Neural Representation

Neural representation research focuses on understanding how information is encoded and processed within neural networks and biological brains, aiming to improve both artificial intelligence and our understanding of cognition. Current research emphasizes characterizing the geometric and topological properties of these representations, often using techniques like representational similarity analysis and topological data analysis, and exploring various model architectures including transformers, Hopfield networks, and neural fields. These investigations are crucial for enhancing the robustness, efficiency, and interpretability of AI systems and for gaining deeper insights into brain function and dysfunction.