Once upon a (slow) time in the land of recurrent neuronal networks…

- Flexible time scales are a hallmark of nonlinear systems.
- Two distinct mechanisms for slow time scale dynamics in networks of recurrently coupled neurons.
- Network time scales diverges at the transition between stable and chaotic firing rate dynamics.
- Noise induced transitions in metastable networks impart a slow time scale.

The brain must both react quickly to new inputs as well as store a memory of past activity. This requires biology that operates over a vast range of time scales. Fast time scales are determined by the kinetics of synaptic conductances and ionic channels; however, the mechanics of slow time scales are more complicated. In this opinion article we review two distinct network-based mechanisms that impart slow time scales in recurrently coupled neuronal networks. The first is in strongly coupled networks where the time scale of the internally generated fluctuations diverges at the transition between stable and chaotic firing rate activity. The second is in networks with finitely many members where noise-induced transitions between metastable states appear as a slow time scale in the ongoing network firing activity. We discuss these mechanisms with an emphasis on their similarities and differences.