Delves into the fundamental limits of gradient-based learning on neural networks, covering topics such as binomial theorem, exponential series, and moment-generating functions.
Explores the bending stiffness of soft neural interfaces, including axons and penetrating probes, with ideal geometrical models and elastic modulus ranges.
