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The Synaptic Morphological Perceptron

机译：突触形态感知器

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In recent years, several researchers have constructed novel neural network models based on lattice algebra. Because of computational similarities to operations in the system of image morphology, these models are often called morphological neural networks. One neural model that has been successfully applied to many pattern recognition problems is the single-layer morphological perceptron with dendritic structure (SLMP). In this model, the fundamental computations are performed at dendrites connected to the body of a single neuron. Current training algorithms for the SLMP work by enclosing the target patterns in a set of hyperboxes orthogonal to the axes of the data space. This work introduces an alternate model of the SLMP, dubbed the synoptic morphological perceptron (SMP). In this model, each dendrite has one or more synapses that receive connections from inputs. The SMP can learn any region of space determined by an arbitrary configuration of hyperplanes, and is not restricted to forming hyperboxes during training. Thus, it represents a more general form of the morphological perceptron than previous architectures.
机译：近年来，一些研究人员已经基于晶格代数构建了新颖的神经网络模型。由于与图像形态系统中运算的计算相似性，因此这些模型通常称为形态神经网络。已成功应用于许多模式识别问题的一种神经模型是具有树突结构的单层形态感知器（SLMP）。在此模型中，基本计算是在连接到单个神经元身体的树突上执行的。当前用于SLMP的训练算法通过将目标模式包含在与数据空间的轴正交的一组超级框中来工作。这项工作介绍了SLMP的替代模型，称为概要形态学感知器（SMP）。在此模型中，每个树突都有一个或多个从输入接收连接的突触。 SMP可以学习由超平面的任意配置确定的空间的任何区域，并且不限于在训练期间形成超框。因此，它代表了形态感知器比以前的体系结构更通用的形式。

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