Variational inference is a family of optimisation-based methods for approximating complex posterior distributions in Bayesian models. By transforming inference into an optimisation problem, these ...

Across modern data-intensive disciplines, the union of numerical computation, statistics, and machine learning has become ...

This work considers a class of canonical neural networks comprising rate coding models, wherein neural activity and plasticity minimise a common cost function—and plasticity is modulated with a ...