Dynamic thresholds for controlling encoding and retrieval operations in localist (or distributed) neural networks: The need for biologically plausible implementations

A dynamic threshold, which controls the nature and course of learning, is a pivotal concept in Page's general localist framework. This commentary addr esses various issues surrounding biologically plausible implementations for such thresholds. Relevant previous research is noted and the particular di fficulties relating to the creation of so-called instance representations a re highlighted. It is stressed that these issues also apply to distributed models.