A model for self-organization of sensorimotor function: spinal interneuronal integration

Enander, Jonas M.D.; Loeb, Gerald E.; Jörntell, Henrik

doi:10.1152/jn.00054.2022

Cited by 8 publications

(12 citation statements)

References 91 publications

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“…Experimental data ( Petersson et al, 2003 ; Fagard et al, 2018 ) and modeling studies ( Marques et al, 2013 ; Enander et al, 2022a ; Enander et al, 2022b ) suggest that details of the spinal connectivity may self-organize during spontaneous motor activity that occurs throughout fetal ( Kiehn and Tresch, 2002 ) and perinatal development ( Piek, 2006 ; Caligiore et al, 2008 ). If biological middleware self-organizes around the mechanics of the sensorimotor plant in which it finds itself, the same learning process might be applied to generate suitable middleware for arbitrary robotic plants ( Blumberg et al, 2013 ).…”

Section: Bio-inspired Development Of Control Systemsmentioning

confidence: 99%

“…After roughly a days’ worth of experience the initially random synaptic weights reorganize into mature and stable patterns, with only slow and sparse changes in the later stages. Redrawn from ( Enander et al, 2022b ), which provides detailed analysis of emergent patterns of connectivity that resemble those shown in Figure 5 .…”

Section: Bio-inspired Development Of Control Systemsmentioning

confidence: 99%

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Developing Intelligent Robots that Grasp Affordance

Loeb

2022

Front. Robot. AI

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Humans and robots operating in unstructured environments both need to classify objects through haptic exploration and use them in various tasks, but currently they differ greatly in their strategies for acquiring such capabilities. This review explores nascent technologies that promise more convergence. A novel form of artificial intelligence classifies objects according to sensory percepts during active exploration and decides on efficient sequences of exploratory actions to identify objects. Representing objects according to the collective experience of manipulating them provides a substrate for discovering causality and affordances. Such concepts that generalize beyond explicit training experiences are an important aspect of human intelligence that has eluded robots. For robots to acquire such knowledge, they will need an extended period of active exploration and manipulation similar to that employed by infants. The efficacy, efficiency and safety of such behaviors depends on achieving smooth transitions between movements that change quickly from exploratory to executive to reflexive. Animals achieve such smoothness by using a hierarchical control scheme that is fundamentally different from those of conventional robotics. The lowest level of that hierarchy, the spinal cord, starts to self-organize during spontaneous movements in the fetus. This allows its connectivity to reflect the mechanics of the musculoskeletal plant, a bio-inspired process that could be used to adapt spinal-like middleware for robots. Implementation of these extended and essential stages of fetal and infant development is impractical, however, for mechatronic hardware that does not heal and replace itself like biological tissues. Instead such development can now be accomplished in silico and then cloned into physical robots, a strategy that could transcend human performance.

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Section: Bio-inspired Development Of Control Systemsmentioning

confidence: 99%

Section: Bio-inspired Development Of Control Systemsmentioning

confidence: 99%

Developing Intelligent Robots that Grasp Affordance

Loeb

2022

Front. Robot. AI

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“…These species obviously have bodies of very different biomechanical properties, different movement patterns, and consequently, they require different neuromechanical pattern generators. This is hard to explain without assuming that the spinal cord circuitry ‘phenotype’ is to a large extent shaped by the spinal neuronal network learns plant biomechanics as an essential part of its early development (Kohler et al, 2020 ; Enander et al, 2021 , 2022 ). This can be achieved by a neural network that initially is a passive follower to the anatomically predefined biomechanical dynamics and corresponding sensor activation patterns during locomotion.…”

Section: Neural Considerationsmentioning

confidence: 99%

Whole Body Coordination for Self-Assistance in Locomotion

Seyfarth

Zhao²,

Jörntell³

2022

Front. Neurorobot.

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The dynamics of the human body can be described by the accelerations and masses of the different body parts (e.g., legs, arm, trunk). These body parts can exhibit specific coordination patterns with each other. In human walking, we found that the swing leg cooperates with the upper body and the stance leg in different ways (e.g., in-phase and out-of-phase in vertical and horizontal directions, respectively). Such patterns of self-assistance found in human locomotion could be of advantage in robotics design, in the design of any assistive device for patients with movement impairments. It can also shed light on several unexplained infrastructural features of the CNS motor control. Self-assistance means that distributed parts of the body contribute to an overlay of functions that are required to solve the underlying motor task. To draw advantage of self-assisting effects, precise and balanced spatiotemporal patterns of muscle activation are necessary. We show that the necessary neural connectivity infrastructure to achieve such muscle control exists in abundance in the spinocerebellar circuitry. We discuss how these connectivity patterns of the spinal interneurons appear to be present already perinatally but also likely are learned. We also discuss the importance of these insights into whole body locomotion for the successful design of future assistive devices and the sense of control that they could ideally confer to the user.

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“…A computer simulation has recently shown that spinal interneuron networks can self-organize themselves so as to provide proper activity patterns during movement (Enander et al 2022).…”

Section: Changes In Other Spinal Interneuronal Network?mentioning

confidence: 99%

Plastic Spinal Motor Circuits in Health and Disease

Windhorst¹,

Dibaj²

2022

Preprint

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In former times, the spinal cord was considered a hard-wired network for spinal reflexes and a conduit for long-range connections. This view has changed dramatically over the past few decades. It is now recognized as a plastic device whose structures and functions adapt to changing circumstances. While such changes also occur under physiological conditions, the most dramatic alterations take place during or after various pathological events. It is astonishing what mechanisms the musculo-skeletal system has evolved to come to grips with the damages. Many of these changes are maladaptive, but some appear to help adapt to the new conditions. Although myriads of studies, using manifold methods, have been devoted to elucidating the underlying mechanisms, in humans and animal models, the etiology and pathophysiology of various diseases are still little understood, due to a number of reasons. We will here try to summarize some results and remaining problems in a selection of diseases, in particular spinal muscular atrophy (SMA), amyotrophic laterals sclerosis (ALS), and predominantly spinal cord injury (SCI) with occasional relations to stroke. Especially the changes in SCI (and stroke) depend on the cause, site and extent of the afflicted damage and are therefore multifarious. At the end, we will briefly summarize results indicating that operant, classical and instrumental conditioning can be used to produce plastic changes in healthy people, with potentials for applications to patients with spinal cord injury. In order not to overload the article, we will not delve deeply into sub-cellular processes.

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A model for self-organization of sensorimotor function: spinal interneuronal integration

Cited by 8 publications

References 91 publications

Developing Intelligent Robots that Grasp Affordance

Developing Intelligent Robots that Grasp Affordance

Whole Body Coordination for Self-Assistance in Locomotion

Plastic Spinal Motor Circuits in Health and Disease

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