  Time perception is the phenomenological experience of time by an individual.
In this paper, we study how to replicate neural mechanisms involved in time
perception, allowing robots to take a step towards temporal cognition. Our
framework follows a twofold biologically inspired approach. The first step
consists of estimating the passage of time from sensor measurements, since
environmental stimuli influence the perception of time. Sensor data is modeled
as Gaussian processes that represent the second-order statistics of the natural
environment. The estimated elapsed time between two events is computed from the
maximum likelihood estimate of the joint distribution of the data collected
between them. Moreover, exactly how time is encoded in the brain remains
unknown, but there is strong evidence of the involvement of dopaminergic
neurons in timing mechanisms. Since their phasic activity has a similar
behavior to the reward prediction error of temporal-difference learning models,
the latter are used to replicate this behavior. The second step of this
approach consists therefore of applying the agent's estimate of the elapsed
time in a reinforcement learning problem, where a feature representation called
Microstimuli is used. We validate our framework by applying it to an experiment
that was originally conducted with mice, and conclude that a robot using this
framework is able to reproduce the timing mechanisms of the animal's brain.
