Before revision:  We reveal that neural networks perceive time intervals through the evolution of population state along stereotypical trajectory, maintain time intervals by line attractor along which most neurons vary their activities 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 .
After revision:  We reveal that neural networks perceive time intervals through the evolution of population state along stereotypical trajectory, maintain time intervals by line attractor along which most neurons vary their activities monotonically with the duration of the maintained interval, and produce or compare time intervals by scaling state evolution speed. Temporal and non-temporal information are coded in subspaces orthogonal with each other, which facilitates generalizable decoding of time and non-time information .