  Blind source separation, i.e. extraction of independent sources from a
mixture, is an important problem for both artificial and natural signal
processing. Here, we address a special case of this problem when sources (but
not the mixing matrix) are known to be nonnegative, for example, due to the
physical nature of the sources. We search for the solution to this problem that
can be implemented using biologically plausible neural networks. Specifically,
we consider the online setting where the dataset is streamed to a neural
network. The novelty of our approach is that we formulate blind nonnegative
source separation as a similarity matching problem and derive neural networks
from the similarity matching objective. Importantly, synaptic weights in our
networks are updated according to biologically plausible local learning rules.
