Emergent Representation

Emergent representation research investigates how complex, meaningful representations spontaneously arise in artificial neural networks during training, mirroring the development of cognitive abilities in biological systems. Current studies focus on understanding the influence of network architecture (e.g., recurrent autoencoders), training algorithms (e.g., forward-forward, self-supervised learning), and constraints (e.g., variance-invariance) on the type of representations that emerge, including spatial maps, symbolic codes, and disentangled content-style features. These findings offer valuable insights into the fundamental mechanisms of learning and representation in both artificial and biological intelligence, potentially leading to more efficient and interpretable AI systems and a deeper understanding of brain function.