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Motor control is an area of natural science exploring how the nervous system interacts with other body parts and the environment to produce purposeful, coordinated actions. A central problem of motor control—the problem of motor redundancy—was formulated by Nikolai Bernstein as the problem of elimination of redundant degrees-of-freedom. Traditionally, this problem has been addressed using optimization methods based on a variety of cost functions. This review draws attention to a body of recent findings suggesting that the problem has been formulated incorrectly. An alternative view has been suggested as the principle of abundance, which considers the apparently redundant degrees-of-freedom as useful and even vital for many aspects of motor behavior. Over the past 10 years, dozens of publications have provided support for this view based on the ideas of synergic control, computational apparatus of the uncontrolled manifold hypothesis, and the equilibrium-point (referent configuration) hypothesis. In particular, large amounts of “good variance”—variance in the space of elements that has no effect on the overall performance—have been documented across a variety of natural actions. “Good variance” helps an abundant system to deal with secondary tasks and unexpected perturbations; its amount shows adaptive modulation across a variety of conditions. These data support the view that there is no problem of motor redundancy; there is bliss of motor abundance.

Classical studies of mammalian movement control define a prominent role for the primary motor cortex. Investigating the mouse whisker system, we found an additional and equally direct pathway for cortical motor control driven by the primary somatosensory cortex. Whereas activity in primary motor cortex directly evokes exploratory whisker protraction, primary somatosensory cortex directly drives whisker retraction, providing a rapid negative feedback signal for sensorimotor integration. Motor control by sensory cortex suggests the need to reevaluate the functional organization of cortical maps.

Control systems have come to play an important role in the performance of modern vehicles with regards to meeting goals on low emissions and low fuel consumption. To achieve these goals, modeling, simulation, and analysis have become standard tools for the development of control systems in the automotive industry.

In vivo effects of transcranial direct current stimulation (tDCS) have attracted much attention nowadays as this area of research spreads to both the motor and cognitive domains. The common assumption is that the anode electrode causes an enhancement of cortical excitability during stimulation, which then lasts for a few minutes thereafter, while the cathode electrode generates the opposite effect, i.e., anodal-excitation and cathodal-inhibition effects (AeCi). Yet, this dual-polarity effect has not been observed in all tDCS studies. Here, we conducted a meta-analytical review aimed to investigate the homogeneity/heterogeneity of the effect sizes of the AeCi dichotomy in both motor and cognitive functions. The AeCi effect was found to occur quite commonly with motor investigations and rarely in cognitive studies. When the anode electrode is applied over a non-motor area, in most cases, it will cause an excitation as measured by a relevant cognitive or perceptual task; however, the cathode electrode rarely causes an inhibition. We found homogeneity in motor studies and heterogeneity in cognitive studies with the electrode’s polarity serving as a moderator that can explain the source of heterogeneity in cognitive studies. The lack of inhibitory cathodal effects might reflect compensation processes as cognitive functions are typically supported by rich brain networks. Further insights as to the polarity and domain interaction are offered, including subdivision to different classes of cognitive functions according to their likelihood of being affected by stimulation.

Key Points The central pattern generator (CPG) networks that generate relatively simple motor outputs are ideal experimental models for circuit analysis. Locomotor CPGs in the ventral spinal cord function autonomously to generate repetitive patterns of oscillatory motor activity. Recent progress has been made in identifying the neuronal components that make up the locomotor circuitry, with functional studies indicating that the locomotor CPG has a modular structure. The development and assembly of the locomotor CPG is regulated by a genetic programme that operates in the embryonic spinal cord. The merging of genetic analyses with systems approaches, coupled with new tools for imaging and regulating neuronal excitability, provides the means for a comprehensive analysis of these circuits. The emerging phylogenetic relationship between neurons in the vertebrate spinal cord is providing key insights into the structure and function of the spinal motor circuitry. Intrinsic spinal cord networks generate the rhythmic patterns of motor activity that underlie locomotion. Goulding shows how genetic analyses, coupled with classical systems neuroscience approaches, are providing new information about the cellular components and functional organization of these circuits. Neurobiologists have long sought to understand how circuits in the nervous system are organized to generate the precise neural outputs that underlie particular behaviours. The motor circuits in the spinal cord that control locomotion, commonly referred to as central pattern generator networks, provide an experimentally tractable model system for investigating how moderately complex ensembles of neurons generate select motor behaviours. The advent of novel molecular and genetic techniques coupled with recent advances in our knowledge of spinal cord development means that a comprehensive understanding of how the motor circuitry is organized and operates may be within our grasp.

Joint movement tracked by a feedback pathway For animals with multi-joint limbs, one of the daunting problems that the nervous system has to solve is how to correctly interpret and respond to sensory input induced by complex combinations of limb movements. For example, one apparently simple displacement of the shoulder could arise from an infinite number of different combinations of forces acting at the shoulder and elbow. Pruszynski et al . use neurophysiological recordings in monkeys and stimulation studies in humans to demonstrate that knowledge of limb mechanics is solved through a feedback pathway involving the primary motor cortex (M1), rather than through the feed-forward processing of motion variables, a view which has been dominant for the past 25 years. The results have implications for the design of humanoid robots and brain–machine interfaces, as well as for understanding and treating patients with motor dysfunctions such as stroke. A basic difficulty for the nervous system is integrating locally ambiguous sensory information to form accurate perceptions about the outside world 1 , 2 , 3 , 4 . This local-to-global problem is also fundamental to motor control of the arm, because complex mechanical interactions between shoulder and elbow allow a particular amount of motion at one joint to arise from an infinite combination of shoulder and elbow torques 5 . Here we show, in humans and rhesus monkeys, that a transcortical pathway through primary motor cortex (M1) resolves this ambiguity during fast feedback control. We demonstrate that single M1 neurons of behaving monkeys can integrate shoulder and elbow motion information into motor commands that appropriately counter the underlying torque within about 50 milliseconds of a mechanical perturbation. Moreover, we reveal a causal link between M1 processing and multi-joint integration in humans by showing that shoulder muscle responses occurring ∼50 milliseconds after pure elbow displacement can be potentiated by transcranial magnetic stimulation. Taken together, our results show that transcortical processing through M1 permits feedback responses to express a level of sophistication that rivals voluntary control; this provides neurophysiological support for influential theories positing that voluntary movement is generated by the intelligent manipulation of sensory feedback 6 , 7 .

Cortical circuits: learning to behave Although it is generally accepted that specific cortical circuits drive behavioural execution, the relationship between task performance and modulation within the circuit is unknown. Taking advantage of a technique that allows simultaneous activity monitoring of many neurons within the same circuit, Komiyama et al . imaged activity in two motor cortical areas in mice involved in the control of licking. In both areas there were cells that are preferentially excited in different trial types and predict different actions. These neurons were spatially intermingled. However, nearby neurons showed pronounced temporally coincident activity. These temporal correlations were particularly high for pairs of neurons with similar response types, and increased with learning. These correlations provide direct evidence for rapid changes in cortical microcircuits underlying flexible behaviour. It is generally accepted that specific neuronal circuits in the brain's cortex drive behavioural execution, but the relationship between the performance of a task and the function of a circuit is unknown. Here, this problem was tackled by using a technique that allows many neurons within the same circuit to be monitored simultaneously. The findings indicate that enhanced correlated activity in specific ensembles of neurons can identify and encode specific behavioural responses while a task is learned. Cortical neurons form specific circuits 1 , but the functional structure of this microarchitecture and its relation to behaviour are poorly understood. Two-photon calcium imaging can monitor activity of spatially defined neuronal ensembles in the mammalian cortex 2 , 3 , 4 , 5 . Here we applied this technique to the motor cortex of mice performing a choice behaviour. Head-fixed mice were trained to lick in response to one of two odours, and to withhold licking for the other odour 6 , 7 . Mice routinely showed significant learning within the first behavioural session and across sessions. Microstimulation 8 , 9 and trans-synaptic tracing 10 , 11 identified two non-overlapping candidate tongue motor cortical areas. Inactivating either area impaired voluntary licking. Imaging in layer 2/3 showed neurons with diverse response types in both areas. Activity in approximately half of the imaged neurons distinguished trial types associated with different actions. Many neurons showed modulation coinciding with or preceding the action, consistent with their involvement in motor control. Neurons with different response types were spatially intermingled. Nearby neurons (within ∼150 μm) showed pronounced coincident activity. These temporal correlations increased with learning within and across behavioural sessions, specifically for neuron pairs with similar response types. We propose that correlated activity in specific ensembles of functionally related neurons is a signature of learning-related circuit plasticity. Our findings reveal a fine-scale and dynamic organization of the frontal cortex that probably underlies flexible behaviour.

There has traditionally been a separation between voluntary control processes and the fast feedback responses which follow mechanical perturbations (i.e., stretch “reflexes”). However, a recent theory of motor control, based on optimal control, suggests that voluntary motor behavior involves the sophisticated manipulation of sensory feedback. We have recently proposed that one implication of this theory is that the long-latency stretch “reflex”, like voluntary control, should support a rich assortment of behaviors because these two processes are intimately linked through shared neural circuitry including primary motor cortex. In this review, we first describe the basic principles of optimal feedback control related to voluntary motor behavior. We then explore the functional properties of upper-limb stretch responses, with a focus on how the sophistication of the long-latency stretch response rivals voluntary control. And last, we describe the neural circuitry that underlies the long-latency stretch response and detail the evidence that primary motor cortex participates in sophisticated feedback responses to mechanical perturbations.

Key Points Spinal muscular atrophy (SMA) is caused by reduced amounts of the ubiquitously expressed survival motor neuron protein (SMN). SMN functions in RNA metabolism, but the question of which aspect of its function is disrupted to give a motor neuron disease remains unanswered. SMN functions in the assembly of Sm proteins onto small nuclear RNAs (snRNAs) during pre-mRNA splicing. It has been suggested that SMN might have a role in the assembly of other ribonucleoprotein (RNP) complexes. SMA is caused by loss or mutation of SMN1 and retention of SMN2 ,leading to low SMN levels. Proteins that carry mild missense mutations complement SMN2 to restore assembly activity and give a mild phenotype. Loss of SMN in all species results in lethality, indicating that SMN has an essential function. Animal models of SMA can be created by reducing the levels of SMN. It has been proposed that reduction of SMN levels results in an alteration of the small nuclear ribonucleoprotein (snRNP) profile. This is supported by the correlation between snRNP assembly activity and SMA severity in mice; however, a clear indication of the downstream target genes that are affected is currently lacking. SMN is found in axons of cultured cells, and a second hypothesis suggests that altered mRNA transport in axons may contribute to SMA. However, a clear indication of what SMN function is disrupted to alter mRNA transport is lacking. SMN functions in the assembly of RNPs, but it remains unresolved whether it is an axonal or an snRNP component that is disrupted in SMA. Experiments showing a clear suppression of the phenotype by manipulating a particular pathway could be used to demonstrate the crucial pathway in SMA. How a reduction in the level of a ubiquitously expressed protein, SMN, causes the motor neuron–specific deficits that characterize spinal muscular atrophy is unknown. Burghes and Beattie discuss the function of SMN and the debate concerning the crucial pathways disrupted in SMA. Many neurogenetic disorders are caused by the mutation of ubiquitously expressed genes. One such disorder, spinal muscular atrophy, is caused by loss or mutation of the survival motor neuron1 gene ( SMN1 ), leading to reduced SMN protein levels and a selective dysfunction of motor neurons. SMN, together with partner proteins, functions in the assembly of small nuclear ribonucleoproteins (snRNPs), which are important for pre-mRNA splicing. It has also been suggested that SMN might function in the assembly of other ribonucleoprotein complexes. Two hypotheses have been proposed to explain the molecular dysfunction that gives rise to spinal muscular atrophy (SMA) and its specificity to a particular group of neurons. The first hypothesis states that the loss of SMN's well-known function in snRNP assembly causes an alteration in the splicing of a specific gene (or genes). The second hypothesis proposes that SMN is crucial for the transport of mRNA in neurons and that disruption of this function results in SMA.

Climbing fibers, the projections from the inferior olive to the cerebellar cortex, carry sensorimotor error and clock signals that trigger motor learning by controlling cerebellar Purkinje cell synaptic plasticity and discharge. Purkinje cells target the deep cerebellar nuclei, which are the output of the cerebellum and include an inhibitory GABAergic projection to the inferior olive. This pathway identifies a potential closed loop in the olivo-cortico-nuclear network. Therefore, sets of Purkinje cells may phasically control their own climbing fiber afferents. Here, using in vitro and in vivo recordings, we describe a genetically modified mouse model that allows the specific optogenetic control of Purkinje cell discharge. Tetrode recordings in the cerebellar nuclei demonstrate that focal stimulations of Purkinje cells strongly inhibit spatially restricted sets of cerebellar nuclear neurons. Strikingly, such stimulations trigger delayed climbing-fiber input signals in the stimulated Purkinje cells. Therefore, our results demonstrate that Purkinje cells phasically control the discharge of their own olivary afferents and thus might participate in the regulation of cerebellar motor learning.