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Asynchronous Spiking Neural P Systems with Structural Plasticity

机译:具有结构可塑性的异步尖峰神经P系统

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Spiking neural P (in short, SNP) systems are computing devices inspired by biological spiking neurons. In this work we consider SNP systems with structural plasticity (in short, SNPSP systems) working in the asynchronous (in short, asyn mode). SNPSP systems represent a class of SNP systems that have dynamic synapses, i.e. neurons can use plasticity rules to create or remove synapses. We prove that for asyn mode, bounded SNPSP systems (where any neuron produces at most one spike each step) are not universal, while unbounded SNPSP systems with weighted synapses (a weight associated with each synapse allows a neuron to produce more than one spike each step) are universal. The latter systems are similar to SNP systems with extended rules in asyn mode (known to be universal) while the former are similar to SNP systems with standard rules only in asyn mode (conjectured not to be universal). Our results thus provide support to the conjecture of the still open problem.
机译:尖峰神经P(简称,SNP)系统是通过生物尖峰神经元启发的计算设备。在这项工作中,我们考虑具有在异步(简短,ASYN模式)中工作的结构可塑性(简短的SNPSP系统)的SNP系统。 SNPSP系统代表一类具有动态突触的SNP系统,即神经元可以使用可塑性规则来创建或删除突触。我们证明,对于ASYN模式,有界SNPSP系统(任何神经元在每个步骤中的大多数尖峰产生)都不是普遍的,而具有加权突触的无限SNPSP系统(与每个突触相关的重量允许神经元产生多个尖峰步骤)是普遍的。后一种系统类似于SNP系统,其中ASYN模式中的扩展规则(已知是通用),而前者类似于仅在ASYN模式(猜想不为通用)中具有标准规则的SNP系统。因此,我们的结果为仍然打开问题提供了支持。

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