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.

翻译：我们提出了一种名为“动态网络架构”（DNA）的新型智能系统架构，该架构依赖于递归稳定网络，并讨论了其在视觉领域的应用。我们的架构模拟了一个（大脑皮层）区域，其中基本特征神经元编码视觉结构的细节，而此类神经元组成的相干网络则建模整体对象结构。通过将区域网络中较小或较大的相干片段解释为复杂特征，我们的模型编码了与人工神经网络（ANNs）本质上不同的层次化特征表示。DNA模型基于动态连接主义原理运行，其中源自初始传入信号的神经激活通过自组织机制实现稳定化，该机制由赫布可塑性及周期性增强的抑制共同促成。与依赖前馈连接和误差反向传播的ANNs相比，我们认为这种处理范式能够产生高度鲁棒的表征，因为通过采用动态侧向连接，神经激活中的无关细节被滤除，使后续处理步骤免受干扰噪声和过早决策的影响。我们通过将线段片段组合成更长的线条，实证验证了DNA的可行性，并表明即使每个空间位置引入高达$59\%$的噪声，代表线条的网络构建仍保持鲁棒性。此外，我们证明了该模型能够从部分遮挡的输入中重建预期特征，并且可以泛化到训练期间未观察到的模式。在本工作中，我们将DNA限制在单个皮层区域，重点关注其内部机制，同时深入探讨独立区域的优缺点。此外，我们还展望了未来工作如何通过整合多个区域来实现不变性物体识别。