  We present a novel intelligent-system architecture called "Dynamic Net
Architecture" (DNA) that relies on recurrence-stabilized networks and discuss
it in application to vision. Our architecture models a (cerebral cortical) area
wherein elementary feature neurons encode details of visual structures, and
coherent nets of such neurons model holistic object structures. By interpreting
smaller or larger coherent pieces of an area network as complex features, our
model encodes hierarchical feature representations essentially different than
artificial neural networks (ANNs).
  DNA models operate on a dynamic connectionism principle, wherein neural
activations stemming from initial afferent signals undergo stabilization
through a self-organizing mechanism facilitated by Hebbian plasticity alongside
periodically tightening inhibition. In contrast to ANNs, which rely on
feed-forward connections and backpropagation of error, we posit that this
processing paradigm leads to highly robust representations, as by employing
dynamic lateral connections, irrelevant details in neural activations are
filtered out, freeing further processing steps from distracting noise and
premature decisions.
  We empirically demonstrate the viability of the DNA by composing line
fragments into longer lines and show that the construction of nets representing
lines remains robust even with the introduction of up to $59\%$ noise at each
spatial location. Furthermore, we demonstrate the model's capability to
reconstruct anticipated features from partially obscured inputs and that it can
generalize to patterns not observed during training. In this work, we limit the
DNA to one cortical area and focus on its internals while providing insights
into a standalone area's strengths and shortcomings. Additionally, we provide
an outlook on how future work can implement invariant object recognition by
combining multiple areas.
