Resonator network papers in print

December 1, 2020

An exciting new model of factorization in neural circuits, called resonator networks, has been published in the December issue of Neural Computation. Appearing as a two-part series, the articles by Paxon Frady, Spencer Kent, and collaborators, detail how factorization problems arise in cognition and how resonator networks may be a powerful new tool for solving such problems efficiently. They explain how resonator networks define a nonlinear dynamical system with fascinating properties and how using this system to solve an important vector factorization problem is superior to a number of alternative approaches. This opens the door for applications of Vector Symbolic Architectures (VSAs, otherwise known as HD Computing) to a much wider range of problems than previously considered, and is a cornerstone of our ongoing efforts to explore VSAs as framework for cognitive representation in the brain. You can view both articles by visiting our publications page.