To predict and maximize future rewards in this ever-changing world, animals must be able to discover the temporal structure of stimuli and then take the right action at the right time. However, we still lack a systematic understanding of the neural mechanism of how animals perceive, maintain, and use time intervals ranging from hundreds of milliseconds to multi-seconds in working memory and appropriately combine time information with spatial information processing and decision making  . 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. 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. Our work discloses fundamental principles of the neuronal coding of time that supports the brain for flexible temporal processing. These principles facilitate generalizable decoding of time and non-time information .
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 .
R
take the right action
anticipate or act correctly
R
mechanism
mechanisms
R
and appropriately combine
, the combination of
A
, and the reasons for the strong neuronal temporal signal even when animals performing tasks in which time information is not required
R
this problem
these problems
R
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
. We
D
a
R
attractors along which the activities of
attractor along which
A
their activities
R
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.
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.
R
principles of the neuronal coding of time that supports the brain for flexible temporal processing. These principles facilitate generalizable decoding of time and non-time information
computional principles of temporal processing, and is supported by and gives predictions to a number of experimental phenomena