标题： 接收器是一个具有内在多维选择能力的非线性阈值逻辑门，用于模拟输入 显示英文标题

标题： The receptron is a nonlinear threshold logic gate with intrinsic multi-dimensional selective capabilities for analog inputs

摘要： 阈值逻辑门（TLGs）被提出作为具有基于线性预测函数的分类能力的生物神经元的人工对应物，该函数结合了一组权重与特征向量。 TLGs的线性限制了其分类能力，需要使用网络来完成复杂任务。 一种称为接收器的TLG模型的推广，其特点是输入相关的权重函数，即使仅使用单个单元也能显著提高分类性能。 在这里，我们正式证明，当输入向量位于三维空间中的立方域内时，由非线性输入相关权重函数表征的接收器对模拟输入表现出固有的选择性激活特性。 所提出的模型可以扩展到n维情况，用于多维应用。 我们的结果表明，基于接收器的网络可以代表一类新的设备，能够处理大量模拟输入，适用于需要高选择性和分类能力但无需复杂训练负担的边缘应用。 显示英文摘要

摘要： Threshold logic gates (TLGs) have been proposed as artificial counterparts of biological neurons with classification capabilities based on a linear predictor function combining a set of weights with the feature vector. The linearity of TLGs limits their classification capabilities requiring the use of networks for the accomplishment of complex tasks. A generalization of the TLG model called receptron, characterized by input-dependent weight functions allows for a significant enhancement of classification performances even with the use of a single unit. Here we formally demonstrate that a receptron, characterized by nonlinear input-dependent weight functions, exhibit intrinsic selective activation properties for analog inputs, when the input vector is within cubic domains in a 3D space. The proposed model can be extended to the n-dimensional case for multidimensional applications. Our results suggest that receptron-based networks can represent a new class of devices capable to manage a large number of analog inputs, for edge applications requiring high selectivity and classification capabilities without the burden of complex training.