  Artificial neural networks, one of the most successful approaches to
supervised learning, were originally inspired by their biological counterparts.
However, the most successful learning algorithm for artificial neural networks,
backpropagation, is considered biologically implausible. We contribute to the
topic of biologically plausible neuronal learning by building upon and
extending the equilibrium propagation learning framework. Specifically, we
introduce: a new neuronal dynamics and learning rule for arbitrary network
architectures; a sparsity-inducing method able to prune irrelevant connections;
a dynamical-systems characterization of the models, using Lyapunov theory.
