Neurophysiology and neural engineering: a review.

J Neurophysiol

Department of Physiology, University of Alberta, Edmonton, Alberta, Canada

Published: August 2017

Neurophysiology is the branch of physiology concerned with understanding the function of neural systems. Neural engineering (also known as neuroengineering) is a discipline within biomedical engineering that uses engineering techniques to understand, repair, replace, enhance, or otherwise exploit the properties and functions of neural systems. In most cases neural engineering involves the development of an interface between electronic devices and living neural tissue. This review describes the origins of neural engineering, the explosive development of methods and devices commencing in the late 1950s, and the present-day devices that have resulted. The barriers to interfacing electronic devices with living neural tissues are many and varied, and consequently there have been numerous stops and starts along the way. Representative examples are discussed. None of this could have happened without a basic understanding of the relevant neurophysiology. I also consider examples of how neural engineering is repaying the debt to basic neurophysiology with new knowledge and insight.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5558026PMC
http://dx.doi.org/10.1152/jn.00149.2017DOI Listing

Publication Analysis

Top Keywords

neural engineering
20
neural
8
neural systems
8
electronic devices
8
devices living
8
living neural
8
engineering
7
neurophysiology
4
neurophysiology neural
4
engineering review
4

Similar Publications

Shaping the structural dynamics of motor learning through cueing during sleep.

Sleep

January 2025

UR2NF-Neuropsychology and Functional Neuroimaging Research Unit affiliated at CRCN - Centre for Research in Cognition and Neurosciences and UNI - ULB Neuroscience Institute, Université Libre de Bruxelles (ULB), Brussels, Belgium.

Enhancing the retention of recent memory traces through sleep reactivation is possible via Targeted Memory Reactivation (TMR), involving cueing learned material during post-training sleep. Evidence indicates detectable short-term microstructural changes in the brain within an hour after motor sequence learning, and post-training sleep is believed to contribute to the consolidation of these motor memories, potentially leading to enduring microstructural changes. In this study, we explored how TMR during post-training sleep affects performance gains and delayed microstructural remodeling, using both standard Diffusion Tensor Imaging (DTI) and advanced Neurite Orientation Dispersion & Density Imaging (NODDI).

View Article and Find Full Text PDF

Automatic 4D mitral valve segmentation from transesophageal echocardiography: a semi-supervised learning approach.

Med Biol Eng Comput

January 2025

Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy.

Performing automatic and standardized 4D TEE segmentation and mitral valve analysis is challenging due to the limitations of echocardiography and the scarcity of manually annotated 4D images. This work proposes a semi-supervised training strategy using pseudo labelling for MV segmentation in 4D TEE; it employs a Teacher-Student framework to ensure reliable pseudo-label generation. 120 4D TEE recordings from 60 candidates for MV repair are used.

View Article and Find Full Text PDF

EOSnet: Embedded Overlap Structures for Graph Neural Networks in Predicting Material Properties.

J Phys Chem Lett

January 2025

Department of Physics, Rutgers University, Newark, New Jersey 07102, United States of America.

Graph Neural Networks (GNNs) have emerged as powerful tools for predicting material properties, yet they often struggle to capture many-body interactions and require extensive manual feature engineering. Here, we present EOSnet (Embedded Overlap Structures for Graph Neural Networks), a novel approach that addresses these limitations by incorporating Gaussian Overlap Matrix (GOM) fingerprints as node features within the GNN architecture. Unlike models that rely on explicit angular terms or human-engineered features, EOSnet efficiently encodes many-body interactions through orbital overlap matrices, providing a rotationally invariant and transferable representation of atomic environments.

View Article and Find Full Text PDF

Bioinspired Smart Triboelectric Soft Pneumatic Actuator-Enabled Hand Rehabilitation Robot.

Adv Mater

January 2025

Division of Intelligent and Biomechanical Systems, State Key Laboratory of Tribology in Advanced Equipment, Department of Mechanical Engineering, Tsinghua University, Haidian, Beijing, 100084, China.

Quantitative assessment for post-stroke spasticity remains a significant challenge due to the encountered variable resistance during passive stretching, which can lead to the widely used modified Ashworth scale (MAS) for spasticity assessment depending heavily on rehabilitation physicians. To address these challenges, a high-force-output triboelectric soft pneumatic actuator (TENG-SPA) inspired by a lobster tail is developed. The bioinspired TENG-SPA can generate approximately 20 N at 0.

View Article and Find Full Text PDF

Accurate segmentation of the left ventricular myocardium in cardiac MRI is essential for developing reliable deep learning models to diagnose left ventricular non-compaction cardiomyopathy (LVNC). This work focuses on improving the segmentation database used to train these models, enhancing the quality of myocardial segmentation for more precise model training. We present a semi-automatic framework that refines segmentations through three fundamental approaches: (1) combining neural network outputs with expert-driven corrections, (2) implementing a blob-selection method to correct segmentation errors and neural network hallucinations, and (3) employing a cross-validation process using the baseline U-Net model.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!