  Naturalistic animal behavior exhibits a strikingly complex organization in
the temporal domain, whose variability stems from at least three sources:
hierarchical, contextual, and stochastic. What are the neural mechanisms and
computational principles generating such complex temporal features? In this
review, we provide a critical assessment of the existing behavioral and
neurophysiological evidence for these sources of temporal variability in
naturalistic behavior. We crystallize recent studies which converge on an
emergent mechanistic theory of temporal variability based on attractor neural
networks and metastable dynamics, arising from the coordinated interactions
between mesoscopic neural circuits. We highlight the crucial role played by
structural heterogeneities and by noise arising in mesoscopic circuits. We
assess the shortcomings and missing links in the current theoretical and
experimental literature and propose new directions of investigations to fill
these gaps.
