Before revision:  Here, we addressed this problem by training neural network models on four timing tasks: interval production, interval comparison, timed spatial reproduction, and timed decision making. We studied time-coding principles of the network after training, and found them consistent with existing experimental observations. 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.
After revision:  Here, we addressed this problem by training neural network models . 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.