Abstract

A fundamental characteristic of living systems is sensing and subsequent robust response to continuously varying environmental signals. Even seemingly “simple” systems, such as single cells or single-cell organisms, reveal higher-order computational capabilities that go beyond simple stimulus-response association. Developing a theoretical framework to characterize transient sets in dynamical systems, we describe how ghost sets can be utilized as a memory to integrate information from time-varying signals. We have identified experimentally that these states are an emergent feature of cell-surface receptor networks organized at criticality, which they exploit for memory-based navigation in changing environments. Furthermore, by formulating a theory of computation with ghost sets, we explore theoretically and experimentally basic paradigms of learning and memory on the level of single cells, but also neuronal networks.