Storing memory for molecular recognition is an efficient strategy for responding to external stimuli. Biological processes use different strategies to store memory. In the olfactory cortex, synaptic connections form when stimulated by an odor and establish an associative distributed memory that can be retrieved upon reexposure to the same odors. In contrast, the immune system encodes specialized memory by diverse receptors that can recognize a multitude of evolving pathogens. Despite the mechanistic differences between memory storage in the olfactory system and the immune system, these processes can still be viewed as different information encoding strategies. Here, we develop analytical and numerical techniques for a generalized Hopfield network to probe the utility of distinct memory strategies against both static and dynamic (evolving) patterns. We find that while classical Hopfield networks with distributed memory can efficiently encode a memory of static patterns, they are inadequate against evolving patterns. To follow an evolving pattern, we show that a Hopfield network should use a higher learning rate, which can in turn distort the energy landscape associated with the stored memory attractors. Specifically, we observe the emergence of narrow connecting paths between memory attractors that lead to misclassification of evolving patterns. We demonstrate that compartmentalized networks with specialized subnetworks are the optimal solutions to memory storage for evolving patterns. We postulate that evolution of pathogens may be the reason for the immune system to be encoded in a focused memory, in contrast to the distributed memory used in the olfactory cortex that interacts with mixtures of static odors. Our approach offers a principled framework to study learning and memory retrieval in out-of-equilibrium dynamical systems.

中文翻译：

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进化模式的记忆学习和组织

存储用于分子识别的记忆是响应外部刺激的有效策略。生物过程使用不同的策略来存储记忆。在嗅觉皮层中，当受到气味刺激时，突触连接形成，并建立关联的分布式记忆，在再次暴露于相同的气味时可以恢复。相比之下，免疫系统通过能够识别多种进化病原体的多种受体编码专门的记忆。尽管嗅觉系统和免疫系统中的记忆存储在机制上存在差异，但这些过程仍然可以被视为不同的信息编码策略。在这里，我们为广义 Hopfield 网络开发分析和数值技术，以探索针对静态和动态（演化）模式的不同记忆策略的效用。我们发现，虽然具有分布式记忆的经典 Hopfield 网络可以有效地编码静态模式的记忆，但它们不足以应对不断发展的模式。为了遵循不断发展的模式，我们表明 Hopfield 网络应该使用更高的学习率，这反过来会扭曲与存储的记忆吸引子相关的能量景观。具体来说，我们观察到记忆吸引子之间出现了狭窄的连接路径，导致对进化模式的错误分类。我们证明了具有专门子网的分区网络是用于进化模式的内存存储的最佳解决方案。我们假设病原体的进化可能是免疫系统被编码在集中记忆中的原因，与嗅觉皮层中使用的分布式记忆相比，它与静态气味的混合物相互作用。我们的方法提供了一个原则框架来研究非平衡动态系统中的学习和记忆检索。