论文标题
基于尖峰的构件，用于使用Spinnaker上的尖峰神经网络执行逻辑操作
Spike-based building blocks for performing logic operations using Spiking Neural Networks on SpiNNaker
论文作者
论文摘要
神经形态工程是最有趣且仍在增长的科学领域之一，该工程的重点是研究和设计硬件和软件，以模仿生物神经系统的基本原理。当前，有许多研究小组基于神经科学知识开发实用应用。这项工作为研究人员提供了基于尖峰神经网络的构建块的新工具包，这些神经网络模仿了不同逻辑门的行为。由于逻辑门是数字电路的基础，因此在许多基于尖峰的应用中可能非常有用。提出的设计和模型是在大三角帆硬件平台上介绍和实现的。进行不同的实验以验证预期的行为，并讨论了获得的结果。研究了传统逻辑门的功能和所提出的块，并讨论了提出的方法的可行性。
One of the most interesting and still growing scientific fields is neuromorphic engineering, which is focused on studying and designing hardware and software with the purpose of mimicking the basic principles of biological nervous systems. Currently, there are many research groups developing practical applications based on neuroscientific knowledge. This work provides researchers with a novel toolkit of building blocks based on Spiking Neural Networks that emulate the behavior of different logic gates. These could be very useful in many spike-based applications, since logic gates are the basis of digital circuits. The designs and models proposed are presented and implemented on a SpiNNaker hardware platform. Different experiments were performed in order to validate the expected behavior, and the obtained results are discussed. The functionality of traditional logic gates and the proposed blocks is studied, and the feasibility of the presented approach is discussed.
