Neuron Level

Neuron-level analysis aims to understand the inner workings of deep neural networks by examining the function of individual neurons. Current research focuses on developing methods to interpret neuron behavior across various model architectures, including large language models (LLMs) and convolutional neural networks (CNNs), often leveraging techniques like neuron activation mapping, concept discovery, and natural language descriptions to explain their roles. This research is crucial for improving model transparency, robustness, and fairness, ultimately leading to more trustworthy and reliable AI systems in diverse applications such as medical image analysis and natural language processing. Furthermore, understanding neuron-level representations offers insights into how these models learn and represent knowledge, bridging the gap between artificial and biological neural systems.