Abstract
A novel variant of NSN P systems, called numerical spiking neural P systems with a variable consumption strategy (NSNVC P systems), is proposed. Like the spiking rules consuming spikes in spiking neural P systems, NSNVC P systems introduce a variable consumption strategy by modifying the form of the production functions used in NSN P systems. Similar to the delay feature of the spiking rules, NSNVC P systems introduce a postponement feature into the production functions. The execution of the production functions in NSNVC P systems is controlled by two, i.e., polarization and threshold, conditions. Multiple synaptic channels are used to transmit the charges and the production values in NSNVC P systems. The proposed NSNVC P systems are a type of distributed parallel computing models with a directed graphical structure. The Turing universality of the proposed NSNVC P systems is proved as number generating/accepting devices. Detailed descriptions are provided for NSNVC P systems as number generating/accepting devices. In addition, a universal NSNVC P system with 66 neurons is constructed as a function computing device.
Highlights
Membrane computing is a class of distributed parallel-computing models introduced by Păun [1], which is inspired by the structure and function of living cells and their cooperation in tissues, organs, and biological neural networks
Spiking neural P (SN P) systems with anti-spikes were constructed by Pan and Păun [6] with anti-spikes abstracted from inhibitory impulses that participate in spiking and forgetting rules and annihilate spikes when they are in the same neuron
In order to overcome this difficulty, Wu et al [41] proposed numerical spiking neural P (NSN P) systems by introducing numerical variables and production functions used in numerical P (NP) systems into SN P systems
Summary
Membrane computing is a class of distributed parallel-computing models introduced by Păun [1], which is inspired by the structure and function of living cells and their cooperation in tissues, organs, and biological neural networks. In order to more effectively control the application of the programs, many variants of NP systems, such as enzymatic NP systems [27], NP systems with production thresholds [28], and NP systems with Boolean conditions [29] have been proposed These biologically inspired P systems have both advantages and disadvantages for solving real-world problems. In order to overcome this difficulty, Wu et al [41] proposed numerical spiking neural P (NSN P) systems by introducing numerical variables and production functions used in NP systems into SN P systems In this way, NSN P systems are equipped with numerical capabilities, making them more capable of solving real-world problems. The improvement in the computation performance of NSN P systems will be investigated when both polarization and threshold are used to control the execution of the production functions.
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