Before revision:  We reveal that neural networks perceive time intervals through the evolution of population state along a stereotypical trajectory, maintain time intervals by line attractors along which the activities of most neurons vary  monotonically with the duration of the maintained interval, and adjust the evolution speed of the statetrajectory for producing or comparing time intervals. Spatial information or decision choice preserves the profiles of neuronal activities as functions of time intervals maintained in working memory or flow of time , and is coded in the amplitudes of these profiles. Decision making is combined with time perception through two firing sequences with mutual inhibition.
After revision:  We reveal that neural networks perceive time intervals through the evolution of population state along a stereotypical trajectory, maintain time intervals by line attractors along which the activities of most neurons vary monotonically with the duration of the maintained interval, and produce or compare time intervals by scaling the evolution velocity of population state. Time and non-time information is coded in subspaces orthogonal with each other, which facilitates generalizable decoding of time and non-time information. This is based on the network structure of multiple feedforward sequences that mutually excite or inhibit depending on whether their preferences of non-timing information are similar or not. Strong temporal signal in non-timing tasks arises from high temporal complexity of task, broad input tuning, multi-tasking and timing anticipation.