The brain's ability to seamlessly assimilate and process temporal information is critical to most behaviors, from understanding speech to playing the piano. Such timing and temporal processing represent a fundamental neural computation; and it will not be possible to develop general models of brain function without an understanding of how the brain solves temporal tasks. We have proposed that sensory timing (e.g., interval discrimination) emerges from the dynamics of the internal states of neural networks. The internal state, is defined not only by ongoing activity (the active state) but by time-varying synaptic properties, such as short-term synaptic plasticity (the hidden state). The differential STP profile at excitatory and inhibitory synapses ensures that the E/I balance changes dynamically on the time scale of hundreds of milliseconds; and our experimental and theoretical results suggest that these dynamic E/I changes contribute to sensory timing. Motor timing requires networks to actively generate responses, and thus rely on ongoing neural dynamics. It has been proposed that motor timing emerges from the dynamics of balanced recurrent neural networks. A long-standing limitation of this theory, however, is that the relevant regime is chaotic. We show that it is possible to tame chaos in firing rate recurrent networks, by tuning the weight of the recurrent networks in a manner that the E/I balance is maintained.