Contemporary implementations of spiking bio-inspired neural networks
Andrey E. Schegolev, Marina V. Bastrakova, Michael A. Sergeev, Anastasia A. Maksimovskaya, Nikolay V. Klenov, Igor I. Soloviev
脉冲神经网络领域的广泛发展已经对人们生活的许多方面产生了直接影响。作为所有神经网络中最具生物相似性的一种，脉冲神经网络不仅能够解决识别和聚类问题（包括动态问题），还促进了人类神经系统知识的增长。我们的分析表明，硬件实现至关重要，因为网络细胞中物理过程的特性会影响它们模拟活体神经组织神经活动的能力，以及信息处理、存储和传输的某些阶段的效率。本综述回顾了在"半导体"、"超导体"和"光学"领域中现有的生物启发脉冲神经网络的硬件神经形态实现。特别关注不同方法有效"混合"的可能性。
The extensive development of the field of spiking neural networks has led to many areas of research that have a direct impact on people's lives. As the most bio-similar of all neural networks, spiking neural networks not only allow the solution of recognition and clustering problems (including dynamics), but also contribute to the growing knowledge of the human nervous system. Our analysis has shown that the hardware implementation is of great importance, since the specifics of the physical proc...