Activation Pattern

Activation patterns, the distributions of neural activity within artificial and biological neural networks, are a central focus in understanding how these systems process information and make decisions. Current research investigates activation patterns using various techniques, including contrastive learning, Kolmogorov-Arnold Networks, and analysis of activation patterns across different layers of deep neural networks, to improve model interpretability, enhance predictive accuracy, and develop more robust and reliable systems. This work has significant implications for advancing explainable AI, improving the safety and trustworthiness of AI systems, and furthering our understanding of brain function.