  Recent evidence suggests that neural information is encoded in packets and
may be flexibly routed from region to region. We have hypothesized that neural
circuits are split into sub-circuits where one sub-circuit controls information
propagation via pulse gating and a second sub-circuit processes graded
information under the control of the first sub-circuit. Using an explicit
pulse-gating mechanism, we have been able to show how information may be
processed by such pulse-controlled circuits and also how, by allowing the
information processing circuit to interact with the gating circuit, decisions
can be made. Here, we demonstrate how Hebbian plasticity may be used to
supplement our pulse-gated information processing framework by implementing a
machine learning algorithm. The resulting neural circuit has a number of
structures that are similar to biological neural systems, including a layered
structure and information propagation driven by oscillatory gating with a
complex frequency spectrum.
