1 Key Laboratory of Image Information Processing, and Intelligent Control of Education Ministry of China, School of Automation, Huazhong University of Science and Technology, Wuhan 430074, Hubei, P. R. China.
2 and Zhengzhou University of Light Industry, Zhengzhou 450002, Henan, P. R. China.
Int J Neural Syst. 2017 Dec;27(8):1750042. doi: 10.1142/S0129065717500423. Epub 2017 Aug 16.
Spiking Neural [Formula: see text] Systems are Neural System models characterized by the fact that each neuron mimics a biological cell and the communication between neurons is based on spikes. In the Spiking Neural [Formula: see text] systems investigated so far, the application of evolution rules depends on the contents of a neuron (checked by means of a regular expression). In these [Formula: see text] systems, a specified number of spikes are consumed and a specified number of spikes are produced, and then sent to each of the neurons linked by a synapse to the evolving neuron. [Formula: see text]In the present work, a novel communication strategy among neurons of Spiking Neural [Formula: see text] Systems is proposed. In the resulting models, called Spiking Neural [Formula: see text] Systems with Communication on Request, the spikes are requested from neighboring neurons, depending on the contents of the neuron (still checked by means of a regular expression). Unlike the traditional Spiking Neural [Formula: see text] systems, no spikes are consumed or created: the spikes are only moved along synapses and replicated (when two or more neurons request the contents of the same neuron). [Formula: see text]The Spiking Neural [Formula: see text] Systems with Communication on Request are proved to be computationally universal, that is, equivalent with Turing machines as long as two types of spikes are used. Following this work, further research questions are listed to be open problems.
尖峰神经网络系统是一种神经网络模型，其特点是每个神经元都模仿生物细胞，神经元之间的通信基于尖峰。在迄今为止研究的尖峰神经网络系统中，进化规则的应用取决于神经元的内容（通过正则表达式检查）。在这些系统中，消耗指定数量的尖峰，并产生指定数量的尖峰，然后将其发送到通过突触与进化神经元相连的每个神经元。在本工作中，提出了一种尖峰神经网络系统中神经元之间的新的通信策略。在由此产生的模型中，称为基于请求的尖峰神经网络系统，根据神经元的内容（仍然通过正则表达式检查）从相邻神经元请求尖峰。与传统的尖峰神经网络系统不同，不会消耗或创建尖峰：尖峰只是沿着突触移动并复制（当两个或多个神经元请求同一神经元的内容时）。基于请求的尖峰神经网络系统被证明在计算上是通用的，即只要使用两种类型的尖峰，就与图灵机等效。在这项工作之后，列出了进一步的研究问题作为开放问题。
