论文标题
用尖峰神经元实现的大彩色建筑
A Macrocolumn Architecture Implemented with Spiking Neurons
论文作者
论文摘要
宏coloclumn是神经形态计算系统的关键组成部分，该系统与由代理控制的外部环境相互作用。当标记的有向图连接特征和标签指示它们之间的相对位移时，将学习并存储在宏观柱上。首先使用状态机器模型定义了宏collyumn功能。然后通过由尖峰神经元组成的神经网络实现该模型。神经元模型采用活跃的树突，并反映了霍金斯/numenta神经元模型。通过研究基准来证明该体系结构，在该基准测试基准中，代理商使用宏collumn首先学习，然后浏览包含伪随机放置功能的2-D环境。
The macrocolumn is a key component of a neuromorphic computing system that interacts with an external environment under control of an agent. Environments are learned and stored in the macrocolumn as labeled directed graphs where edges connect features and labels indicate the relative displacements between them. Macrocolumn functionality is first defined with a state machine model. This model is then implemented with a neural network composed of spiking neurons. The neuron model employs active dendrites and mirrors the Hawkins/Numenta neuron model. The architecture is demonstrated with a research benchmark in which an agent employs a macrocolumn to first learn and then navigate 2-d environments containing pseudo-randomly placed features.
