  The paper explores the capability of continuous-time recurrent neural
networks to store and recall precisely timed spike patterns. We show (by
numerical experiments) that this is indeed possible: within some range of
parameters, any random score of spike trains (for all neurons in the network)
can be robustly memorized and autonomously reproduced with stable accurate
relative timing of all spikes, with probability close to one. We also
demonstrate associative recall under noisy conditions.
  In these experiments, the required synaptic weights are computed offline, to
satisfy a template that encourages temporal stability.
