  Neural networks are composed of neurons and synapses, which are responsible
for learning in a slow adaptive dynamical process. Here we experimentally show
that neurons act like independent anisotropic multiplex hubs, which relay and
mute incoming signals following their input directions. Theoretically, the
observed information routing enriches the computational capabilities of neurons
by allowing, for instance, equalization among different information routes in
the network, as well as high-frequency transmission of complex time-dependent
signals constructed via several parallel routes. In addition, this kind of hubs
adaptively eliminate very noisy neurons from the dynamics of the network,
preventing masking of information transmission. The timescales for these
features are several seconds at most, as opposed to the imprint of information
by the synaptic plasticity, a process which exceeds minutes. Results open the
horizon to the understanding of fast and adaptive learning realities in higher
cognitive functionalities of the brain.
