  We introduce a distributed spatio-temporal artificial neural network
architecture (DISTANA). It encodes mesh nodes using recurrent, neural
prediction kernels (PKs), while neural transition kernels (TKs) transfer
information between neighboring PKs, together modeling and predicting
spatio-temporal time series dynamics. As a consequence, DISTANA assumes that
generally applicable causes, which may be locally modified, generate the
observed data. DISTANA learns in a parallel, spatially distributed manner,
scales to large problem spaces, is capable of approximating complex dynamics,
and is particularly robust to overfitting when compared to other competitive
ANN models. Moreover, it is applicable to heterogeneously structured meshes.
