We propose a novel neural model with lateral interaction for learning tasks. The model consists of two functional fields: an elementary field to extract features and a high-level field to store and recognize patterns. Each field is composed of some neurons with lateral interaction, and the neurons in different fields are connected by the rules of synaptic plasticity. The model is established on the current research of cognition and neuroscience, making it more transparent and biologically explainable. Our proposed model is applied to data classification and clustering. The corresponding algorithms share similar processes without requiring any parameter tuning and optimization processes. Numerical experiments validate that the proposed model is feasible in different learning tasks and superior to some state-of-the-art methods, especially in small sample learning, one-shot learning, and clustering.

中文翻译：

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一种用于学习任务的具有横向交互的新型神经模型

我们为学习任务提出了一种具有横向交互的新型神经模型。该模型由两个功能字段组成：一个用于提取特征的基本字段和一个用于存储和识别模式的高级字段。每个场由一些具有横向相互作用的神经元组成，不同场的神经元通过突触可塑性规则连接起来。该模型建立在当前认知和神经科学的研究之上，使其更加透明和生物学上的可解释性。我们提出的模型应用于数据分类和聚类。相应的算法共享相似的过程，无需任何参数调整和优化过程。数值实验验证了所提出的模型在不同的学习任务中是可行的，并且优于一些最先进的方法，