This paper proposes a new variant of spiking neural P systems (in short, SNP systems), nonlinear spiking neural P systems (in short, NSNP systems). In NSNP systems, the state of each neuron is denoted by a real number, and a real configuration vector is used to characterize the state of the whole system. A new type of spiking rules, nonlinear spiking rules, is introduced to handle the neuron’s firing, where the consumed and generated amounts of spikes are often expressed by the nonlinear functions of the state of the neuron. NSNP systems are a class of distributed parallel and nondeterministic computing systems. The computational power of NSNP systems is discussed. Specifically, it is proved that NSNP systems as number-generating/accepting devices are Turing-universal. Moreover, we establish two small universal NSNP systems for function computing and number generator, containing 117 neurons and 164 neurons, respectively.

中文翻译：

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非线性脉冲神经 P 系统

本文提出了一种脉冲神经P系统（简称SNP系统）的新变体，非线性脉冲神经P系统（简称NSNP系统）。在 NSNP 系统中，每个神经元的状态用一个实数表示，一个实配置向量用来表征整个系统的状态。引入了一种新型的脉冲规则，非线性脉冲规则来处理神经元的放电，其中消耗和产生的脉冲量通常由神经元状态的非线性函数表示。NSNP 系统是一类分布式并行和非确定性计算系统。讨论了 NSNP 系统的计算能力。具体来说，证明了作为数字生成/接受设备的 NSNP 系统是图灵通用的。而且，