论文标题
一种用于增长尖峰神经网络的多代理模型
A multi-agent model for growing spiking neural networks
论文作者
论文摘要
人工智能已将生物系统视为灵感的来源。尽管尚未发现大脑的许多方面，但神经科学发现了证据表明，神经元之间的联系不断增长并重塑作为学习过程的一部分。这与人工神经网络的设计不同，这些神经网络通过不断发展它们与它们之间的突触中的权重来实现学习，随着时间的流逝，它们并没有改变。 该项目探讨了扩大神经网络中神经元之间连接的规则，作为一种学习机制。这些规则已在多代理系统上实施，用于创建简单的逻辑功能，该功能为建立更复杂的系统和体系结构建立了基础。在模拟环境中的结果表明，对于给定的一组参数，可以达到重现经过测试函数的拓扑。 该项目还为使用诸如遗传算法（用于获得模型参数的最佳值的遗传算法）的技术打开了大门，因此创建了可以适应不同功能的神经网络。
Artificial Intelligence has looked into biological systems as a source of inspiration. Although there are many aspects of the brain yet to be discovered, neuroscience has found evidence that the connections between neurons continuously grow and reshape as a part of the learning process. This differs from the design of Artificial Neural Networks, that achieve learning by evolving the weights in the synapses between them and their topology stays unaltered through time. This project has explored rules for growing the connections between the neurons in Spiking Neural Networks as a learning mechanism. These rules have been implemented on a multi-agent system for creating simple logic functions, that establish a base for building up more complex systems and architectures. Results in a simulation environment showed that for a given set of parameters it is possible to reach topologies that reproduce the tested functions. This project also opens the door to the usage of techniques like genetic algorithms for obtaining the best suited values for the model parameters, and hence creating neural networks that can adapt to different functions.
