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Animals learn tasks requiring a sequence of actions over time. Waiting agiven time before taking an action is a simple example. Mimicry is a complexexample, e.g. in humans, humming a brief tune you have just heard.Re-experiencing a sensory pattern mentally must involve reproducing a sequenceof neural activities over time. In mammals, neurons in prefrontal cortex havetime-dependent firing rates that vary smoothly and slowly in a stereotypedfashion. We show through modeling that a Many are Equal computation can usesuch slowly-varying activities to identify each timepoint in a sequence by thepopulation pattern of activity at the timepoint. The MAE operation implementedhere is facilitated by a common inhibitory conductivity due to a theta rhythm.Sequences of analog values of discrete events, exemplified by a brief tunehaving notes of different durations and intensities, can be learned in a singletrial through STDP. An action sequence can be played back sped up, slowed down,or reversed by modulating the system that generates the slowly changingstereotyped activities. Synaptic adaptation and cellular post-hyperpolarizationrebound contribute to robustness. An ability to mimic a sequence only secondsafter observing it requires the STDP to be effective within seconds.

Sequence reproduction, single trial learning, and mimicry based on a mammalian-like distributed code for time