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Downbeat nystagmus (DBN) is a neuro-otological finding frequently encountered by clinicians dealing with patients with vertigo. Since DBN is a finding that should be understood because of central vestibular dysfunction, it is necessary to know how to frame it promptly to suggest the correct diagnostic-therapeutic pathway to the patient. As knowledge of its pathophysiology has progressed, the importance of this clinical sign has been increasingly understood. At the same time, clinical diagnostic knowledge has increased, and it has been recognized that this sign may occur sporadically or in association with others within defined clinical syndromes. Thus, in many cases, different therapeutic solutions have become possible. In our work, we have attempted to systematize current knowledge about the origin of this finding, the clinical presentation and current treatment options, to provide an overview that can be used at different levels, from the general practitioner to the specialist neurologist or neurotologist. [ABSTRACT FROM AUTHOR]

Myoelectric prostheses have recently shown significant promise for restoring hand function in individuals with upper limb loss or deficiencies, driven by advances in machine learning and increasingly accessible bioelectrical signal acquisition devices. Here, we first introduce and validate a novel experimental paradigm using a virtual reality headset equipped with hand-tracking capabilities to facilitate the recordings of synchronized EMG signals and hand pose estimation. Using both the phasic and tonic EMG components of data acquired through the proposed paradigm, we compare hand gesture classification pipelines based on standard signal processing features, convolutional neural networks, and covariance matrices with Riemannian geometry computed from raw or xDAWN-filtered EMG signals. We demonstrate the performance of the latter for gesture classification using EMG signals. We further hypothesize that introducing physiological knowledge in machine learning models will enhance their performances, leading to better myoelectric prosthesis control. We demonstrate the potential of this approach by using the neurophysiological integration of the "move command" to better separate the phasic and tonic components of the EMG signals, significantly improving the performance of sustained posture recognition. These results pave the way for the development of new cutting-edge machine learning techniques, likely refined by neurophysiology, that will further improve the decoding of real-time natural gestures and, ultimately, the control of myoelectric prostheses. [ABSTRACT FROM AUTHOR]

During foraging behavior, action values are persistently encoded in neural activity and updated depending on the history of choice outcomes. What is the neural mechanism for action value maintenance and updating? Here, we explore two contrasting network models: synaptic learning of action value versus neural integration. We show that both models can reproduce extant experimental data, but they yield distinct predictions about the underlying biological neural circuits. In particular, the neural integrator model but not the synaptic model requires that reward signals are mediated by neural pools selective for action alternatives and their projections are aligned with linear attractor axes in the valuation system. We demonstrate experimentally observable neural dynamical signatures and feasible perturbations to differentiate the two contrasting scenarios, suggesting that the synaptic model is a more robust candidate mechanism. Overall, this work provides a modeling framework to guide future experimental research on probabilistic foraging. [ABSTRACT FROM AUTHOR]

Downbeat nystagmus (DBN) is a neuro-otological finding frequently encountered by clinicians dealing with patients with vertigo. Since DBN is a finding that should be understood because of central vestibular dysfunction, it is necessary to know how to frame it promptly to suggest the correct diagnostic-therapeutic pathway to the patient. As knowledge of its pathophysiology has progressed, the importance of this clinical sign has been increasingly understood. At the same time, clinical diagnostic knowledge has increased, and it has been recognized that this sign may occur sporadically or in association with others within defined clinical syndromes. Thus, in many cases, different therapeutic solutions have become possible. In our work, we have attempted to systematize current knowledge about the origin of this finding, the clinical presentation and current treatment options, to provide an overview that can be used at different levels, from the general practitioner to the specialist neurologist or neurotologist.

Myoelectric prostheses have recently shown significant promise for restoring hand function in individuals with upper limb loss or deficiencies, driven by advances in machine learning and increasingly accessible bioelectrical signal acquisition devices. Here, we first introduce and validate a novel experimental paradigm using a virtual reality headset equipped with hand-tracking capabilities to facilitate the recordings of synchronized EMG signals and hand pose estimation. Using both the phasic and tonic EMG components of data acquired through the proposed paradigm, we compare hand gesture classification pipelines based on standard signal processing features, convolutional neural networks, and covariance matrices with Riemannian geometry computed from raw or xDAWN-filtered EMG signals. We demonstrate the performance of the latter for gesture classification using EMG signals. We further hypothesize that introducing physiological knowledge in machine learning models will enhance their performances, leading to better myoelectric prosthesis control. We demonstrate the potential of this approach by using the neurophysiological integration of the “move command" to better separate the phasic and tonic components of the EMG signals, significantly improving the performance of sustained posture recognition. These results pave the way for the development of new cutting-edge machine learning techniques, likely refined by neurophysiology, that will further improve the decoding of real-time natural gestures and, ultimately, the control of myoelectric prostheses.

In this chapter, Robinson develops models to account for the neural control of the vestibulo-ocular reflex in response to horizontal and vertical head rotations. By combining knowledge of the discharge properties of the several subpopulations of neurons that contribute to vestibular eye movements with their known anatomical connections, these models seek to explain how specific signals are combined to enable the ocular motoneurons to program vestibular eye movements that compensate for head perturbations. Details such as the integration of raw vestibular signals, differences in the neuronal processing for vertical versus horizontal reflexes, and the role of individual pathways such as the medial longitudinal fasciculus are discussed., (Copyright © 2022 Elsevier B.V. All rights reserved.)

The biophysical properties of the labyrinthine semicircular canals, and the electrophysiological properties of peripheral vestibular afferent neurons over a range of stimulus frequencies, are reviewed. Resting discharge activity and adaptive properties of vestibular neurons are discussed. Central processing of vestibular signals is then examined, including push-pull organization and the velocity storage mechanism. A detailed treatment of the final common neural integrator for oculomotor signals follows with consideration of its neural substrate and how distributed networks of neurons can overcome several problems posed by conventional control-systems models, such as why neural signals, but not background discharge, are integrated. Next, the behavior of the vestibulo-ocular reflex in darkness is compared with how it satisfies visual demands during natural activities. Finally, the reflex's performance at high frequencies of head rotation is discussed., (Copyright © 2022 Elsevier B.V. All rights reserved.)

This chapter discusses the premotor neural mechanisms that control horizontal saccadic eye movements. Oculomotoneurons carry a pulse-step signal that underlies the pulse-step force driving the overdamped plant. The pulse and step are both generated by a common signal, arising from medium-lead burst neurons in the pons. Their burst signal encodes saccadic eye velocity, while the number of spikes in the burst relates to the saccade amplitude. The step component, which encodes the eye position, is obtained by neural integration of the burst. Several oculomotor neural disorders can be explained by impairments in the binocular push-pull organization of this pulse-step mechanism. Plasticity of the pulse-step control, e.g., in response to muscle weakening, is mediated by cerebellar vermis and flocculus. Saccadic offset may be controlled, either by active braking, or by an exponential slide signal. The neurophysiology is summarized by a quantitative model, in which the firing rate of burst neurons is controlled by a dynamic negative feedback loop that carries the instantaneous eye position signal from the neural integrator. This signal is compared with a desired eye-position command in the head from higher centers, and the resulting dynamic motor error drives the high-gain burst cells. Instability of the system is prevented by the mutual inhibitory interaction between burst cells and omnipause neurons. The model explains many features of normal saccades, but also accounts for pathologies and abnormalities like dynamic overshoots and saccade oscillations., (Copyright © 2022 Elsevier B.V. All rights reserved.)