Maximization of Learning Speed in the Motor Cortex Due to Neuronal Redundancy

Many redundancies play functional roles in motor control and motor learning. For example, kinematic and muscle redundancies contribute to stabilizing posture and impedance control, respectively. Another redundancy is the number of neurons themselves; there are overwhelmingly more neurons than muscles, and many combinations of neural activation can generate identical muscle activity. The functional roles of this neuronal redundancy remains unknown. Analysis of a redundant neural network model makes it possible to investigate these functional roles while varying the number of model neurons and holding constant the number of output units. Our analysis reveals that learning speed reaches its maximum value if and only if the model includes sufficient neuronal redundancy. This analytical result does not depend on whether the distribution of the preferred direction is uniform or a skewed bimodal, both of which have been reported in neurophysiological studies. Neuronal redundancy maximizes learning speed, even if the neural network model includes recurrent connections, a nonlinear activation function, or nonlinear muscle units. Furthermore, our results do not rely on the shape of the generalization function. The results of this study suggest that one of the functional roles of neuronal redundancy is to maximize learning speed

On Neuromechanical Approaches for the Study of Biological Grasp and Manipulation

Biological and robotic grasp and manipulation are undeniably similar at the level of mechanical task performance. However, their underlying fundamental biological vs. engineering mechanisms are, by definition, dramatically different and can even be antithetical. Even our approach to each is diametrically opposite: inductive science for the study of biological systems vs. engineering synthesis for the design and construction of robotic systems. The past 20 years have seen several conceptual advances in both fields and the quest to unify them. Chief among them is the reluctant recognition that their underlying fundamental mechanisms may actually share limited common ground, while exhibiting many fundamental differences. This recognition is particularly liberating because it allows us to resolve and move beyond multiple paradoxes and contradictions that arose from the initial reasonable assumption of a large common ground. Here, we begin by introducing the perspective of neuromechanics, which emphasizes that real-world behavior emerges from the intimate interactions among the physical structure of the system, the mechanical requirements of a task, the feasible neural control actions to produce it, and the ability of the neuromuscular system to adapt through interactions with the environment. This allows us to articulate a succinct overview of a few salient conceptual paradoxes and contradictions regarding under-determined vs. over-determined mechanics, under- vs. over-actuated control, prescribed vs. emergent function, learning vs. implementation vs. adaptation, prescriptive vs. descriptive synergies, and optimal vs. habitual performance. We conclude by presenting open questions and suggesting directions for future research. We hope this frank assessment of the state-of-the-art will encourage and guide these communities to continue to interact and make progress in these important areas

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 333)

This bibliography lists 122 reports, articles and other documents introduced into the NASA Scientific and Technical Information System during January, 1990. Subject coverage includes: aerospace medicine and psychology, life support systems and controlled environments, safety equipment, exobiology and extraterrestrial life, and flight crew behavior and performance

Assistive telehealth systems for neurorehabilitation

Telehealth is an evolving field within the broader domain of Biomedical Engineering, specifically situated within the context of the Internet of Medical Things (IoMT). In today's society, the importance of Telehealth systems is increasingly recognized, as they enable remote patient treatment by physicians. One significant application in neurorehabilitation is Transcranial Direct Current Stimulation (tDCS), which has demonstrated its effectiveness in modulating mental function and learning over several years. Furthermore, tDCS is widely accepted as a safe approach in the field. This presentation focuses on the development of a non-invasive wearable tDCS device with integrated Internet connectivity. This IoMT device enables remote configuration of treatment parameters, such as session duration, current level, and placebo status. Clinicians can remotely access the device and define these parameters within the approved safety ranges for tDCS treatments. In addition to the wearable tDCS device, a prototype web portal is being developed to collect performance data during neurorehabilitation exercises conducted by individuals at home. This portal also facilitates remote interaction between patients and clinicians. To provide a platform-independent solution for accessing up-to-date healthcare information, a Progressive Web Application (PWA) is being developed. The PWA enables real-time communication between patients and doctors through text chat and video conferencing. The primary objective is to create a cross-platform web application with PWA features that can function effectively as a native application in various operating systems

Sensory coding in supragranular cells of the vibrissal cortex in anesthetized and awake mice

Sensory perception entails reliable representation of the external stimuli as impulse activity of individual neurons (i.e. spikes) and neuronal populations in the sensory area. An ongoing challenge in neuroscience is to identify and characterize the features of the stimuli which are relevant to a specific sensory modality and neuronal strategies to effectively and efficiently encode those features. It is widely hypothesized that the neuronal populations employ “sparse coding” strategies to optimize the stimulus representations with a low energetic cost (i.e. low impulse activity). In the past two decades, a wealth of experimental evidence has supported this hypothesis by showing spatiotemporally sparse activity in sensory area. Despite numerous studies, the extent of sparse coding and its underlying mechanisms are not fully understood, especially in primary vibrissal somatosensory cortex (vS1), which is a key model system in sensory neuroscience. Importantly, it is not clear yet whether sparse activation of supragranular vS1 is due to insufficient synaptic input to the majority of the cells or the absence of effective stimulus features. In this thesis, first we asked how the choice of stimulus could affect the degree of sparseness and/or the overall fraction of the responsive vS1 neurons. We presented whisker deflections spanning a broad range of intensities, including “standard stimuli” and a high-velocity, “sharp” stimulus, which simulated the fast slip events that occur during whisker mediated object palpation. We used whole-cell and cell-attached recording and calcium imaging to characterize the neuronal responses to these stimuli. Consistent with previous literature, whole-cell recording revealed a sparse response to the standard range of velocities: although all recorded cells showed tuning to velocity in their postsynaptic potentials, only a small fraction produced stimulus-evoked spikes. In contrast, the sharp stimulus evoked reliable spiking in a large fraction of regular spiking neurons in the supragranular vS1. Spiking responses to the sharp stimulus were binary and precisely timed, with minimum trial-to-trial variability. Interestingly, we also observed that the sharp stimulus produced a consistent and significant reduction in action potential threshold. In the second step we asked whether the stimulus dependent sparse and dense activations we found in anesthetized condition would generalize to the awake condition. We employed cell-attached recordings in head-fixed awake mice to explore the degree of sparseness in awake cortex. Although, stimuli delivered by a piezo-electric actuator evoked significant response in a small fraction of regular spiking supragranular neurons (16%-29%), we observed that a majority of neurons (84%) were driven by manual probing of whiskers. Our results demonstrate that despite sparse activity, the majority of neurons in the superficial layers of vS1 contribute to coding by representing a specific feature of the tactile stimulus. Thesis outline: Chapter 1 provides a review of the current knowledge on sparse coding and an overview of the whisker-sensory pathway. Chapter 2 represents our published results regarding sparse and dense coding in vS1 of anesthetized mice (Ranjbar-Slamloo and Arabzadeh 2017). Chapter 3 represents our pending manuscript with results obtained with piezo and manual stimulation in awake mice. Finally, in Chapter 4 we discuss and conclude our findings in the context of the literature. The appendix provides unpublished results related to Chapter 2. This section is referenced in the final chapter for further discussion

Adaptive Robotic Control Driven by a Versatile Spiking Cerebellar Network

The cerebellum is involved in a large number of different neural processes, especially in associative learning and in fine motor control. To develop a comprehensive theory of sensorimotor learning and control, it is crucial to determine the neural basis of coding and plasticity embedded into the cerebellar neural circuit and how they are translated into behavioral outcomes in learning paradigms. Learning has to be inferred from the interaction of an embodied system with its real environment, and the same cerebellar principles derived from cell physiology have to be able to drive a variety of tasks of different nature, calling for complex timing and movement patterns. We have coupled a realistic cerebellar spiking neural network (SNN) with a real robot and challenged it in multiple diverse sensorimotor tasks. Encoding and decoding strategies based on neuronal firing rates were applied. Adaptive motor control protocols with acquisition and extinction phases have been designed and tested, including an associative Pavlovian task (Eye blinking classical conditioning), a vestibulo-ocular task and a perturbed arm reaching task operating in closed-loop. The SNN processed in real-time mossy fiber inputs as arbitrary contextual signals, irrespective of whether they conveyed a tone, a vestibular stimulus or the position of a limb. A bidirectional long-term plasticity rule implemented at parallel fibers-Purkinje cell synapses modulated the output activity in the deep cerebellar nuclei. In all tasks, the neurorobot learned to adjust timing and gain of the motor responses by tuning its output discharge. It succeeded in reproducing how human biological systems acquire, extinguish and express knowledge of a noisy and changing world. By varying stimuli and perturbations patterns, real-time control robustness and generalizability were validated. The implicit spiking dynamics of the cerebellar model fulfill timing, prediction and learning functions.European Union (Human Brain Project) REALNET FP7-ICT270434 CEREBNET FP7-ITN238686 HBP-60410

The role of noise in sensorimotor control

Goal-directed arm movements show stereotypical trajectories, despite the infinite possible ways to reach a given end point. This thesis examines the hypothesis that this stereotypy arises because movements are optimised to reduce the consequences of signal-dependent noise on the motor command. Both experimental and modelling studies demonstrate that signal-dependent noise arises from the normal behaviour of the muscle and motor neuron pool, and has a particular distribution across muscles of different sizes. Specifically, noise decreases in a systematic fashion with increasing muscle strength and motor unit number. Simulations of obstacle avoidance performance in the presence of signal-dependent noise demonstrate that the optimal trajectory for reaching the target accurately and without collision matches the observed trajectories. Isometric force generation is also shown to have systematic changes in variability with posture, which can be explained by the presence of signal-dependent noise in the muscles of the arm. These results confirm the tested hypothesis and imply that consideration of the statistics of action is crucial to human movement planning. To investigate the importance of feedback in the motor system, the impact of static position on motor excitability was examined using transcranial magnetic stimulation and systematic changes in motor evoked potentials were observed. Force generated at the wrist following stimulation was analysed in terms of different possible movement representations, and the differences between force fields arising from stimulation over the cervical spinal cord and from stimulation over primary motor cortex are determined. These results demonstrate the structured influence of proprioceptive feedback on the human motor system. All the experiments are discussed in relation to current theories describing the control of human movements and the impact of noise in the motor system

Brain-Inspired Computational Intelligence via Predictive Coding

Artificial intelligence (AI) is rapidly becoming one of the key technologies of this century. The majority of results in AI thus far have been achieved using deep neural networks trained with the error backpropagation learning algorithm. However, the ubiquitous adoption of this approach has highlighted some important limitations such as substantial computational cost, difficulty in quantifying uncertainty, lack of robustness, unreliability, and biological implausibility. It is possible that addressing these limitations may require schemes that are inspired and guided by neuroscience theories. One such theory, called predictive coding (PC), has shown promising performance in machine intelligence tasks, exhibiting exciting properties that make it potentially valuable for the machine learning community: PC can model information processing in different brain areas, can be used in cognitive control and robotics, and has a solid mathematical grounding in variational inference, offering a powerful inversion scheme for a specific class of continuous-state generative models. With the hope of foregrounding research in this direction, we survey the literature that has contributed to this perspective, highlighting the many ways that PC might play a role in the future of machine learning and computational intelligence at large.Comment: 37 Pages, 9 Figure