  Accurate estimation of nuclear masses and their prediction beyond the
experimentally explored domains of the nuclear landscape are crucial to an
understanding of the fundamental origin of nuclear properties and to many
applications of nuclear science, most notably in quantifying the $r$-process of
stellar nucleosynthesis. Neural networks have been applied with some success to
the prediction of nuclear masses, but they are known to have shortcomings in
application to extrapolation tasks. In this work, we propose and explore a
novel type of neural network for mass prediction in which the usual neuron-like
processing units are replaced by complex-valued product units that permit
multiplicative couplings of inputs to be learned from the input data. This
generalized network model is tested on both interpolation and extrapolation
data sets drawn from the Atomic Mass Evaluation. Its performance is compared
with that of several neural-network architectures, substantiating its
suitability for nuclear mass prediction. Additionally, a prediction-uncertainty
measure for such complex-valued networks is proposed that serves to identify
regions of expected low prediction error.
