A memory-based random walk model to understand diffusion in crowded heterogeneous environment

Abstract

We study memory based random walk models to understand diffusive motion in crowded heterogeneous environment. The models considered are non-Markovian as the current move of the random walk models is determined by randomly selecting a move from history. At each step, particle can take right, left or stay moves which is correlated with the randomly selected past step. There is a perfect stay-stay correlation which ensures that the particle does not move if the randomly selected past step is a stay move. The arXiv:1712.04151v1 [cond-mat.stat-mech]