Data-driven reduced modeling of neural dynamics
Spectral submanifolds can be used to reduce recurrent neural networks to low-dimensional models, revealing their core dynamics, and authors uncover robust structures underlying decision-making and working-memory tasks, providing predictions about the underlying behavior of neural computations.