Learning and memory in molecular recognition: from olfactory to immune system

Storing memory for molecular recognition is an efficient strategy for sensing and responding to external stimuli. Biological processes use different strategies to store memory. For example, the olfactory system uses general receptors that can bind to a large number of natural molecular mixtures, while the immune memory is encoded by a multitude of specialized receptors to target diverse antigens. To probe the emergence of distinct memory strategies, we propose a dynamical Hebbian network model that can efficiently learn and store memory against evolving patterns. Specifically, we demonstrate that specialized memory emerges as an optimal strategy against evolving patterns, whereas a general distributed memory can be used to recognize static patterns. Our results shed light on the distinct encoding of memory in the olfactory and the immune system, where the former uses general receptors against static molecular mixtures and the latter uses specialized receptors against evolving pathogens.