Functional Similarity

Functional similarity assesses how alike different systems are in their outputs or functional behavior, regardless of their internal structures. Current research focuses on developing and evaluating metrics for measuring functional similarity across diverse applications, including neural networks (where methods like model stitching and feature distillation are employed) and biological systems (analyzing spike train decoding in spiking neural networks). Understanding functional similarity is crucial for improving model efficiency (e.g., in distributed optimization), enhancing machine learning techniques (e.g., through improved feature distillation), and gaining insights into complex systems like the brain and terrorist organizations by identifying patterns of shared behavior.