Instituto Politécnico Nacional ESIME Culhuacan, Av. Santana 1000, Coyoacan, 04260, Ciudad de México, Mexico.
Instituto Politécnico Nacional ESIME Culhuacan, Av. Santana 1000, Coyoacan, 04260, Ciudad de México, Mexico.
Neural Netw. 2021 Jun;138:126-139. doi: 10.1016/j.neunet.2021.02.010. Epub 2021 Feb 16.
In spiking neural P (SN P) systems, neurons are interconnected by means of synapses, and they use spikes to communicate with each other. However, in biology, the complex structure of dendritic tree is also an important part in the communication scheme between neurons since these structures are linked to advanced neural process such as learning and memory formation. In this work, we present a new variant of the SN P systems inspired by diverse dendrite and axon phenomena such as dendritic feedback, dendritic trunk, dendritic delays and axonal delays, respectively. This new variant is referred to as a spiking neural P system with dendritic and axonal computation (DACSN P system). Specifically, we include experimentally proven biological features in the current SN P systems to reduce the computational complexity of the soma by providing it with stable firing patterns through dendritic delays, dendritic feedback and axonal delays. As a consequence, the proposed DACSN P systems use the minimum number of synapses and neurons with simple and homogeneous standard spiking rules. Here, we study the computational capabilities of a DACSN P system. In particular, we prove that DACSN P systems with dendritic and axonal behavior are universal as both number-accepting/generating devices. In addition, we constructed a small universal SN P system using 39 neurons with standard spiking rules to compute any Turing computable function.
在尖峰神经网络 (SNP) 系统中,神经元通过突触相互连接,它们使用尖峰来相互通信。然而,在生物学中,树突的复杂结构也是神经元之间通信方案的一个重要部分,因为这些结构与学习和记忆形成等高级神经过程相关联。在这项工作中,我们提出了一种受多种树突和轴突现象启发的 SNP 系统的新变体,分别是树突反馈、树突干、树突延迟和轴突延迟。这个新变体被称为具有树突和轴突计算功能的尖峰神经网络 (DACSN P 系统)。具体来说,我们在当前的 SNP 系统中包含了经过实验验证的生物学特性,通过树突延迟、树突反馈和轴突延迟为躯体提供稳定的点火模式,从而降低了躯体的计算复杂性。因此,所提出的 DACSN P 系统使用了最少数量的突触和神经元,具有简单和均匀的标准尖峰规则。在这里,我们研究了 DACSN P 系统的计算能力。特别是,我们证明了具有树突和轴突行为的 DACSN P 系统是通用的,既是数字接受/生成设备。此外,我们使用具有标准尖峰规则的 39 个神经元构建了一个小型通用 SNP 系统,用于计算任何可计算的图灵函数。