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Updated: May 31, 2025

Slice Patch Clamp Technique for Analyzing Learning-Induced Plasticity
Published on: November 11, 2017
Neural reshaping: the plasticity of human brain and artificial intelligence in the learning process
Seyed-Ali Sadegh-Zadeh1, Mahboobe Bahrami2, Ommolbanin Soleimani3
1Department of Computing, School of Digital, Technologies and Arts, Staffordshire University Stoke-on-Trent ST4 2DE, UK.
Abstract:
This study explores the concept of neural reshaping and the mechanisms through which both human and artificial intelligence adapt and learn.
Objectives:
To investigate the parallels and distinctions between human brain plasticity and artificial neural network plasticity, with a focus on their learning processes.
Methods:
A comparative analysis was conducted using literature reviews and machine learning experiments, specifically employing a multi-layer perceptron neural network to examine regression and classification problems.
Results:
Experimental findings demonstrate that machine learning models, similar to human neuroplasticity, enhance performance through iterative learning and optimization, drawing parallels in strengthening and adjusting connections.
Conclusions:
Understanding the shared principles and limitations of neural and artificial plasticity can drive advancements in AI design and cognitive neuroscience, paving the way for future interdisciplinary innovations.
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