Intermittent localization and fast spatial learning by non-Markov random walks with decaying memory (2509.01806v1)

Abstract: Random walks on lattices with preferential relocation to previously visited sites provide a simple modeling of the displacements of animals and humans. When the lattice contains a single impurity or resource site where the walker spends more time on average at each visit than on the other sites, the long range memory can suppress diffusion and induce by reinforcement a steady state localized around the resource. This phenomenon can be identified with a spatial learning process by the walker. Here we study theoretically and numerically how the decay of memory impacts learning in these models. If memory decays as $1/\tau$ or slower, where $\tau$ is the time backward into the past, the localized solutions are the same as with perfect, non-decaying memory and they are linearly stable. If forgetting is faster than $1/\tau$, for instance exponential, an unusual regime of intermittent localization is observed, where well localized periods of exponentially distributed duration are interspersed with intervals of diffusive motion. At the transition between the two regimes, for a kernel in $1/\tau$, the approach to the stable localized state is the fastest, opposite to the expected critical slowing down effect. Hence forgetting can allow the walker to save memory without compromising learning and to achieve a faster learning. These findings agree with biological evidence on the benefits of forgetting.