System Neuroscience

Systems neuroscience aims to understand how neural circuits give rise to complex behaviors by analyzing large-scale neural data. Current research heavily utilizes machine learning techniques, including reinforcement learning, deep generative models (like transformers), and physically-constrained neural networks, to analyze and interpret this data, often focusing on identifying functional modules and decoding neural representations of behavior. These approaches are improving our ability to build accurate models of neural activity and ultimately contribute to a deeper understanding of brain function and dysfunction, potentially informing the development of new diagnostic and therapeutic tools.