Optimization Challenges in Bio-Physical Models

The chapter delves into the use of backprop learning rules for optimization in bio-physical models with numerous parameters, emphasizing the need for over-parameterized models to efficiently fit nonlinear functions. It explores the complexities of high-dimensional spaces, plasticity in mammalian systems, and the contrast with synaptic plasticity in C.elegans. The conversation challenges the notion of simplicity in neural models, discusses synaptic connection interpretation challenges, brain plasticity, and the impact of the environment on brain complexity.