Motor Signals Research Articles

Investigating heading representation in the zebrafish interpeduncular nucleus (2024 FENS‐Kavli network of excellence PhD thesis prize)

AbstractThe brain's ability to integrate sensory and motor information allows us to maintain a sense of orientation in space, a process in which head‐direction cells play a key role. While these neurons have been studied extensively in mammals, their presence and function in non‐mammalian species remain less understood. Here, I summarize the research work for my PhD thesis, where we explore the interpeduncular nucleus (IPN) in zebrafish, a lesser known brain region, using whole‐brain electron microscopy and calcium imaging techniques. We identified a novel population of unipolar neurons, with their activity exhibiting a dynamic, rotational pattern during head movements, even in the absence of sensory cues. This population mirrors the functionality of head‐direction cells observed in mammals, suggesting a conserved mechanism for spatial orientation across vertebrates. Our findings reveal the potential of the zebrafish IPN as a vertebrate model for studying ring attractor networks, a theoretical framework previously used to explain head‐direction cell activity. These results pave the way for future research on how motor and sensory signals converge in the vertebrate brain to maintain spatial orientation.

A comparison of electromyography techniques: surface versus intramuscular recording.

This review is a comprehensive guide for electromyography (EMG) researchers, providing a comparison of skin EMG recording (surface EMG: sEMG and high-density sEMG: HD-sEMG) and intramuscular EMG recording (multi-motor unit-MMU and single motor unit electromyography-SMU). We delve into the nuances of techniques, highlighting their strengths and limitations in quantifying muscle activation during dynamic and static conditions. We first examine how EMG signals change with time, focussing on the interplay between motor unit synchronisation and signal amplitude. The review then explores the impact of electrode placement on signal quality. We further discuss the challenges of signal cancellation, crosstalk from neighbouring muscles, and motion artifacts, which can significantly affect signal integrity. Finally, we address the temporal changes in electrode impedance and its implications for data interpretation. Our analysis proposes that specific research objectives should guide the choice amongst sEMG, HD-sEMG, SMU and MMU. MMU, which records the peak counts of individual motor unit action potentials from a localised muscle area, is particularly suited for studying deep or small muscles during static and dynamic activities. Its high sensitivity to motor unit recruitment and discharge rates minimises the impact of factors such as signal cancellation and motion artefacts. Conversely, sEMG is well-suited for short-duration, isometric assessments of large, superficial muscles. HD-sEMG helps study single motor unit properties under isometric conditions. SMU is particularly suited for studying neuronal networks between stimulated sites and motor neurons. This review aims toprovideresearchers with the information to select the most appropriate EMG technique for their investigations.

Suggestion of a hybrid computational model to optimize EEG motor imagery signals classification

Brain-computer interfaces (BCIs) have proven to be innovative and promising technologies for human-machine interaction. However, distinguishing with relative precision between EEG motor signals generated by real or imagined movements of the left and right hands has still been a major challenge. By analyzing brain electrical signals and the processes of reading, processing, and interpreting data, the hybrid model suggested in this dissertation contributed to greater effectiveness of the algorithms, with accuracy above that normally found in most models present in the most recent literature. The experimental and observational research carried out in the creation of the methodology applied in this dissertation ensures the replication of automation methods, techniques, and algorithms, machine learning, and more specifically, in data preparation, feature extraction, signal classification, and obtaining results with greater accuracy for the implementation of brain-computer interfaces, BCIs. This dissertation opens new possibilities for future research and deepening of techniques, tools, and new experiments. With a premise to create personalized BCIs and trained specifically for individual pattern recognition, as they have shown greater potential results. Each human being is unique and has singular characteristics that distinguish them from all other beings.

A Cortical Site that Encodes Vocal Expression and Reception.

Socially effective vocal communication requires brain regions that encode expressive and receptive aspects of vocal communication in a social context-dependent manner. Here, we combined a novel behavioral assay with microendoscopy to interrogate neuronal activity in the posterior insula (pIns) in socially interacting mice as they switched rapidly between states of vocal expression and reception. We found that distinct but spatially intermingled subsets of pIns neurons were active during vocal expression and reception. Notably, pIns activity during vocal expression increased prior to vocal onset and was also detected in congenitally deaf mice, pointing to a motor signal. Furthermore, receptive pIns activity depended strongly on social cues, including female odorants. Lastly, tracing experiments reveal that deep layer neurons in the pIns directly bridge the auditory thalamus to a midbrain vocal gating region. Therefore, the pIns is a site that encodes vocal expression and reception in a manner that depends on social context.

Balance performance in aged mice is dependent on unipolar brush cells.

The vestibular processing regions of the cerebellum integrate vestibular information with other sensory modalities and motor signals to regulate balance, gaze stability, and spatial orientation. A class of excitatory glutamatergic interneurons known as unipolar brush cells (UBCs) are highly concentrated within the granule cell layer of these regions. UBCs receive vestibular signals directly from primary vestibular afferents and indirectly from mossy fibers. Each UBC excites numerous granule cells and could contribute to computations necessary for balance-related motor function. Prior research has implicated UBCs in motor function, but their influence on balance performance remains unclear, especially in aged mice that have age-related impairment. Here we tested whether UBCs contribute to motor coordination and balance by disrupting their activity with chemogenetics in aged and young mice. Age-related balance deficits were apparent in mice > 6 months old. Disrupting the activity of a subpopulation of UBCs caused aged mice to fall off a balance beam more frequently and altered swimming behaviors that are sensitive to vestibular dysfunction. These effects were not seen in young (7-week-old) mice. Thus, disrupting the activity of UBCs impairs mice with age-related balance issues and suggest that UBCs are essential for balance and vestibular function in aged mice.

Cell class-specific long-range axonal projections of neurons in mouse whisker-related somatosensory cortices.

Long-range axonal projections of diverse classes of neocortical excitatory neurons likely contribute to brain-wide interactions processing sensory, cognitive and motor signals. Here, we performed light-sheet imaging of fluorescently labeled axons from genetically defined neurons located in posterior primary somatosensory barrel cortex and supplemental somatosensory cortex. We used convolutional networks to segment axon-containing voxels and quantified their distribution within the Allen Mouse Brain Atlas Common Coordinate Framework. Axonal density was analyzed for different classes of glutamatergic neurons using transgenic mouse lines selectively expressing Cre recombinase in layer 2/3 intratelencephalic projection neurons (Rasgrf2-dCre), layer 4 intratelencephalic projection neurons (Scnn1a-Cre), layer 5 intratelencephalic projection neurons (Tlx3-Cre), layer 5 pyramidal tract projection neurons (Sim1-Cre), layer 5 projection neurons (Rbp4-Cre), and layer 6 corticothalamic neurons (Ntsr1-Cre). We found distinct axonal projections from the different neuronal classes to many downstream brain areas, which were largely similar for primary and supplementary somatosensory cortices. Functional connectivity maps obtained from optogenetic activation of sensory cortex and wide-field imaging revealed topographically organized evoked activity in frontal cortex with neurons located more laterally in somatosensory cortex signaling to more anteriorly located regions in motor cortex, consistent with the anatomical projections. The current methodology therefore appears to quantify brain-wide axonal innervation patterns supporting brain-wide signaling.

Cell class-specific long-range axonal projections of neurons in mouse whisker-related somatosensory cortices

Long-range axonal projections of diverse classes of neocortical excitatory neurons likely contribute to brain-wide interactions processing sensory, cognitive and motor signals. Here, we performed light-sheet imaging of fluorescently labeled axons from genetically defined neurons located in posterior primary somatosensory barrel cortex and supplemental somatosensory cortex. We used convolutional networks to segment axon-containing voxels and quantified their distribution within the Allen Mouse Brain Atlas Common Coordinate Framework. Axonal density was analyzed for different classes of glutamatergic neurons using transgenic mouse lines selectively expressing Cre recombinase in layer 2/3 intratelencephalic projection neurons (Rasgrf2-dCre), layer 4 intratelencephalic projection neurons (Scnn1a-Cre), layer 5 intratelencephalic projection neurons (Tlx3-Cre), layer 5 pyramidal tract projection neurons (Sim1-Cre), layer 5 projection neurons (Rbp4-Cre), and layer 6 corticothalamic neurons (Ntsr1-Cre). We found distinct axonal projections from the different neuronal classes to many downstream brain areas, which were largely similar for primary and supplementary somatosensory cortices. Functional connectivity maps obtained from optogenetic activation of sensory cortex and wide-field imaging revealed topographically organized evoked activity in frontal cortex with neurons located more laterally in somatosensory cortex signaling to more anteriorly located regions in motor cortex, consistent with the anatomical projections. The current methodology therefore appears to quantify brain-wide axonal innervation patterns supporting brain-wide signaling.

Fault diagnosis for multiple redundancy aileron actuator based on parallel-SDP and polar sparse representation

Abstract As a critical component of aircraft flight control systems, the aileron actuator's fault directly affects the performance of flight control performance system and the overall flight safety of aircraft. Therefore, effective fault diagnosis of the aileron actuator becomes particularly important. However, with the development of redundancy design, the structure of aileron actuators more and more complex, and the fault modes are more and more diverse, which increases the difficulty of fault diagnosis significantly. To address this challenge, this study proposes a fault diagnosis method based on Parallel-SDP and Polar Sparse Representation.
First, the current residual signals of force motors are obtained using bi-step observer. Then, the residual signals are transformed into Parallel-SDP images, where each of the four petals of Parallel-SDP image generated from the corresponding channel. The Parallel-SDP image presents global fault information and capture subtle changes of residual signals, which increases the sensitivity of fault diagnosis. Subsequently, rapid single-channel fault localization is achieved by barycenter analysis of Parallel-SDP images. Finally, based on the result of fault localization, the proposed polar sparse representation algorithm is utilized for fault diagnosis of mechanical and control faults separately. Based on the dataset obtained from physical-parameter-simulation, the proposed method was validated, and the accuracy of fault diagnosis of was 99.29%, which is better than conventional fault diagnosis, meanwhile, the computational resources consumption of the proposed method is much less than deeplearning-based method, which is suitable for embedded computing system in aircraft.

Impaired motor-to-sensory transformation mediates auditory hallucinations.

Distinguishing reality from hallucinations requires efficient monitoring of agency. It has been hypothesized that a copy of motor signals, termed efference copy (EC) or corollary discharge (CD), suppresses sensory responses to yield a sense of agency; impairment of the inhibitory function leads to hallucinations. However, how can the sole absence of inhibition yield positive symptoms of hallucinations? We hypothesize that selective impairments in functionally distinct signals of CD and EC during motor-to-sensory transformation cause the positive symptoms of hallucinations. In an electroencephalography (EEG) experiment with a delayed articulation paradigm in schizophrenic patients with (AVHs) and without auditory verbal hallucinations (non-AVHs), we found that preparing to speak without knowing the contents (general preparation) did not suppress auditory responses in both patient groups, suggesting the absent of inhibitory function of CD. Whereas, preparing to speak a syllable (specific preparation) enhanced the auditory responses to the prepared syllable in non-AVHs, whereas AVHs showed enhancement in responses to unprepared syllables, opposite to the observations in the normal population, suggesting that the enhancement function of EC is not precise in AVHs. A computational model with a virtual lesion of an inhibitory inter-neuron and disproportional sensitization of auditory cortices fitted the empirical data and further quantified the distinct impairments in motor-to-sensory transformation in AVHs. These results suggest that "broken" CD plus "noisy" EC causes erroneous monitoring of the imprecise generation of internal auditory representation and yields auditory hallucinations. Specific impairments in functional granularity of motor-to-sensory transformation mediate positivity symptoms of agency abnormality in mental disorders.

Syngap1 Promotes Cognitive Function through Regulation of Cortical Sensorimotor Dynamics.

Perception, a cognitive construct, emerges through sensorimotor integration (SMI). The genetic mechanisms that shape SMI required for perception are unknown. Here, we demonstrate in mice that expression of the autism/intellectual disability gene, Syngap1, in cortical excitatory neurons is required for formation of somatomotor networks that promote SMI-mediated perception. Cortical Syngap1 expression was necessary and sufficient for setting tactile sensitivity, sustaining tactile object exploration, and promoting tactile learning. Mice with deficient Syngap1 expression exhibited impaired neural dynamics induced by exploratory touches within a cortical-thalamic network known to promote attention and perception. Disrupted neuronal dynamics were associated with circuit-specific long-range synaptic connectivity abnormalities. Our data support a model where autonomous Syngap1 expression in cortical excitatory neurons promotes cognitive abilities through assembly of circuits that integrate temporally-overlapping sensory and motor signals, a process that promotes perception and attention. These data provide systems-level insights into the robust association between Syngap1 expression and cognitive ability.

Sense of Agency during Encoding Predicts Subjective Reliving.

Autonoetic consciousness (ANC), the ability to re-experience personal past events links episodic memory and self-consciousness by bridging awareness of oneself in a past event (i.e., during its encoding) with awareness of oneself in the present (i.e., during the reliving of a past event). Recent neuroscience research revealed a bodily form of self-consciousness, including the sense of agency (SoA) and the sense of body ownership (SoO) that are based on the integration of multisensory bodily inputs and motor signals. However, the relation between SoA and/or SoO with ANC is not known. Here, we used immersive virtual reality technology and motion tracking and investigated the potential association of SoA/SoO with ANC. For this, we exposed participants to different levels of visuomotor and perspectival congruency, known to modulate SoA and SoO, during the encoding of virtual scenes and collected ANC ratings 1 week after the encoding session. In a total of 74 healthy participants, we successfully induced systematic changes in SoA and SoO during encoding and found that ANC depended on the level of SoA experienced during encoding. Moreover, ANC was positively associated with SoA, but only for the scene encoded with preserved visuomotor and perspectival congruency, and such SoA-ANC coupling was absent for SoO and control questions. Collectively, these data provide behavioral evidence in a novel paradigm that links a key subjective component of bodily self-consciousness during encoding, SoA, to the subjective reliving of those encoded events from one's past, ANC.

Characteristics of motion signal profiles of tonic-clonic, tonic, hyperkinetic, and motor seizures extracted from nocturnal video recordings.

In this study, characteristics of signal profiles formed by motion, oscillation, and sound signals were analyzed to evaluate generalizability and variability in a single patient setting (intra-patient variability) and between patients (inter-patient variability). As a secondary objective, the effect of brivaracetam intervention on signal profiles was explored. Patient data included 13 hyperkinetic seizures, 65 tonic seizures, 13 tonic-clonic seizures, and 138 motor seizures from 11 patients. All patients underwent an 8-week monitoring, and after a 3-week baseline, brivaracetam was initiated. Motion, oscillation, and sound features extracted from the video were used to form signal profiles. Variance of signals was calculated, and combined median and quartile visualizations were used to visualize the results. Similarly, the effect of intervention was visualized. Hyperkinetic motion signals showed a rapid increase in motion and sound signals without oscillations and achieved low intra-patient variance. Tonic component created a recognizable peak in motion signal typical for tonic and tonic-clonic seizures. For tonic seizures, inter-patient variance was low. Motor signal profiles were varying, and they did not form a generalizable signal profile. Visually recognizable changes were observed in the signal profiles of two patients. Video-based motion signal analysis enabled the extraction of motion features characteristic for different motor seizure types which might be useful in further development of this system. Tonic component formed a recognizable seizure signature in the motion signal. Hyperkinetic and motor seizures may have not only significantly different motion signal amplitude but also overlapping signal profile characteristics which might hamper their automatic differentiation. Motion signals might be useful in the assessment of movement intensity changes to evaluate the treatment effect. Further research is needed to test generalizability and to increase reliability of the results.

Descending Motor Pathways

The brain is the origin of the descending motor pathways or tracts. These are responsible for the origination or modification of motor activity. This action is performed by motor signals, which transmitted from the brain and target lower motor neurons. Motor control is a chief task of the central nervous system (CNS). It can be recognized as the initiation of signals to coordinate muscle contraction of the body and head, aiming to keep a posture or to make a movement (transition between two postures). A large amount of the CNS is included in the process of motor control. The term "motor neuron, also known as motoneurons" may refer to single or one type of neurons responsible for the movement. However, this is not the truth. Actually, the “motor neuron” describe two categories of neurons, the “upper and lower motor neurons”. They are significantly different in their origins, synapses, pathways, neurotransmitters and developed lesions due to their affection. Motor neurons are essential constituents of two different circuits “the upper and lower motor circuits”. These circuits control both voluntary and involuntary movements. This was achieved by the connection of higher centers with muscles and glands Descending tracts of the motor system are originating mainly from two parts, the cortex and the brainstem. Tracts originating from the cortex control the voluntary movements and adjust posture relating to voluntary movement. Furthermore, the descending pathways are subdivided into (pathways) describing their start and termination. These are (1) corticospinal and corticobulbar, (2) cortico-reticulo-spinal, (3) cortico-rubro-spinal (4) cortico-tecto-spinal, (5) vestibulo-spinal and (6) raphe–spinal and ceruleus–spinal tracts (aminergic pathways) Pyramidal tracts, originating in the cortex and carry motor fibres to the spinal cord and brain stem. It is subdivided into corticospinal and Corticobulbar tracts. The extrapyramidal tracts originate in the brainstem. Then carry motor projections to the spinal cord. These tracts are primarily concerned with the control of the involuntary and automatic innervations for all musculature (e.g., muscle tone, balance, posture and locomotion).

The Effect of Short-Term Exposure to Lower Body Positive Pressure on Motor Signal Processing, Reaction Times, and Cardiovascular Parameters in Healthy Volunteers Using Medical Anti-shock Trousers.

Microgravity, as experienced during spaceflight has notable effects on the cognition and cardiovascular systems. However, its effect on motor signal processing is not known. Inthis study, we planned to study the effect of microgravity simulation with a lower body positive pressure of 50 mmHg on motor signal processing, reaction times, and cardiovascular parameters. Thirty healthy human volunteers participated in this investigation, and continuous ECG and non-invasive blood pressure were measured at baseline, during, and after a lower body positive pressure of 50 mmHg.Bereitschafts potential was recorded at 0 mmHg and 50 mmHg pressure values in a lower body positive pressure (LBPP) suit.Parameters recorded during the pressure change of 0 mmHg to 50 mmHg were RR interval, heart rate, systolic blood pressure, diastolic blood pressure, stroke volume, cardiac output, and peripheral vascular resistance. Heart rate variability (HRV) was calculated from RR intervals during resting and pressure of 50mmof Hg.We also compared simple and choice reaction times for visual and auditory stimuli during 50 mmHg LBPP exposure with baseline recording. We found a significant increase in systolic blood pressure, stroke volume, and cardiac output from baseline at 50 mmHg of LBPP. We found a significant change in amplitude and area of Bereitschaft potential at the C4 site at 50 mmHg of LBPP. We found a significant change in low-frequency power (LF)as compared to the baseline in HRV. Simple reaction time (visual & auditory) and auditory choice reaction time were improved at 50 mmHg of LBPP. Motor signal processing and reaction time were improved during 50 mmHg of lower body positive pressure exposure.

Different mechanisms of contextual inference govern associatively learned and sensory-evoked postural responses

Human standing balance relies on the continuous monitoring and integration of sensory signals to infer our body's motion and orientation within the environment. However, when sensory information is no longer contextually relevant to balancing the body (e.g., when sensory and motor signals are incongruent), sensory-evoked balance responses are rapidly suppressed, much earlier than any conscious perception of changes in balance control. Here, we used a robotic balance simulator to assess whether associatively learned postural responses are similarly modulated by sensorimotor incongruence and contextual relevance to postural control. Twenty-nine participants in three groups were classically conditioned to generate postural responses to whole-body perturbations when presented with an initially neutral sound cue. During catch and extinction trials, participants received only the auditory stimulus but in different sensorimotor states corresponding to their group: 1) during normal active balance, 2) while immobilized, and 3) throughout periods where the computer subtly removed active control over balance. In the balancing and immobilized states, conditioned responses were either evoked or suppressed, respectively, according to the (in)ability to control movement. Following the immobilized state, conditioned responses were renewed when balance was restored, indicating that conditioning was retained but only expressed when contextually relevant. In contrast, conditioned responses persisted in the computer-controlled state even though there was no causal relationship between motor and sensory signals. These findings suggest that mechanisms responsible for sensory-evoked and conditioned postural responses do not share a single, central contextual inference and assessment of their relevance to postural control, and may instead operate in parallel.

Neurotensin-specific corticothalamic circuit regulates innate response conflict

Animals must simultaneously select and balance multiple action contingencies in ambiguous situations: for instance, evading danger during feeding. This has rarely been examined in the context of information selection; despite corticothalamic pathways that mediate sensory attention being relatively well characterized, neural mechanisms filtering conflicting actions remain unclear. Here, we develop a new loom/feed test to observe conflict between naturally induced fear and feeding and identify a novel anterior cingulate cortex (ACC) output to the ventral anterior and ventral lateral thalamus (VA/VL) that adjusts selectivity between these innate actions. Using micro-endoscopy and fiber photometry, we reveal that activity in corticofugal outputs was lowered during unbalanced/singularly occupied periods, as were the resulting decreased thalamic initiation-related signals for less-favored actions, suggesting that the integration of ACC-thalamic firing may directly regulate the output of behavior choices. Accordingly, the optoinhibition of ACC-VA/VL circuits induced high bias toward feeding at the expense of defense. To identify upstream "commander" cortical cells gating this output, we established dual-order tracing (DOT)-translating ribosome affinity purification (TRAP)-a scheme to label upstream neurons with transcriptome analysis-and found a novel population of neurotensin-positive interneurons (ACCNts). The photoexcitation of ACCNts cells indeed caused similarly hyper-selective behaviors. Collectively, this new "corticofugal action filter" scheme suggests that communication in multi-step cingulate circuits may critically influence the summation of motor signals in thalamic outputs, regulating bias between innate action types.

Characterization and classification of kinesthetic motor imagery levels

Objective. Kinesthetic Motor Imagery (KMI) represents a robust brain paradigm intended for electroencephalography (EEG)-based commands in brain-computer interfaces (BCIs). However, ensuring high accuracy in multi-command execution remains challenging, with data from C3 and C4 electrodes reaching up to 92% accuracy. This paper aims to characterize and classify EEG-based KMI of multilevel muscle contraction without relying on primary motor cortex signals. Approach. A new method based on Hurst exponents is introduced to characterize EEG signals of multilevel KMI of muscle contraction from electrodes placed on the premotor, dorsolateral prefrontal, and inferior parietal cortices. EEG signals were recorded during a hand-grip task at four levels of muscle contraction (0%, 10%, 40%, and 70% of the maximal isometric voluntary contraction). The task was executed under two conditions: first, physically, to train subjects in achieving muscle contraction at each level, followed by mental imagery under the KMI paradigm for each contraction level. EMG signals were recorded in both conditions to correlate muscle contraction execution, whether correct or null accurately. Independent component analysis (ICA) maps EEG signals from the sensor to the source space for preprocessing. For characterization, three algorithms based on Hurst exponents were used: the original (HO), using partitions (HRS), and applying semivariogram (HV). Finally, seven classifiers were used: Bayes network (BN), naive Bayes (NB), support vector machine (SVM), random forest (RF), random tree (RT), multilayer perceptron (MP), and k-nearest neighbors (kNN). Main results. A combination of the three Hurst characterization algorithms produced the highest average accuracy of 96.42% from kNN, followed by MP (92.85%), SVM (92.85%), NB (91.07%), RF (91.07%), BN (91.07%), and RT (80.35%). of 96.42% for kNN. Significance. Results show the feasibility of KMI multilevel muscle contraction detection and, thus, the viability of non-binary EEG-based BCI applications without using signals from the motor cortex.

Mistimed Motor Signals from the Brain Affect Force Precision

Mistimed Motor Signals from the Brain Affect Force Precision

Decoding Remapped Spatial Information in the Peri-Saccadic Period.

It has been suggested that, prior to a saccade, visual neurons predictively respond to stimuli that will fall in their receptive fields after completion of the saccade. This saccadic remapping process is thought to compensate for the shift of the visual world across the retina caused by eye movements. To map the timing of this predictive process in the brain, we recorded neural activity using electroencephalography during a saccade task. Human participants (male and female) made saccades between two fixation points while covertly attending to oriented gratings briefly presented at various locations on the screen. Data recorded during trials in which participants maintained fixation were used to train classifiers on stimuli in different positions. Subsequently, data collected during saccade trials were used to test for the presence of remapped stimulus information at the post-saccadic retinotopic location in the peri-saccadic period, providing unique insight into when remapped information becomes available. We found that the stimulus could be decoded at the remapped location ∼180 ms post-stimulus onset, but only when the stimulus was presented 100-200 ms before saccade onset. Within this range, we found that the timing of remapping was dictated by stimulus onset rather than saccade onset. We conclude that presenting the stimulus immediately before the saccade allows for optimal integration of the corollary discharge signal with the incoming peripheral visual information, resulting in a remapping of activation to the relevant post-saccadic retinotopic neurons.

Realistic 3D human saccades generated by a 6-DOF biomimetic robotic eye under optimal control.

We recently developed a biomimetic robotic eye with six independent tendons, each controlled by their own rotatory motor, and with insertions on the eye ball that faithfully mimic the biomechanics of the human eye. We constructed an accurate physical computational model of this system, and learned to control its nonlinear dynamics by optimising a cost that penalised saccade inaccuracy, movement duration, and total energy expenditure of the motors. To speed up the calculations, the physical simulator was approximated by a recurrent neural network (NARX). We showed that the system can produce realistic eye movements that closely resemble human saccades in all directions: their nonlinear main-sequence dynamics (amplitude-peak eye velocity and duration relationships), cross-coupling of the horizontal and vertical movement components leading to approximately straight saccade trajectories, and the 3D kinematics that restrict 3D eye orientations to a plane (Listing's law). Interestingly, the control algorithm had organised the motors into appropriate agonist-antagonist muscle pairs, and the motor signals for the eye resembled the well-known pulse-step characteristics that have been reported for monkey motoneuronal activity. We here fully analyse the eye-movement properties produced by the computational model across the entire oculomotor range and the underlying control signals. We argue that our system may shed new light on the neural control signals and their couplings within the final neural pathways of the primate oculomotor system, and that an optimal control principle may account for a wide variety of oculomotor behaviours. The generated data are publicly available at https://data.ru.nl/collections/di/dcn/DSC_626870_0003_600.