A neural network model for the intersensory coordination involved in goal-directed movements

A neural network model for a sensorimotor system, which was developed to simulate oriented movements in man, is presented. It is composed of a formal neural network comprising two layers: a sensory layer receiving and processing sensory inputs, and a motor layer driving a simulated arm. The sensory layer is an extension of the topological network previously proposed by Kohonen (1984). Two kinds of sensory modality, proprioceptive and exteroceptive, are used to define the arm position. Each sensory cell receives proprioceptive inputs provided by each arm-joint together with the exteroceptive inputs. This sensory layer is therefore a kind of associative layer which integrates two separate sensory signals relating to movement coding. It is connected to the motor layer by means of adaptive synapses which provide a physical link between a motor activity and its sensory consequences. After a learning period, the spatial map which emerges in the sensory layer clearly depends on the sensory inputs and an associative map of both the arm and the extra-personal space is built up if proprioceptive and exteroceptive signals are processed together. The sensorimotor transformations occuring in the junctions linking the sensory and motor layers are organized in such a manner that the simulated arm becomes able to reach towards and track a target in extra-personal space. Proprioception serves to determine the final arm posture adopted and to correct the ongoing movement in cases where changes in the target location occur. With a view to developing a sensorimotor control system with more realistic salient features, a robotic model was coupled with the formal neural network. This robotic implementation of our model shows the capacity of formal neural networks to control the displacement of mechanical devices.     

Tactile Gap Detection Deteriorates during Bimanual Symmetrical Movements under Mirror Visual Feedback

It has been suggested that incongruence between signals for motor intention and sensory input can cause pain and other sensory abnormalities. This claim is supported by reports that moving in an environment of induced sensorimotor conflict leads to elevated pain and sensory symptoms in those with certain painful conditions. Similar procedures can lead to reports of anomalous sensations in healthy volunteers too. In the present study, we used mirror visual feedback to investigate the effects of sensorimotor incongruence on responses to stimuli that arise from sources external to the body, in particular, touch. Incongruence between the sensory and motor signals for the right arm was manipulated by having the participants make symmetrical or asymmetrical movements while watching a reflection of their left arm in a parasagittal mirror, or the left hand surface of a similarly positioned opaque board. In contrast to our prediction, sensitivity to the presence of gaps in tactile stimulation of the right forearm was not reduced when participants made asymmetrical movements during mirror visual feedback, as compared to when they made symmetrical or asymmetrical movements with no visual feedback. Instead, sensitivity was reduced when participants made symmetrical movements during mirror visual feedback relative to the other three conditions. We suggest that small discrepancies between sensory and motor information, as they occur during mirror visual feedback with symmetrical movements, can impair tactile processing. In contrast, asymmetrical movements with mirror visual feedback may not impact tactile processing because the larger discrepancies between sensory and motor information may prevent the integration of these sources of information. These results contrast with previous reports of anomalous sensations during exposure to both low and high sensorimotor conflict, but are nevertheless in agreement with a forward model interpretation of perceptual modulations during goal directed movement.     

A Real-Time Brain-Machine Interface Combining Motor Target and Trajectory Intent Using an Optimal Feedback Control Design

Real-time brain-machine interfaces (BMI) have focused on either estimating the continuous movement trajectory or target intent. However, natural movement often incorporates both. Additionally, BMIs can be modeled as a feedback control system in which the subject modulates the neural activity to move the prosthetic device towards a desired target while receiving real-time sensory feedback of the state of the movement. We develop a novel real-time BMI using an optimal feedback control design that jointly estimates the movement target and trajectory of monkeys in two stages. First, the target is decoded from neural spiking activity before movement initiation. Second, the trajectory is decoded by combining the decoded target with the peri-movement spiking activity using an optimal feedback control design. This design exploits a recursive Bayesian decoder that uses an optimal feedback control model of the sensorimotor system to take into account the intended target location and the sensory feedback in its trajectory estimation from spiking activity. The real-time BMI processes the spiking activity directly using point process modeling. We implement the BMI in experiments consisting of an instructed-delay center-out task in which monkeys are presented with a target location on the screen during a delay period and then have to move a cursor to it without touching the incorrect targets. We show that the two-stage BMI performs more accurately than either stage alone. Correct target prediction can compensate for inaccurate trajectory estimation and vice versa. The optimal feedback control design also results in trajectories that are smoother and have lower estimation error. The two-stage decoder also performs better than linear regression approaches in offline cross-validation analyses. Our results demonstrate the advantage of a BMI design that jointly estimates the target and trajectory of movement and more closely mimics the sensorimotor control system.     

Sensorimotor Learning Biases Choice Behavior: A Learning Neural Field Model for Decision Making

According to a prominent view of sensorimotor processing in primates, selection and specification of possible actions are not sequential operations. Rather, a decision for an action emerges from competition between different movement plans, which are specified and selected in parallel. For action choices which are based on ambiguous sensory input, the frontoparietal sensorimotor areas are considered part of the common underlying neural substrate for selection and specification of action. These areas have been shown capable of encoding alternative spatial motor goals in parallel during movement planning, and show signatures of competitive value-based selection among these goals. Since the same network is also involved in learning sensorimotor associations, competitive action selection (decision making) should not only be driven by the sensory evidence and expected reward in favor of either action, but also by the subject''s learning history of different sensorimotor associations. Previous computational models of competitive neural decision making used predefined associations between sensory input and corresponding motor output. Such hard-wiring does not allow modeling of how decisions are influenced by sensorimotor learning or by changing reward contingencies. We present a dynamic neural field model which learns arbitrary sensorimotor associations with a reward-driven Hebbian learning algorithm. We show that the model accurately simulates the dynamics of action selection with different reward contingencies, as observed in monkey cortical recordings, and that it correctly predicted the pattern of choice errors in a control experiment. With our adaptive model we demonstrate how network plasticity, which is required for association learning and adaptation to new reward contingencies, can influence choice behavior. The field model provides an integrated and dynamic account for the operations of sensorimotor integration, working memory and action selection required for decision making in ambiguous choice situations.     

Interference and Shaping in Sensorimotor Adaptations with Rewards

When a perturbation is applied in a sensorimotor transformation task, subjects can adapt and maintain performance by either relying on sensory feedback, or, in the absence of such feedback, on information provided by rewards. For example, in a classical rotation task where movement endpoints must be rotated to reach a fixed target, human subjects can successfully adapt their reaching movements solely on the basis of binary rewards, although this proves much more difficult than with visual feedback. Here, we investigate such a reward-driven sensorimotor adaptation process in a minimal computational model of the task. The key assumption of the model is that synaptic plasticity is gated by the reward. We study how the learning dynamics depend on the target size, the movement variability, the rotation angle and the number of targets. We show that when the movement is perturbed for multiple targets, the adaptation process for the different targets can interfere destructively or constructively depending on the similarities between the sensory stimuli (the targets) and the overlap in their neuronal representations. Destructive interferences can result in a drastic slowdown of the adaptation. As a result of interference, the time to adapt varies non-linearly with the number of targets. Our analysis shows that these interferences are weaker if the reward varies smoothly with the subject''s performance instead of being binary. We demonstrate how shaping the reward or shaping the task can accelerate the adaptation dramatically by reducing the destructive interferences. We argue that experimentally investigating the dynamics of reward-driven sensorimotor adaptation for more than one sensory stimulus can shed light on the underlying learning rules.     

Brain–computer interfaces for dissecting cognitive processes underlying sensorimotor control

Sensorimotor control engages cognitive processes such as prediction, learning, and multisensory integration. Understanding the neural mechanisms underlying these cognitive processes with arm reaching is challenging because we currently record only a fraction of the relevant neurons, the arm has nonlinear dynamics, and multiple modalities of sensory feedback contribute to control. A brain–computer interface (BCI) is a well-defined sensorimotor loop with key simplifying advantages that address each of these challenges, while engaging similar cognitive processes. As a result, BCI is becoming recognized as a powerful tool for basic scientific studies of sensorimotor control. Here, we describe the benefits of BCI for basic scientific inquiries and review recent BCI studies that have uncovered new insights into the neural mechanisms underlying sensorimotor control.     

Spatiotemporal dynamics of cortical sensorimotor integration in behaving mice

Tactile information is actively acquired and processed in the brain through concerted interactions between movement and sensation. Somatosensory input is often the result of self-generated movement during the active touch of objects, and conversely, sensory information is used to refine motor control. There must therefore be important interactions between sensory and motor pathways, which we chose to investigate in the mouse whisker sensorimotor system. Voltage-sensitive dye was applied to the neocortex of mice to directly image the membrane potential dynamics of sensorimotor cortex with subcolumnar spatial resolution and millisecond temporal precision. Single brief whisker deflections evoked highly distributed depolarizing cortical sensory responses, which began in the primary somatosensory barrel cortex and subsequently excited the whisker motor cortex. The spread of sensory information to motor cortex was dynamically regulated by behavior and correlated with the generation of sensory-evoked whisker movement. Sensory processing in motor cortex may therefore contribute significantly to active tactile sensory perception.     

Characteristics of synaptic connections between rodent primary somatosensory and motor cortices

The reciprocal connections between primary motor (M1) and primary somatosensory cortices (S1) are hypothesized to play a crucial role in the ability to update motor plans in response to changes in the sensory periphery. These interactions provide M1 with information about the sensory environment that in turn signals S1 with anticipatory knowledge of ongoing motor plans. In order to examine the synaptic basis of sensorimotor feedforward (S1–M1) and feedback (M1–S1) connections directly, we utilized whole-cell recordings in slices that preserve these reciprocal sensorimotor connections. Our findings indicate that these regions are connected via direct monosynaptic connections in both directions. Larger magnitude responses were observed in the feedforward direction (S1–M1), while the feedback (M1–S1) responses occurred at shorter latencies. The morphology as well as the intrinsic firing properties of the neurons in these pathways indicates that both excitatory and inhibitory neurons are targeted. Differences in synaptic physiology suggest that there exist specializations within the sensorimotor pathway that may allow for the rapid updating of sensory–motor processing within the cortex in response to changes in the sensory periphery.     

Characteristics of synaptic connections between rodent primary somatosensory and motor cortices

The reciprocal connections between primary motor (M1) and primary somatosensory cortices (S1) are hypothesized to play a crucial role in the ability to update motor plans in response to changes in the sensory periphery. These interactions provide M1 with information about the sensory environment that in turn signals S1 with anticipatory knowledge of ongoing motor plans. In order to examine the synaptic basis of sensorimotor feedforward (S1-M1) and feedback (M1-S1) connections directly, we utilized whole-cell recordings in slices that preserve these reciprocal sensorimotor connections. Our findings indicate that these regions are connected via direct monosynaptic connections in both directions. Larger magnitude responses were observed in the feedforward direction (S1-M1), while the feedback (M1-S1) responses occurred at shorter latencies. The morphology as well as the intrinsic firing properties of the neurons in these pathways indicates that both excitatory and inhibitory neurons are targeted. Differences in synaptic physiology suggest that there exist specializations within the sensorimotor pathway that may allow for the rapid updating of sensory-motor processing within the cortex in response to changes in the sensory periphery.     

Sensing with the motor cortex

The primary motor cortex is a critical node in the network of brain regions responsible for voluntary motor behavior. It has been less appreciated, however, that the motor cortex exhibits sensory responses in a variety of modalities including vision and somatosensation. We review current work that emphasizes the heterogeneity in sensorimotor responses in the motor cortex and focus on its implications for cortical control of movement as well as for brain-machine interface development.     

Corollary discharge across the animal kingdom

Our movements can hinder our ability to sense the world. Movements can induce sensory input (for example, when you hit something) that is indistinguishable from the input that is caused by external agents (for example, when something hits you). It is critical for nervous systems to be able to differentiate between these two scenarios. A ubiquitous strategy is to route copies of movement commands to sensory structures. These signals, which are referred to as corollary discharge (CD), influence sensory processing in myriad ways. Here we review the CD circuits that have been uncovered by neurophysiological studies and suggest a functional taxonomic classification of CD across the animal kingdom. This broad understanding of CD circuits lays the groundwork for more challenging studies that combine neurophysiology and psychophysics to probe the role of CD in perception.     

Adaptive dynamic programming as a theory of sensorimotor control

Many characteristics of sensorimotor control can be explained by models based on optimization and optimal control theories. However, most of the previous models assume that the central nervous system has access to the precise knowledge of the sensorimotor system and its interacting environment. This viewpoint is difficult to be justified theoretically and has not been convincingly validated by experiments. To address this problem, this paper presents a new computational mechanism for sensorimotor control from a perspective of adaptive dynamic programming (ADP), which shares some features of reinforcement learning. The ADP-based model for sensorimotor control suggests that a command signal for the human movement is derived directly from the real-time sensory data, without the need to identify the system dynamics. An iterative learning scheme based on the proposed ADP theory is developed, along with rigorous convergence analysis. Interestingly, the computational model as advocated here is able to reproduce the motor learning behavior observed in experiments where a divergent force field or velocity-dependent force field was present. In addition, this modeling strategy provides a clear way to perform stability analysis of the overall system. Hence, we conjecture that human sensorimotor systems use an ADP-type mechanism to control movements and to achieve successful adaptation to uncertainties present in the environment.     

Active touch, exploratory movements, and sensory prediction

The relation between somatosensory input and motor output is asymmetric. Somatosensation is associated with every movement an animal makes, but movement is not required for somatosensation. This symposium paper proposes a classification scheme for movement, in which movements are placed along a continuum that describes the role that somatosensory information plays during the movement. Fine sensorimotor control-manipulation and exploration-are found to fall to one extreme of the spectrum, and exploratory movements in particular are shown to possess characteristics that clearly distinguish them from other varieties of movement. Specifically, the exploratory process must permit animals to extract an object's features independently of the sequence of movements executed to explore the object. Based in part on our work on the rat vibrissal system, we suggest that exploration of objects may consist of two complementary levels of sensorimotor prediction operating in parallel. At the cognitive level, the animal might move so as to perform hypothesis testing about the identity or nature of the object. The particular hypothesis tests chosen by the animal might be implemented through sequences of control-level predictions that could be generated at the level of the brainstem and cerebellum.     

Characterisation of whisker control in the California sea lion (Zalophus californianus) during a complex,dynamic sensorimotor task

Studies in pinniped whisker use have shown that their whiskers are extremely sensitive to tactile and hydrodynamic signals. While pinnipeds position their whiskers on to objects and have some control over their whisker protractions, it has always been thought that head movements are more responsible for whisker positioning than the movement of the whiskers themselves. This study uses ball balancing, a dynamic sensorimotor skill that is often used in human and robotic coordination studies, to promote sea lion whisker movements during the task. For the first time, using tracked video footage, we show that sea lion whisker movements respond quickly (26.70 ms) and mirror the movement of the ball, much more so than the head. We show that whisker asymmetry and spread are both altered to help sense and control the ball during balancing. We believe that by designing more dynamic sensorimotor tasks we can start to characterise the active nature of this specialised sensory system in pinnipeds.     

Learning from sensory and reward prediction errors during motor adaptation

Voluntary motor commands produce two kinds of consequences. Initially, a sensory consequence is observed in terms of activity in our primary sensory organs (e.g., vision, proprioception). Subsequently, the brain evaluates the sensory feedback and produces a subjective measure of utility or usefulness of the motor commands (e.g., reward). As a result, comparisons between predicted and observed consequences of motor commands produce two forms of prediction error. How do these errors contribute to changes in motor commands? Here, we considered a reach adaptation protocol and found that when high quality sensory feedback was available, adaptation of motor commands was driven almost exclusively by sensory prediction errors. This form of learning had a distinct signature: as motor commands adapted, the subjects altered their predictions regarding sensory consequences of motor commands, and generalized this learning broadly to neighboring motor commands. In contrast, as the quality of the sensory feedback degraded, adaptation of motor commands became more dependent on reward prediction errors. Reward prediction errors produced comparable changes in the motor commands, but produced no change in the predicted sensory consequences of motor commands, and generalized only locally. Because we found that there was a within subject correlation between generalization patterns and sensory remapping, it is plausible that during adaptation an individual''s relative reliance on sensory vs. reward prediction errors could be inferred. We suggest that while motor commands change because of sensory and reward prediction errors, only sensory prediction errors produce a change in the neural system that predicts sensory consequences of motor commands.     

Inhibitors of protein and RNA synthesis block structural changes that accompany long-term heterosynaptic plasticity in Aplysia.

Synaptic connections between the sensory and motor neurons of Aplysia in culture undergo long-term facilitation in response to serotonin (5-HT) and long-term depression in response to FMRFamide. These long-term functional changes are dependent on the synthesis of macromolecules during the period in which the transmitter is applied and are accompanied by structural changes. There is an increase and a decrease, respectively, in the number of sensory neuron varicosities in response to 5-HT and FMRFamide. To determine whether macromolecular synthesis is also required for the structural changes, we examined in parallel the effects of inhibitors of protein (anisomycin) or RNA (actinomycin D) synthesis on the structural and functional changes. We have found that anisomycin and actinomycin D block both the enduring alterations in varicosity number and the long-lasting changes in synaptic potential. These results indicate that macromolecular synthesis is required for expression of the long-lasting structural changes in the sensory cells and that this synthesis is correlated with the long-term functional modulation of sensorimotor synapses.     

Learning in sensorimotor circuits

The study of plasticity in the central nervous system is a major and very dynamic neuroscience research field with enormous clinical potential. Considerable advances in this field have been made during the past 10 years. It now appears that most circuits in the brain and spinal cord show plasticity and that they can be modified by experience. Knowledge of the mechanisms of plasticity in the nervous system is therefore essential for the understanding of how the nervous system is wired during development and how it adapts in response to changes in the body and environment. Recent findings indicate that functional sensorimotor modules probe the sensory signals from the body that are generated as a consequence of module specific activity and use this sensory feedback to calibrate the strength in its input-output connections. This experience-dependent signal adapts the circuitry in the sensorimotor module to the body anatomy and biomechanics.     

Postsynaptic regulation of the development and long-term plasticity of Aplysia sensorimotor synapses in cell culture

The monosynaptic component of the neuronal circuit that mediates the withdrawal reflex of Aplysia californica can be reconstituted in dissociated cell culture. Study of these in vitro monosynaptic connections has yielded insights into the basic cellular mechanisms of synaptogenesis and long-term synaptic plasticity. One such insight has been that the development of the presynaptic sensory neurons is strongly regulated by the postsynaptic motor neuron. Sensory neurons which have been cocultured with a target motor neuron have more elaborate structures—characterized by neurites with more branches and varicosities—than do sensory neurons grown alone in culture or sensory neurons that have been cocultured with an inappropriate target cell. Another way in which the motor neuron regulates the development of sensory neurons is apparent when sensorimotor cocultures with two presynaptic cells are examined. In such cocultures the outgrowth from the different presynaptic cells is obviously segregated on the processes of the postsynaptic cell. By contrast, when two sensory neurons are placed into cell culture without a motor neuron, thier processes readily grow together. In addition to regulating the in vitro development of sensory neurons, the motor neuron also regulates learning-related changes in the structure of sensory neurons. Application of the endogenous facilitatory trasmitter serotonin (5-HT) causes long-term facilitation of in vitro sensorimotor synapses due in part to growth of new presynatpic varicosities. But 5-HT applied to sensory neurons alone in cultuer does not produce structural changes in these cells. More recently it has been found that sensorimotor synapses in cell culture can exhibit long-term potentiation (LTP). Like LTP of some hippocampal synapses, LTP of in vitro Aplysia syanpses is regulated by the voltage of the postsynaptic cell. Pairing high-frequency stimulation of sensory neurons with strong hyperpolarization of the motor neuron blocks the induction of LTP. Moreover, LTP of sensorimotor synapses can be induced in Hebbian fashion by pairing weak presynaptic stimulation with strong postsynaptic depolarization. These findings implicate a Habbian mechanism in classical conditioning in Aplysia. They also indicate that Hebbian LTP is a phylogenetically ancient form of synaptic plasticity. 1994 John Wiley & Sons, Inc.     

Regulation of neurotrophin signaling in aging sensory and motoneurons

A hallmark of senescence is sensorimotor impairment, involving locomotion and postural control as well as fine-tuned movements. Sensory and motoneurons are not lost to any significant degree with advancing age, but do show characteristic changes in gene-expression pattern, morphology, and connectivity. This review covers recent experimental findings corroborating that alterations in trophic signaling may induce several of the phenotypic changes seen in primary sensory and motoneurons during aging. Furthermore, the data suggests that target failure, and/or breakdown of neuron-target interaction, is a critical event in the aging process of sensory and motoneurons.