接收器神经元可精准识别三维模拟输入，无需复杂训练。
The receptron is a nonlinear threshold logic gate with intrinsic multi-dimensional selective capabilities for analog inputs
- 用非线性输入依赖权重函数实现多维选择性
- 在三维立方域内对模拟输入有内在激活特性
- 适合边缘设备的高选择性分类任务
阈值逻辑门（TLG）是基于线性预测函数的类生物神经元模型，但其线性限制了分类能力，需依赖网络完成复杂任务。本文提出的接收器（receptron）模型通过输入相关的权重函数，显著提升单个单元的分类性能。我们正式证明，当输入向量位于三维空间的立方域内时，具有非线性输入依赖权重函数的receptron展现出内在的选择性激活特性。该模型可扩展至n维，适用于多维应用场景。结果表明，基于receptron的网络可成为一类新型器件，能在无需复杂训练的前提下，高效处理大量模拟输入，适用于对高选择性和分类能力有要求的边缘计算应用。
原文摘要 · Abstract (English)
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.
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