Stochasticity arises in many different contexts and many different forms in biological systems. This talk will present recent novel approaches to modeling noise, from the single cell level to the collective level in high level neural information processing and in insect societies. Stochastic firing in neurons has often been modeled using the point process formalism, in which the intensity of the process may be time dependent or a stochastic process itself. However, this approach is limited when nonlinear dynamical effects related to excitability are of interest, which in turn leads to drift-diffusion process formulations. We describe such a dynamical model for electroreceptor firing activity, in which a fast and a slow stochastic process drive the deterministic dynamics. This is shown to be needed for explaining the first and second order firing statistics, as well as the regularity of spike train over different counting windows (Fano factor).

We then present a recent model for the motion of eyes and of attention during reading. The attention is made stochastic by distributing it over words. This model is more parsimonious than existing ones, and is shown to nevertheless fit fixation time data. Finally, we discuss stochastic task allocation in ants societies. Individual ants sample their environment and make decisions at random times to switch task. The inherent stochasticity enables a novel formulation of this problem in terms of iterated function systems as well as birth-death stochastic processes (state dependent random walks).