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.

Bidirectional recurrent learning of inverse dynamic models for robots with elastic joints: a real-time real-world implementation.

Collaborative robots, or cobots, are designed to work alongside humans and to alleviate their physical burdens, such as lifting heavy objects or performing tedious tasks. Ensuring the safety of human-robot interaction (HRI) is paramount for effective collaboration. To achieve this, it is essential to have a reliable dynamic model of the cobot that enables the implementation of torque control strategies. These strategies aim to achieve accurate motion while minimizing the amount of torque exerted by the robot. However, modeling the complex non-linear dynamics of cobots with elastic actuators poses a challenge for traditional analytical modeling techniques. Instead, cobot dynamic modeling needs to be learned through data-driven approaches, rather than analytical equation-driven modeling. In this study, we propose and evaluate three machine learning (ML) approaches based on bidirectional recurrent neural networks (BRNNs) for learning the inverse dynamic model of a cobot equipped with elastic actuators. We also provide our ML approaches with a representative training dataset of the cobot's joint positions, velocities, and corresponding torque values. The first ML approach uses a non-parametric configuration, while the other two implement semi-parametric configurations. All three ML approaches outperform the rigid-bodied dynamic model provided by the cobot's manufacturer in terms of torque precision while maintaining their generalization capabilities and real-time operation due to the optimized sample dataset size and network dimensions. Despite the similarity in torque estimation of these three configurations, the non-parametric configuration was specifically designed for worst-case scenarios where the robot dynamics are completely unknown. Finally, we validate the applicability of our ML approaches by integrating the worst-case non-parametric configuration as a controller within a feedforward loop. We verify the accuracy of the learned inverse dynamic model by comparing it to the actual cobot performance. Our non-parametric architecture outperforms the robot's default factory position controller in terms of accuracy.

Electrical coupling regulated by GABAergic nucleo-olivary afferent fibres facilitates cerebellar sensory-motor adaptation.

The inferior olivary (IO) nucleus makes up the signal gateway for several organs to the cerebellar cortex. Located within the sensory-motor-cerebellum pathway, the IO axons, i.e., climbing fibres (CFs), massively synapse onto the cerebellar Purkinje cells (PCs) regulating motor learning whilst the olivary nucleus receives negative feedback through the GABAergic nucleo-olivary​ (NO) pathway. The NO pathway regulates the electrical coupling (EC) amongst the olivary cells thus facilitating synchrony and timing. However, the involvement of this EC regulation on cerebellar adaptive behaviour is still under debate. In our study we have used a spiking cerebellar model to assess the role of the NO pathway in regulating vestibulo-ocular-reflex (VOR) adaptation. The model incorporates spike-based synaptic plasticity at multiple cerebellar sites and an electrically-coupled olivary system. The olivary system plays a central role in regulating the CF spike-firing patterns that drive the PCs, whose axons ultimately shape the cerebellar output. Our results suggest that a systematic GABAergic NO deactivation decreases the spatio-temporal complexity of the IO firing patterns thereby worsening the temporal resolution of the olivary system. Conversely, properly coded IO spatio-temporal firing patterns, thanks to NO modulation, finely shape the balance between long-term depression and potentiation, which optimises VOR adaptation. Significantly, the NO connectivity pattern constrained to the same micro-zone helps maintain the spatio-temporal complexity of the IO firing patterns through time. Moreover, the temporal alignment between the latencies found in the NO fibres and the sensory-motor pathway delay appears to be crucial for facilitating the VOR. When we consider all the above points we believe that these results predict that the NO pathway is instrumental in modulating the olivary coupling and relevant to VOR adaptation.

A cerebellar-based solution to the nondeterministic time delay problem in robotic control.

The presence of computation and transmission-variable time delays within a robotic control loop is a major cause of instability, hindering safe human-robot interaction (HRI) under these circumstances. Classical control theory has been adapted to counteract the presence of such variable delays; however, the solutions provided to date cannot cope with HRI robotics inherent features. The highly nonlinear dynamics of HRI cobots (robots intended for human interaction in collaborative tasks), together with the growing use of flexible joints and elastic materials providing passive compliance, prevent traditional control solutions from being applied. Conversely, human motor control natively deals with low power actuators, nonlinear dynamics, and variable transmission time delays. The cerebellum, pivotal to human motor control, is able to predict motor commands by correlating current and past sensorimotor signals, and to ultimately compensate for the existing sensorimotor human delay (tens of milliseconds). This work aims at bridging those inherent features of cerebellar motor control and current robotic challenges­namely, compliant control in the presence of variable sensorimotor delays. We implement a cerebellar-like spiking neural network (SNN) controller that is adaptive, compliant, and robust to variable sensorimotor delays by replicating the cerebellar mechanisms that embrace the presence of biological delays and allow motor learning and adaptation.

On Robot Compliance: A Cerebellar Control Approach.

The work presented here is a novel biological approach for the compliant control of a robotic arm in real time (RT). We integrate a spiking cerebellar network at the core of a feedback control loop performing torque-driven control. The spiking cerebellar controller provides torque commands allowing for accurate and coordinated arm movements. To compute these output motor commands, the spiking cerebellar controller receives the robot's sensorial signals, the robot's goal behavior, and an instructive signal. These input signals are translated into a set of evolving spiking patterns representing univocally a specific system state at every point of time. Spike-timing-dependent plasticity (STDP) is then supported, allowing for building adaptive control. The spiking cerebellar controller continuously adapts the torque commands provided to the robot from experience as STDP is deployed. Adaptive torque commands, in turn, help the spiking cerebellar controller to cope with built-in elastic elements within the robot's actuators mimicking human muscles (inherently elastic). We propose a natural integration of a bioinspired control scheme, based on the cerebellum, with a compliant robot. We prove that our compliant approach outperforms the accuracy of the default factory-installed position control in a set of tasks used for addressing cerebellar motor behavior: controlling six degrees of freedom (DoF) in smooth movements, fast ballistic movements, and unstructured scenario compliant movements.

RegA Plays a Key Role in Oxygen-Dependent Establishment of Persistence and in Isocitrate Lyase Activity, a Critical Determinant of In vivo Brucella suis Pathogenicity.

For aerobic human pathogens, adaptation to hypoxia is a critical factor for the establishment of persistent infections, as oxygen availability is low inside the host. The two-component system RegB/A of Brucella suis plays a central role in the control of respiratory systems adapted to oxygen deficiency, and in persistence in vivo. Using an original "in vitro model of persistence" consisting in gradual oxygen depletion, we compared transcriptomes and proteomes of wild-type and ΔregA strains to identify the RegA-regulon potentially involved in the set-up of persistence. Consecutive to oxygen consumption resulting in growth arrest, 12% of the genes in B. suis were potentially controlled directly or indirectly by RegA, among which numerous transcriptional regulators were up-regulated. In contrast, genes or proteins involved in envelope biogenesis and in cellular division were repressed, suggesting a possible role for RegA in the set-up of a non-proliferative persistence state. Importantly, the greatest number of the RegA-repressed genes and proteins, including aceA encoding the functional IsoCitrate Lyase (ICL), were involved in energy production. A potential consequence of this RegA impact may be the slowing-down of the central metabolism as B. suis progressively enters into persistence. Moreover, ICL is an essential determinant of pathogenesis and long-term interactions with the host, as demonstrated by the strict dependence of B. suis on ICL activity for multiplication and persistence during in vivo infection. RegA regulates gene or protein expression of all functional groups, which is why RegA is a key regulator of B. suis in adaptation to oxygen depletion. This function may contribute to the constraint of bacterial growth, typical of chronic infection. Oxygen-dependent activation of two-component systems that control persistence regulons, shared by several aerobic human pathogens, has not been studied in Brucella sp. before. This work therefore contributes significantly to the unraveling of persistence mechanisms in this important zoonotic pathogen.