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Neurogenesis and Regeneration of Nervous Tissue

Neurogenesis and Regeneration of Nervous Tissue

In the CNS, neurogenesis, the birth of new neurons from stem cells, is limited to the hippocampus in adults. In other regions of the brain and spinal cord, neurogenesis is almost non-existent due to inhibitory influences from neuroglia, especially oligodendrocytes, and the absence of growth-stimulating cues. The myelin produced by oligodendrocytes in the CNS inhibits neuronal regeneration. Furthermore, astrocytes proliferate rapidly after neuronal damage, forming scar tissue that physically...

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Related Experiment Video

Updated: May 13, 2026

Engineering Platform and Experimental Protocol for Design and Evaluation of a Neurally-controlled Powered Transfemoral Prosthesis

Engineering Platform and Experimental Protocol for Design and Evaluation of a Neurally-controlled Powered Transfemoral Prosthesis

Published on: July 22, 2014

Performance-Recoverable Closed-Loop Neuroprosthetic System.

Yewon Kim^1,2, Kyumin Kang^1,2, Ja Hoon Koo³

¹Department of Electrical and Computer Engineering, Sungkyunkwan University (SKKU), Suwon, 16419, Republic of Korea.

Advanced Materials (Deerfield Beach, Fla.)

|June 27, 2025

Summary

This summary is machine-generated.

This study introduces a novel soft, closed-loop neuroprosthesis that self-heals and uses machine learning to correct performance drift, enabling long-term stable function for sensory-motor recovery.

Keywords:

closed‐loop machine learning neuroprosthetic performance‐recovery self‐healing sensory‐motor function stretchable

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Area of Science:

Bioelectronics
Neuroprosthetics
Materials Science

Background:

Soft bioelectronics are crucial for neuroprosthetic systems aiming to restore sensory-motor functions.
Long-term stability of current neuroprostheses is hindered by material fatigue and mechanical damage, leading to performance drift.
Lack of intelligent feedback limits compensation for lost nervous system functions.

Purpose of the Study:

To develop a novel soft, closed-loop neuroprosthetic system for long-term, stable operation.
To address material fatigue and performance drift in chronic implantable devices.
To enable intelligent feedback for enhanced sensory-motor function recovery.

Main Methods:

Development of a tough, self-healing, stretchable, and conductive bilayer material for sensors and electrodes.
Integration of two central processing units for closed-loop sensory-motor operations.
Implementation of machine-learning-driven correction and spontaneous performance recovery mechanisms.

Main Results:

The novel neuroprosthesis demonstrates spontaneous performance recovery and machine-learning-driven correction capabilities.
The developed bilayer material exhibits high conductivity and exceptional cyclic durability.
Successful in vivo implantation and operation for over 4 weeks, showing resilience to mechanical damage.

Conclusions:

The performance-recoverable closed-loop neuroprosthesis effectively overcomes material fatigue-induced malfunctions.
This innovation advances the potential for stable, long-term neuroprosthetic applications in sensory-motor function restoration.
The system integrates advanced materials and intelligent processing for robust artificial nervous system operations.