  The design and analysis of spiking neural network algorithms will be
accelerated by the advent of new theoretical approaches. In an attempt at such
approach, we provide a principled derivation of a spiking algorithm for
unsupervised learning, starting from the nonnegative similarity matching cost
function. The resulting network consists of integrate-and-fire units and
exhibits local learning rules, making it biologically plausible and also
suitable for neuromorphic hardware. We show in simulations that the algorithm
can perform sparse feature extraction and manifold learning, two tasks which
can be formulated as nonnegative similarity matching problems.
