The Dynamic Net Architecture: Learning Robust and Holistic Visual Representations Through Self-Organizing Networks

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.