To predict and 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. However, we still lack a systematic understanding of the neural mechanisms of how animals perceive, maintain, and use time intervals ranging from hundreds of milliseconds to multi-seconds in working memory , the combination of time information with spatial information processing and decision making , and the reasons for the strong neuronal temporal signal even when animals performing tasks in which time information is not required  . Here, we addressed these problems by training neural network models. 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 . 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 . Our work discloses fundamental computional principles of temporal processing, and is supported by and gives predictions to a number of experimental phenomena.
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.
D
predict and
R
However, we still lack a systematic understanding of the neural mechanisms of how
How the
R
, the combination of time information
? How temporal information is processed concurrently
D
processing
R
, and the reasons for the
? Why there are
R
signal even when animals performing
signals in
R
time
temporal
A
? A systematic understanding of the underlying neural mechanisms is still lacking
R
by training
using supervised training of recurrent
R
reveal
revealed
R
time intervals through the evolution of population state
elapsed time through state evolution
R
by line attractor along which most neurons vary their activities monotonically with the duration of the maintained interval, and produce or compare
in working memory in the monotonic increase or decrease of the firing rates of interval-tuned neurons, and compare or produce
R
the evolution velocity of population state . Time and non-time information is
state evolution speed. Temporal and non-temporal information are
R
which facilitates generalizable decoding of time and non-time information . This is based on the network structure of
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
R
non-timing
non-temporal
R
Strong temporal signal
We identified four factors that facilitate strong temporal signals
R
arises from high temporal complexity of task, broad input tuning, multi-tasking and timing anticipation
, including the anticipation of coming events
R
computional
computational