RORβ Spinal Interneurons Gate Sensory Transmission during Locomotion to Secure a Fluid Walking Gait

Peripheral and intraspinal feedback is required to shape and update the output of spinal networks that execute motor behavior. We report that lumbar dI2 spinal interneurons in chicks receive synaptic input from afferents and premotor neurons. These interneurons innervate contralateral premotor networks in the lumbar and brachial spinal cord, and their ascending projections innervate the cerebellum. These findings suggest that dI2 neurons function as interneurons in local lumbar circuits, are involved in lumbo-brachial coupling, and that part of them deliver peripheral and intraspinal feedback to the cerebellum. Silencing of dI2 neurons leads to destabilized stepping in P8 hatchlings, with occasional collapses, variable step profiles and a wide-base walking gait, suggesting that dI2 neurons may contribute to the stabilization of the bipedal gait.

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Faculty Opinions recommendation of Synchronization of an embryonic network of identified spinal interneurons solely by electrical coupling.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.1000838.12407 ◽

2001 ◽

Author(s):

Ronald Harris-Warrick

Keyword(s):

Electrical Coupling ◽

Spinal Interneurons

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Faculty Opinions recommendation of Conditional rhythmicity of ventral spinal interneurons defined by expression of the Hb9 homeodomain protein.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.1006852.342324 ◽

2005 ◽

Author(s):

Ole Kiehn

Keyword(s):

Homeodomain Protein ◽

Spinal Interneurons

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Faculty Opinions recommendation of Genetic identification of spinal interneurons that coordinate left-right locomotor activity necessary for walking movements.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.1019388.218551 ◽

2004 ◽

Cited By ~ 1

Author(s):

Jack Feldman

Keyword(s):

Locomotor Activity ◽

Genetic Identification ◽

Spinal Interneurons

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Faculty Opinions recommendation of Grading movement strength by changes in firing intensity versus recruitment of spinal interneurons.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.1065936.518891 ◽

2007 ◽

Author(s):

Keith Sillar

Keyword(s):

Spinal Interneurons

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Faculty Opinions recommendation of Sensory transmission in cerebellar granule cells relies on similarly coded mossy fiber inputs.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.1157335.617477 ◽

2009 ◽

Author(s):

Leonard Maler

Keyword(s):

Granule Cells ◽

Cerebellar Granule Cells ◽

Mossy Fiber ◽

Cerebellar Granule ◽

Sensory Transmission

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Sensory-Motor Mechanisms Increasing Falls Risk in Diabetic Peripheral Neuropathy

Medicina ◽

10.3390/medicina57050457 ◽

2021 ◽

Vol 57 (5) ◽

pp. 457

Author(s):

Neil D. Reeves ◽

Giorgio Orlando ◽

Steven J. Brown

Keyword(s):

Peripheral Neuropathy ◽

Diabetic Peripheral Neuropathy ◽

Motor Nerve ◽

Step Length ◽

Falls Prevention ◽

Motor Deficits ◽

Falls Risk ◽

Walking Gait ◽

Risk Of Falls ◽

Diabetes Patients

Diabetic peripheral neuropathy (DPN) is associated with peripheral sensory and motor nerve damage that affects up to half of diabetes patients and is an independent risk factor for falls. Clinical implications of DPN-related falls include injury, psychological distress and physical activity curtailment. This review describes how the sensory and motor deficits associated with DPN underpin biomechanical alterations to the pattern of walking (gait), which contribute to balance impairments underpinning falls. Changes to gait with diabetes occur even before the onset of measurable DPN, but changes become much more marked with DPN. Gait impairments with diabetes and DPN include alterations to walking speed, step length, step width and joint ranges of motion. These alterations also impact the rotational forces around joints known as joint moments, which are reduced as part of a natural strategy to lower the muscular demands of gait to compensate for lower strength capacities due to diabetes and DPN. Muscle weakness and atrophy are most striking in patients with DPN, but also present in non-neuropathic diabetes patients, affecting not only distal muscles of the foot and ankle, but also proximal thigh muscles. Insensate feet with DPN cause a delayed neuromuscular response immediately following foot–ground contact during gait and this is a major factor contributing to increased falls risk. Pronounced balance impairments measured in the gait laboratory are only seen in DPN patients and not non-neuropathic diabetes patients. Self-perception of unsteadiness matches gait laboratory measures and can distinguish between patients with and without DPN. Diabetic foot ulcers and their associated risk factors including insensate feet with DPN and offloading devices further increase falls risk. Falls prevention strategies based on sensory and motor mechanisms should target those most at risk of falls with DPN, with further research needed to optimise interventions.

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EVAL Cane: An IoT based Smart Cane for the Evaluation of Walking Gait and Environment

2020 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB) ◽

10.1109/bmsb49480.2020.9379468 ◽

2020 ◽

Author(s):

Ting Wang ◽

Rupert Grobler ◽

Eric Monacelli

Keyword(s):

Walking Gait

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Task space adaptation via the learning of gait controllers of magnetic soft millirobots

The International Journal of Robotics Research ◽

10.1177/02783649211021869 ◽

2021 ◽

pp. 027836492110218

Author(s):

Sinan O. Demir ◽

Utku Culha ◽

Alp C. Karacakol ◽

Abdon Pena-Francesch ◽

Sebastian Trimpe ◽

...

Keyword(s):

Human Body ◽

Degrees Of Freedom ◽

Bayesian Optimization ◽

Small Scale ◽

Soft Robots ◽

Modeling And Control ◽

Walking Gait ◽

Physical Experiments ◽

Efficient Learning ◽

Task Environments

Untethered small-scale soft robots have promising applications in minimally invasive surgery, targeted drug delivery, and bioengineering applications as they can directly and non-invasively access confined and hard-to-reach spaces in the human body. For such potential biomedical applications, the adaptivity of the robot control is essential to ensure the continuity of the operations, as task environment conditions show dynamic variations that can alter the robot’s motion and task performance. The applicability of the conventional modeling and control methods is further limited for soft robots at the small-scale owing to their kinematics with virtually infinite degrees of freedom, inherent stochastic variability during fabrication, and changing dynamics during real-world interactions. To address the controller adaptation challenge to dynamically changing task environments, we propose using a probabilistic learning approach for a millimeter-scale magnetic walking soft robot using Bayesian optimization (BO) and Gaussian processes (GPs). Our approach provides a data-efficient learning scheme by finding the gait controller parameters while optimizing the stride length of the walking soft millirobot using a small number of physical experiments. To demonstrate the controller adaptation, we test the walking gait of the robot in task environments with different surface adhesion and roughness, and medium viscosity, which aims to represent the possible conditions for future robotic tasks inside the human body. We further utilize the transfer of the learned GP parameters among different task spaces and robots and compare their efficacy on the improvement of data-efficient controller learning.

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