AIMC Topic: Motor Activity
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Neural Interactions in Developing Rhythmogenic Spinal Networks: Insights From Computational Modeling.
Frontiers in neural circuits
Dec 23, 2020
The mechanisms involved in generation of rhythmic locomotor activity in the mammalian spinal cord remain poorly understood. These mechanisms supposedly rely on both intrinsic properties of constituting neurons and interactions between them. A subset ...
A goal-driven modular neural network predicts parietofrontal neural dynamics during grasping.
Proceedings of the National Academy of Sciences of the United States of America
Nov 30, 2020
One of the primary ways we interact with the world is using our hands. In macaques, the circuit spanning the anterior intraparietal area, the hand area of the ventral premotor cortex, and the primary motor cortex is necessary for transforming visual ...
Population coding in the cerebellum: a machine learning perspective.
Journal of neurophysiology
Oct 28, 2020
The cere resembles a feedforward, three-layer network of neurons in which the "hidden layer" consists of Purkinje cells (P-cells) and the output layer consists of deep cerebellar nucleus (DCN) neurons. In this analogy, the output of each DCN neuron i...
Robot-assisted Gait Training Using Welwalk in Hemiparetic Stroke Patients: An Effectiveness Study with Matched Control.
Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
Oct 19, 2020
OBJECTIVE: Although studies on the efficacy of the rehabilitation robot are increasing, there are few reports using the robot for gait training in the actual clinical setting. This study aimed to investigate the effectiveness of gait training using W...
The effect of a social robot intervention on sleep and motor activity of people living with dementia and chronic pain: A pilot randomized controlled trial.
Maturitas
Sep 24, 2020
OBJECTIVE: To investigate the effect of a social robot intervention on sleep and motor activity in nursing home residents living with dementia and chronic pain.
fMRI volume classification using a 3D convolutional neural network robust to shifted and scaled neuronal activations.
NeuroImage
Sep 5, 2020
Deep-learning methods based on deep neural networks (DNNs) have recently been successfully utilized in the analysis of neuroimaging data. A convolutional neural network (CNN) is a type of DNN that employs a convolution kernel that covers a local area...
Screening of Parkinsonian subtle fine-motor impairment from touchscreen typing via deep learning.
Scientific reports
Jul 28, 2020
Fine-motor impairment (FMI) is progressively expressed in early Parkinson's Disease (PD) patients and is now known to be evident in the immediate prodromal stage of the condition. The clinical techniques for detecting FMI may not be robust enough and...
Statistical measures of motor, sensory and cognitive performance across repeated robot-based testing.
Journal of neuroengineering and rehabilitation
Jul 2, 2020
BACKGROUND: Traditional clinical assessments are used extensively in neurology; however, they can be coarse, which can also make them insensitive to change. Kinarm is a robotic assessment system that has been used for precise assessment of individual...
Deep learning for EEG-based Motor Imagery classification: Accuracy-cost trade-off.
PloS one
Jun 11, 2020
Electroencephalography (EEG) datasets are often small and high dimensional, owing to cumbersome recording processes. In these conditions, powerful machine learning techniques are essential to deal with the large amount of information and overcome the...
Behavioral Activity Recognition Based on Gaze Ethograms.
International journal of neural systems
Jun 9, 2020
Noninvasive behavior observation techniques allow more natural human behavior assessment experiments with higher ecological validity. We propose the use of gaze ethograms in the context of user interaction with a computer display to characterize the ...
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