  To maximize future rewards in this ever-changing world, animals must be able
to discover the temporal structure of stimuli and then anticipate or act
correctly at the right time. How the animals perceive, maintain, and use time
intervals ranging from hundreds of milliseconds to multi-seconds in working
memory? How temporal information is processed concurrently with spatial
information and decision making? Why there are strong neuronal temporal signals
in tasks in which temporal information is not required? A systematic
understanding of the underlying neural mechanisms is still lacking. Here, we
addressed these problems using supervised training of recurrent neural network
models. We revealed that neural networks perceive elapsed time through state
evolution along stereotypical trajectory, maintain time intervals in working
memory in the monotonic increase or decrease of the firing rates of
interval-tuned neurons, and compare or produce time intervals by scaling state
evolution speed. Temporal and non-temporal information are coded in subspaces
orthogonal with each other, and the state trajectories with time at different
non-temporal information are quasi-parallel and isomorphic. Such coding
geometry facilitates the decoding generalizability of temporal and non-temporal
information across each other. The network structure exhibits multiple
feedforward sequences that mutually excite or inhibit depending on whether
their preferences of non-temporal information are similar or not. We identified
four factors that facilitate strong temporal signals in non-timing tasks,
including the anticipation of coming events. Our work discloses fundamental
computational principles of temporal processing, and is supported by and gives
predictions to a number of experimental phenomena.
