Animals learn tasks requiring a sequence of actions over time. Waiting a
given time before taking an action is a simple example. Mimicry is a complex
example, e.g. in humans, humming a brief tune you have just heard.
Re-experiencing a sensory pattern mentally must involve reproducing a sequence
of neural activities over time. In mammals, neurons in prefrontal cortex have
time-dependent firing rates that vary smoothly and slowly in a stereotyped
fashion. We show through modeling that a Many are Equal computation can use
such slowly-varying activities to identify each timepoint in a sequence by the
population pattern of activity at the timepoint. The MAE operation implemented
here is facilitated by a common inhibitory conductivity due to a theta rhythm.
Sequences of analog values of discrete events, exemplified by a brief tune
having notes of different durations and intensities, can be learned in a single
trial through STDP. An action sequence can be played back sped up, slowed down,
or reversed by modulating the system that generates the slowly changing
stereotyped activities. Synaptic adaptation and cellular post-hyperpolarization
rebound contribute to robustness. An ability to mimic a sequence only seconds
after observing it requires the STDP to be effective within seconds.Comment: 18 page