  Neural populations exposed to a certain stimulus learn to represent it
better. However, the process that leads local, self-organized rules to do so is
unclear. We address the question of how can a neural periodic input be learned
and use the Differential Hebbian Learning framework, coupled with a homeostatic
mechanism to derive two self-consistency equations that lead to increased
responses to the same stimulus. Although all our simulations are done with
simple Leaky-Integrate and Fire neurons and standard Spiking Time Dependent
Plasticity learning rules, our results can be easily interpreted in terms of
rates and population codes.
