  Neuronal circuits can learn and replay firing patterns evoked by sequences of
sensory stimuli. After training, a brief cue can trigger a spatiotemporal
pattern of neural activity similar to that evoked by a learned stimulus
sequence. Network models show that such sequence learning can occur through the
shaping of feedforward excitatory connectivity via long term plasticity.
Previous models describe how event order can be learned, but they typically do
not explain how precise timing can be recalled. We propose a mechanism for
learning both the order and precise timing of event sequences. In our recurrent
network model, long term plasticity leads to the learning of the sequence,
while short term facilitation enables temporally precise replay of events.
Learned synaptic weights between populations determine the time necessary for
one population to activate another. Long term plasticity adjusts these weights
so that the trained event times are matched during playback. While we chose
short term facilitation as a time-tracking process, we also demonstrate that
other mechanisms, such as spike rate adaptation, can fulfill this role. We also
analyze the impact of trial-to-trial variability, showing how observational
errors as well as neuronal noise result in variability in learned event times.
The dynamics of the playback process determine how stochasticity is inherited
in learned sequence timings. Future experiments that characterize such
variability can therefore shed light on the neural mechanisms of sequence
learning.
