  Animals can quickly learn the timing of events with fixed intervals and their
rate of acquisition does not depend on the length of the interval. In contrast,
recurrent neural networks that use gradient based learning have difficulty
predicting the timing of events that depend on stimulus that occurred long ago.
We present the latent time-adaptive drift-diffusion model (LTDDM), an extension
to the time-adaptive drift-diffusion model (TDDM), a model for animal learning
of timing that exhibits behavioural properties consistent with experimental
data from animals. The performance of LTDDM is compared to that of a state of
the art long short-term memory (LSTM) recurrent neural network across three
timing tasks. Differences in the relative performance of these two models is
discussed and it is shown how LTDDM can learn these events time series orders
of magnitude faster than recurrent neural networks.
