# On Applications of Spiking Neural P Systems
## 4 Conclusions and Future Research Lines  
In this paper, we provided an updated and comprehensive survey on applications of spiking neural P systems. We also provided a comparative study of these models and studied their advantages and disadvantages. Moreover, we studied some implementation of these models in hardware and software. Finally, we discussed some possible extensions of these models which can be investigated further. Next, we discuss some more ideas which can expand the scope of applications of these models.  
1. In this work we discussed different variants of FRSNPs models and their applications in fault diagnosis. It is important to note that these models work efficiently for small scale networks. Along with the implementation of the FRSNPs models in fault diagnoses in large scale power networks, it is imperative to investigate whether these models are effective, accurate and reliable while solving the fault diagnosis problem in micro and smart grids. Additionally, the resources--i.e., time and space required by these model--also need to be investigated. We also have discussed the use of learning mechanisms in solving the problems in fault diagnosis. Until now, only few models have been introduced with the features of machine learning. This area can be further extended by constructing new SNPS models and incorporating learning mechanisms in these models and then using these models for the identification of faults in power systems. Moreover, one could then study their speed and accuracy while performing this task. Additionally, a natural extension of these works will be the construction of fault diagnosis models combining SNPS and metaheuristic algorithms. Another important aspect which is not properly investigated in the literature is the descriptional complexity measures of the models used for fault diagnosis. The optimized use of the neurons is a topic of future research directions.
2. We have already discussed the use of unsupervised machine learning algorithms in the framework of SNPS to solve pattern recognition problems. Construction of new machine learning algorithms is a very new and interesting research area. However, until now no known progress has been made towards the construction of supervised learning algorithms in the framework of SNPS for solving pattern recognition problems. This can be an important direction to expand the scope of machine learning algorithms in SNPS and also applications of membrane computing models.
3. We have discussed the OSNPS model and its variants in solving knapsack problems, TSP and their use in fault diagnosis. The use of this model in solving other computationally hard combinatorial optimization problems, as well as their use in solving problems in real-life applications, can be a direction of future research.  
Conceptualization of this research was done by [PERSON] and [PERSON]; Investigations were done by [PERSON], [PERSON], [PERSON] and [PERSON]; Writing and preparation of the draft version were done by [PERSON] and [PERSON]; Writing and editing and providing suggestions for improving the contents were done by [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON]. Final submission version was prepared with the involvement of all the authors. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
This work was supported by the project of State Grid Corporation of China (No. 521997180016), by the National Natural Science Foundation of China (61972324, 61672437, 61702428), by New Generation Artificial Intelligence Science and Technology Major Project of Sichuan Province (2018 GZDZX0043), Beijing Advanced Innovation Center for Intelligent Robots and Systems (2019 IRS14) and Artificial Intelligence Key Laboratory of Sichuan Province (2019 RY06).  
The authors declare no conflict of interest.