论文标题
生物神经网络的低阶模型
Low-Order Model of Biological Neural Networks
论文作者
论文摘要
生物神经网络的生物学上合理的低阶模型（LOM）是树突状淋巴结/树木，尖峰/非上介神经元的复发等级网络，无治疗/监督/有监督的协调/累积学习机制，反馈连接以及最高通用方案。这些组件模型是通过使LOM轻松学习和检索而无需差异化，优化或迭代，并群集，检测和识别多个/分层损坏，扭曲，扭曲和遮挡的时间和空间模式来实现这些组件模型的动机。
A biologically plausible low-order model (LOM) of biological neural networks is a recurrent hierarchical network of dendritic nodes/trees, spiking/nonspiking neurons, unsupervised/ supervised covariance/accumulative learning mechanisms, feedback connections, and a scheme for maximal generalization. These component models are motivated and necessitated by making LOM learn and retrieve easily without differentiation, optimization, or iteration, and cluster, detect and recognize multiple/hierarchical corrupted, distorted, and occluded temporal and spatial patterns.
