Neural Trajectory

Neural trajectory research focuses on modeling and analyzing the paths of data points—representing neural activity, physical processes, or abstract concepts—through a high-dimensional space. Current efforts utilize various neural network architectures, including variational autoencoders, Kalman filters, and physics-informed neural networks, to represent and learn these trajectories, often incorporating constraints from physics or geometry to improve accuracy and interpretability. This work has implications for diverse fields, from improving robotics and computer graphics through efficient trajectory planning and generation to advancing neuroscience by revealing underlying principles of neural computation and brain connectivity.