  In analyzing information streamed by sensory organs, our brains face
challenges similar to those solved in statistical signal processing. This
suggests that biologically plausible implementations of online signal
processing algorithms may model neural computation. Here, we focus on such
workhorses of signal processing as Principal Component Analysis (PCA) and
whitening which maximize information transmission in the presence of noise. We
adopt the similarity matching framework, recently developed for principal
subspace extraction, but modify the existing objective functions by adding a
decorrelating term. From the modified objective functions, we derive online PCA
and whitening algorithms which are implementable by neural networks with local
learning rules, i.e. synaptic weight updates that depend on the activity of
only pre- and postsynaptic neurons. Our theory offers a principled model of
neural computations and makes testable predictions such as the dropout of
underutilized neurons.
