Topic: multi-compartment models

In neuroscience, ordinary differential equations (ODEs) are widely used to simulate the dynamics of neural systems, but identifying parameters that explain experimental measurements is challenging. In particular, while these models are differentiable and would allow for gradient-based parameter optimization, the nonlinear dynamics inherent of neural systems often lead to many local minima and extreme sensitivity to initial conditions. To address this, we improve a recently developed probabilistic integration method that enables gradient-based parameter inference in ODEs known as ``Physics-Enha