ESPCI web site
Memory and environment sensing in active systems
The behaviour of active systems, such as living cells, is usually modeled by self-propelled particles driven by internal forces and noise. However, these models often assume memoryless dynamics and no coupling of internal active forces to the environment. Here, we introduce a general theoretical framework that goes beyond this paradigm by incorporating internal state dynamics and environmental sensing into active particle models.
We show that when the self-propulsion of a particle depends on internal variables with their own complex dynamics - modulated by local environmental cues - new classes of behaviours emerge. These include memory-induced responses, controllable localization in complex landscapes, suppression of motility-induced phase separation, and enhanced jamming transitions. Our results demonstrate how minimal information processing capabilities, intrinsic to non-equilibrium systems with internal states like living cells, can profoundly influence both individual and collective behaviours. This framework bridges cell-scale activity and large-scale intelligent motion in active agents, and opens the way to the analysis or design of systems ranging from synthetic colloids to biological collectives and robotic swarms.