论文标题
部分可观测时空混沌系统的无模型预测
Capacity Studies for a Differential Growing Neural Gas
论文作者
论文摘要
2019年，Kerdels和Peters提出了一个基于差异化神经气体（DGNG）网络体系结构的网格细胞模型（GCM），作为一种模拟自动相关记忆细胞（AMC）\ cite {kerdels_peters_peters_2019}的计算有效方法。 DGNG体系结构在计算神经科学领域的可能应用方面的一个重要特征是其\ textIt {apcation}，它是指其处理能力并唯一区分输入信号，因此获得了输入空间的有效表示。这项研究评估了在时尚流行数据集上两个分层DGNG网格细胞模型的能力。对研究的重点在于层尺寸的变化，以提高对网络参数及其缩放特性相关的容量属性的理解。此外，还提供了使用像素/段变化方法的参数讨论和合理性检查。可以得出结论，DGNG模型能够获得输入空间的有意义且合理的表示形式，并即使在中等层的大小处也能够应对时尚持续数据集的复杂性。
In 2019 Kerdels and Peters proposed a grid cell model (GCM) based on a Differential Growing Neural Gas (DGNG) network architecture as a computationally efficient way to model an Autoassociative Memory Cell (AMC) \cite{Kerdels_Peters_2019}. An important feature of the DGNG architecture with respect to possible applications in the field of computational neuroscience is its \textit{capacity} refering to its capability to process and uniquely distinguish input signals and therefore obtain a valid representation of the input space. This study evaluates the capacity of a two layered DGNG grid cell model on the Fashion-MNIST dataset. The focus on the study lies on the variation of layer sizes to improve the understanding of capacity properties in relation to network parameters as well as its scaling properties. Additionally, parameter discussions and a plausability check with a pixel/segment variation method are provided. It is concluded, that the DGNG model is able to obtain a meaningful and plausible representation of the input space and to cope with the complexity of the Fashion-MNIST dataset even at moderate layer sizes.
