Abstract
As third-generation neural network models, spiking neural P systems (SNP) have distributed parallel computing capabilities with good performance. In recent years, artificial neural networks have received widespread attention due to their powerful information processing capabilities, which is an effective combination of a class of biological neural networks and mathematical models. However, SNP systems have some shortcomings in numerical calculations. In order to improve the incompletion of current SNP systems in dealing with certain real data technology in this paper, we use neural network structure and data processing methods for reference. Combining them with membrane computing, spiking neural membrane computing models (SNMC models) are proposed. In SNMC models, the state of each neuron is a real number, and the neuron contains the input unit and the threshold unit. Additionally, there is a new style of rules for neurons with time delay. The way of consuming spikes is controlled by a nonlinear production function, and the produced spike is determined based on a comparison between the value calculated by the production function and the critical value. In addition, the Turing universality of the SNMC model as a number generator and acceptor is proved.
Highlights
Membrane computing, an important branch of natural computing, is a computing model inspired by the structure, function, and behavior of biological cells
In the past few years, research on neural P systems has mostly focused on spiking neural P systems, which is a type of computing model inspired by the processing of information in the form of spikes by neurons in biological neural networks
It is a type that combines the understanding of biological neural networks with mathematical models to achieve powerful information processing capabilities, and it has a wide range of applications in pattern recognition, information processing, and image processing
Summary
An important branch of natural computing, is a computing model inspired by the structure, function, and behavior of biological cells. It is notable that the combination of neural network and neural P systems is only a theoretical improvement based on a certain characteristic of neural networks or an improvement in rule structure based on the operation mechanism of a specific network model This has certain research value and development prospects for the development of membrane computing, but these still need further research. Artificial neural networks are currently widely used in classification, image processing, and pattern recognition, but there are few studies on membrane computing dealing with these problems. If they can be combined, the theory and application research of membrane computing can be further expanded.
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