基于大分子自组装的关联模式识别
Associative pattern recognition through macro-molecular self-assembly
摘要：
我们发现，大分子自组装可以识别和分类的$N$不同的分子种类的浓度的高维模式。与联想神经网络类似，这里的识别利用动态吸引子来识别和重建部分损坏的模式。传统的模式识别理论的参数，如稀疏性，保真度和容量相关的物理参数，如成核障碍，相互作用范围，和非平衡组装力。值得注意的是，我们发现自组装与连续吸引子神经网络（例如存储空间记忆的位置细胞网络）而不是离散记忆网络具有更大的相似性。这种关系表明，这里看到的特征和权衡与自组装或神经网络模型的细节无关，而是通过短程相互作用进行的关联模式识别所固有的。
英文摘要：
We show that macro-molecular self-assembly can recognize and classify high-dimensional patterns in the concentrations of $N$ distinct molecular species. Similar to associative neural networks, the recognition here leverages dynamical attractors to recognize and reconstruct partially corrupted patterns. Traditional parameters of pattern recognition theory, such as sparsity, fidelity, and capacity are related to physical parameters, such as nucleation barriers, interaction range, and non-equilibrium assembly forces. Notably, we find that self-assembly bears greater similarity to continuous attractor neural networks, such as place cell networks that store spatial memories, rather than discrete memory networks. This relationship suggests that features and trade-offs seen here are not tied to details of self-assembly or neural network models but are instead intrinsic to associative pattern recognition carried out through short-ranged interactions.
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