Marc Besse (Sorbonne)
Active systems, such as living cells, are traditionally modelled via self-propelled particles driven by internal forces. It is however often assumed that these internal forces do not depend on the environment which is questionable from a biological perspective. Here we use the framework of Generalized Langevin Equations (GLE) to go 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 themselves depending on the environment, qualitatively new behaviours emerge. These include memory-induced responses, controllable localization in complex landscapes, and suppression of motility-induced phase separation or enhanced jamming transitions. Our results demonstrate how minimal information processing capabilities, intrinsic to nonequilibrium systems like living cells, can profoundly influence both individual and collective behaviours. This framework bridges cell-scale activity and large-scale intelligent motion of active agents and offers insights relevant to systems ranging from synthetic colloids to biological collectives or robotic swarms.
Contact : M. Manghi, N. Destainville
