We show that under suitable assumptions (primarily linearization) a simple and
perspicuous online learning rule for Information Bottleneck optimization with
spiking neurons can be derived. This rule performs on common benchmark tasks
as well as a rather complex rule that has previously been proposed [2].
Furthermore, the transparency of this new learning rule makes a theoretical
analysis of its convergence properties feasible. If this learning rule is
applied to an assemble of neurons, it provides a theoretically founded method
for performing principal component analysis (PCA) with spiking neurons. In
addition it makes it possible to preferentially extract those principal
components from incoming signals X that are related to some additional target
signal
. This target signal
(also called relevance variable)
could represent in a biological interpretation proprioception feedback, input
from other sensory modalities, or top-down signals.