Neuroplasticity-inspired Learning in Neural Networks: Examining neuroplasticity-inspired learning mechanisms for enabling neural networks to adapt and reorganize in response to changes

Abstract

Neural networks, inspired by the brain's ability to adapt and reorganize, have shown remarkable success in various tasks. This paper explores neuroplasticity-inspired learning mechanisms for neural networks, aiming to enhance their adaptability and robustness. We examine how neuroplasticity principles can be incorporated into neural network models, enabling them to learn continuously and adjust to changing environments. We discuss key concepts such as synaptic plasticity, Hebbian learning, and homeostatic plasticity, and their application in artificial neural networks. Additionally, we explore the implications of neuroplasticity-inspired learning on model performance, generalization, and transfer learning. Through this exploration, we aim to provide insights into leveraging neuroplasticity principles to enhance the learning capabilities of neural networks.