representation, performance, learning, and recall; letter size scaling and motor equivalence; cursive handwriting models in which sensory-motor transformations are performed by circuits that learn inverse differential kinematic mappings; and fine-grained control of timing and transient forces by circuit models that learn to solve inverse dynamics problems.

A quantitative evaluation of the AVITEWRITE model of handwriting learning

Much sensory-motor behavior develops through imitation, as during the learning of handwriting by children. Such complex sequential acts are broken down into distinct motor control synergies, or muscle groups, whose activities overlap in time to generate continuous, curved movements that obey an inverse relation between curvature and speed. The adaptive vector integration to endpoint handwriting (AVITEWRITE) model of Grossberg and Paine (2000) [A neural model of corticocerebellar interactions during attentive imitation and predictive learning of sequential handwriting movements. Neural Networks, 13, 999-1046] addressed how such complex movements may be learned through attentive imitation. The model suggested how parietal and motor cortical mechanisms, such as difference vector encoding, interact with adaptively-timed, predictive cerebellar learning during movement imitation and predictive performance. Key psychophysical and neural data about learning to make curved movements were simulated, including a decrease in writing time as learning progresses; generation of unimodal, bell-shaped velocity profiles for each movement synergy; size scaling with isochrony, and speed scaling with preservation of the letter shape and the shapes of the velocity profiles; an inverse relation between curvature and tangential velocity; and a two-thirds power law relation between angular velocity and curvature. However, the model learned from letter trajectories of only one subject, and only qualitative kinematic comparisons were made with previously published human data. The present work describes a quantitative test of AVITEWRITE through direct comparison of a corpus of human handwriting data with the model's performance when it learns by tracing the human trajectories. The results show that model performance was variable across the subjects, with an average correlation between the model and human data of 0.89+/-0.10. The present data from simulations using the AVITEWRITE model highlight some of its strengths while focusing attention on areas, such as novel shape learning in children, where all models of handwriting and the learning of other complex sensory-motor skills would benefit from further research.

Modelling the control of interceptive actions

In recent years, several phenomenological dynamical models have been formulated that describe how perceptual variables are incorporated in the control of motor variables. We call these short-route models as they do not address how perception-action patterns might be constrained by the dynamical properties of the sensory, neural and musculoskeletal subsystems of the human action system. As an alternative, we advocate a long-route modelling approach in which the dynamics of these subsystems are explicitly addressed and integrated to reproduce interceptive actions. The approach is exemplified through a discussion of a recently developed model for interceptive actions consisting of a neural network architecture for the online generation of motor outflow commands, based on time-to-contact information and information about the relative positions and velocities of hand and ball. This network is shown to be consistent with both behavioural and neurophysiological data. Finally, some problems are discussed with regard to the question of how the motor outflow commands (i.e. the intended movement) might be modulated in view of the musculoskeletal dynamics.

Prospective control of manual interceptive actions: comparative simulations of extant and new model constructs

Two prospective controllers of hand movements in catching-both based on required velocity control-were simulated. Under certain conditions, this required velocity control led to overshoots of the future interception point. These overshoots were absent in pertinent experiments. To remedy this shortcoming, the required velocity model was reformulated in terms of a neural network, the Vector Integration To Endpoint model, to create a Required Velocity Integration To Endpoint model. Addition of a parallel relative velocity channel, resulting in the Relative and Required Velocity Integration To Endpoint model, provided a better account for the experimentally observed kinematics than the existing, purely behavioral models. Simulations of reaching to intercept decelerating and accelerating objects in the presence of background motion were performed to make distinct predictions for future experiments.

A neural model of cortico-cerebellar interactions during attentive imitation and predictive learning of sequential handwriting movements

Much sensory-motor behavior develops through imitation, as during the learning of handwriting by children. Such complex sequential acts are broken down into distinct motor control synergies, or muscle groups, whose activities overlap in time to generate continuous, curved movements that obey an inverse relation between curvature and speed. How are such complex movements learned through attentive imitation? Novel movements may be made as a series of distinct segments, but a practiced movement can be made smoothly, with a continuous, often bell-shaped, velocity profile. How does learning of complex movements transform reactive imitation into predictive, automatic performance? A neural model is developed which suggests how parietal and motor cortical mechanisms, such as difference vector encoding, interact with adaptively timed, predictive cerebellar learning during movement imitation and predictive performance. To initiate movement, visual attention shifts along the shape to be imitated and generates vector movement using motor cortical cells. During such an imitative movement, cerebellar Purkinje cells with a spectrum of delayed response profiles sample and learn the changing directional information and, in turn, send that learned information back to the cortex and eventually to the muscle synergies involved. If the imitative movement deviates from an attentional focus around a shape to be imitated, the visual system shifts attention, and may make an eye movement, back to the shape, thereby providing corrective directional information to the arm movement system. This imitative movement cycle repeats until the cortico-cerebellar system can accurately drive the movement based on memory alone. A cortical working memory buffer transiently stores the cerebellar output and releases it at a variable rate, allowing speed scaling of learned movements which is limited by the rate of cerebellar memory readout. Movements can be learned at variable speeds if the density of the spectrum of delayed cellular responses in the cerebellum varies with speed. Learning at slower speeds facilitates learning at faster speeds. Size can be varied after learning while keeping the movement duration constant (isochrony). Context-effects arise from the overlap of cerebellar memory outputs. The model is used to simulate key psychophysical and neural data about learning to make curved movements, including a decrease in writing time as learning progresses; generation of unimodal, bell-shaped velocity profiles for each movement synergy; size and speed scaling with preservation of the letter shape and the shapes of the velocity profiles; an inverse relation between curvature and tangential velocity; and a Two-Thirds Power Law relation between angular velocity and curvature.

The imbalanced brain: from normal behavior to schizophrenia

An outstanding problem in psychiatry concerns how to link discoveries about the pharmacological, neurophysiological, and neuroanatomical substrates of mental disorders to the abnormal behaviors that they control. A related problem concerns how to understand abnormal behaviors on a continuum with normal behaviors. During the past few decades, neural models have been developed of how normal cognitive and emotional processes learn from the environment, focus attention and act upon motivationally important events, and cope with unexpected events. When arousal or volitional signals in these models are suitably altered, they give rise to symptoms that strikingly resemble negative and positive symptoms of schizophrenia, including flat affect, impoverishment of will, attentional problems, loss of a theory of mind, thought derailment, hallucinations, and delusions. This article models how emotional centers of the brain, such as the amygdala, interact with sensory and prefrontal cortices (notably ventral, or orbital, prefrontal cortex) to generate affective states, attend to motivationally salient sensory events, and elicit motivated behaviors. Closing this feedback loop between cognitive and emotional centers is predicted to generate a cognitive-emotional resonance that can support conscious awareness. When such emotional centers become depressed, negative symptoms of schizophrenia emerge in the model. Such emotional centers are modeled as opponent affective processes, such as fear and relief, whose response amplitude and sensitivity are calibrated by an arousal level and chemical transmitters that slowly inactivate, or habituate, in an activity-dependent way. These opponent processes exhibit an Inverted-U, whereby behavior becomes depressed if the arousal level is chosen too large or too small. The negative symptoms are owing to the way in which the depressed opponent process interacts with other circuits throughout the brain.

Model of the control of saccades by superior colliculus and cerebellum

Experimental evidence indicates that the superior colliculus (SC) is important but neither necessary nor sufficient to produce accurate saccadic eye movements. Furthermore both clinical and experimental evidence points to the cerebellum as an indispensable component of the saccadic system. Accordingly, we have devised a new model of the saccadic system in which the characteristics of saccades are determined by the cooperation of two pathways, one through the SC and the other through the cerebellum. Both pathways are influenced by feedback information: the feedback determines the decay of activity for collicular neurons and the timing of the activation for cerebellar neurons. We have modeled three types of cells (burst, buildup, and fixation neurons) found in the intermediate layers of the superior colliculus. We propose that, from the point of view of motor execution, the burst neurons and the buildup neurons are not functionally distinct with both providing a directional drive to the brain stem circuitry. The fixation neurons determine the onset of the saccade by disfacilitating the omnipause neurons in the brain stem. Excluding noise-related variations, the ratio of the horizontal to the vertical components of the collicular drive is fixed throughout the saccade (i.e., its direction is fixed); the duration of the drive is such that it always would produce hypermetric movements. The cerebellum plays three roles: first, it provides an additional directional drive, which improves the acceleration of the eyes; second, it keeps track of the progress of the saccade toward the target; and third, it ends the saccade by choking off the collicular drive. The drive provided by the cerebellum can be adjusted in direction to exert a directional control over the saccadic trajectory. We propose here a control mechanism that incorporates a spatial displacement integrator in the cerebellum; under such conditions, we show that a partial directional control arises automatically. Our scheme preserves the advantages of several previous models of the saccadic system (e.g., the lack of a spatial-to-temporal transformation between the SC and the brain stem; the use of efference copy feedback to control the saccade), without incurring many of their drawbacks, and it accounts for a large amount of experimental data.

A vector-integration-to-endpoint model for performance of viapoint movements

Viapoint (VP) movements are movements to a desired point that are constrained to pass through an intermediate point. Studies have shown that VP movements possess properties, such as smooth curvature around the VP, that are not explicable by treating VP movements as strict concatenations of simpler point-to-point (PTP) movements. Such properties have led some theorists to propose whole-trajectory optimization models, which imply that the entire trajectory is precomputed before movement initiation. This paper reports new experiments conducted to systematically compare VP with PTP trajectories. Analyses revealed a statistically significant early directional deviation in VP movements but no associated curvature change. An explanation of this effect is offered by extending the vector-integration-to-endpoint (VITE) model (Bullock, D., & Grossberg, S. (1988a). Neural dynamics of planned arm movements: Emergent invariants and speed-accuracy properties during trajectory formation. Psychological Review, 95, 49-90; Bullock, D., & Grossberg, S. (1988b). The VITE model: A neural command circuit for generating arm and articulator trajectories. In J.A.S. Kelso, A.J. Mandell & M.F. Schlesinger (Eds.), Dynamic patterns in complex systems (pp. 305-326). Singapore: World Scientific Publishers.), which postulates that voluntary movement trajectories emerge as internal gating signals control the integration of continuously computed vector commands based on the evolving, perceptible difference between desired and actual position variables. The model explains the observed trajectories of VP and PTP movements as emergent properties of a dynamical system that does not precompute entire trajectories before movement initiation. The new model includes a working memory and a stage sensitive to time-to-contact information. These cooperate to control serial performance. The structural and functional relationships proposed in the model are consistent with available data on forebrain physiology and anatomy.

Retaining the equilibrium point hypothesis as an abstract description of the neuromuscular system

The lambda version of the equilibrium point (EP) hypothesis for motor control is examined in light of recent criticisms of its various instantiations. Four important assumptions that have formed the basis for recent criticism are analyzed: First, the assumption that intact muscles possess invariant force-length characteristics (ICs). Second, that these ICs are of the same form in agonist-antagonist pairs. Third, that muscle control is monoparametric and that the control parameter, lambda, can be given a neurophysiological interpretation. Fourth, that reflex loop time delays and the known, asymmetric, nonlinear mechanical properties of muscles can be ignored. Mechanical and neurophysiological investigations of the neuromuscular system suggests that none of these assumptions is likely to be correct. This has been taken to mean that the EP hypothesis is oversimplified and a new approach is needed. It is argued that such an approach can be provided without rejecting the EP hypothesis, rather to regard it as an input-output description of muscle and associated segmental circuits. The operation of the segmental circuitry can be interpreted as having the function, at least in part, of compensating for a variety of nonlinearities and asymmetries such that the overall system implements the lambda-EP model equations.

Distributed model of control of saccades by superior colliculus and cerebellum

We investigate the role that superior colliculus (SC) and cerebellum (CBLM) might play in controlling saccadic eye movements. Even though strong experimental evidence argues for an important role for the CBLM, the most recent models of the saccadic system have relied mostly on the SC for the dynamic control of saccades. In this study, we propose that saccades are controlled by two parallel pathways, one including the SC and the other including the CBLM. In this model, both SC and CBLM provide part of the drive to the saccade. Furthermore, the CBLM receives direct feedback from the brain stem and keeps track of the residual motor error, so that it can issue appropriate commands to compensate for incorrect heading and to end the movement when the target has been foveated. We present here a distributed model that produces realistic saccades and accounts for a great deal of neurophysiological data.