@article {doi:10.1142/S0129065720500501,
	title = {Medical Image Fusion Method Based on Coupled Neural P Systems in Nonsubsampled Shearlet Transform Domain},
	journal = {International Journal of Neural Systems},
	volume = {31},
	number = {ja},
	year = {2021},
	pages = {2050050},
	doi = {10.1142/S0129065720500501},
	author = {Bo Li and Hong Peng and Xiaohui Luo and Jun Wang and Xiaoxiao Song and Mario J. P{\'e}rez-Jim{\'e}nez and Agust{\'\i}n Riscos-N{\'u}{\~n}ez}
}
@article {PENG2020110,
	title = {Dendrite P systems},
	journal = {Neural Networks},
	volume = {127},
	year = {2020},
	pages = {110 - 120},
	abstract = {It was recently found that dendrites are not just a passive channel. They can perform mixed computation of analog and digital signals, and therefore can be abstracted as information processors. Moreover, dendrites possess a feedback mechanism. Motivated by these computational and feedback characteristics, this article proposes a new variant of neural-like P systems, dendrite P (DeP) systems, where neurons simulate the computational function of dendrites and perform a firing{\textendash}storing process instead of the storing{\textendash}firing process in spiking neural P (SNP) systems. Moreover, the behavior of the neurons is characterized by dendrite rules that are abstracted by two characteristics of dendrites. Different from the usual firing rules in SNP systems, the firing of a dendrite rule is controlled by the states of the corresponding source neurons. Therefore, DeP systems can provide a collaborative control capability for neurons. We discuss the computational power of DeP systems. In particular, it is proven that DeP systems are Turing-universal number generating/accepting devices. Moreover, we construct a small universal DeP system consisting of 115 neurons for computing functions.},
	keywords = {Computational power, Dendrite P systems, Neural-like P systems, P systems},
	issn = {0893-6080},
	doi = {https://doi.org/10.1016/j.neunet.2020.04.014},
	url = {http://www.sciencedirect.com/science/article/pii/S0893608020301349},
	author = {Hong Peng and Tingting Bao and Xiaohui Luo and Jun Wang and Xiaoxiao Song and Agust{\'\i}n Riscos-N{\'u}{\~n}ez and Mario J. P{\'e}rez-Jim{\'e}nez}
}
@article {2485,
	title = {Nonlinear Spiking Neural P Systems},
	journal = {International Journal of Neural Systems},
	volume = {30},
	year = {2020},
	pages = {2050008},
	doi = {10.1142/S0129065720500082},
	author = {Hong Peng and Zeqiong Lv and Bo Li and Xiaohui Luo and Jun Wang and Xiaoxiao Song and Tao Wang and Mario J. P{\'e}rez-Jim{\'e}nez and Agust{\'\i}n Riscos-N{\'u}{\~n}ez}
}
@article {PENG2020105064,
	title = {Spiking neural P systems with inhibitory rules},
	journal = {Knowledge-Based Systems},
	volume = {188},
	year = {2020},
	pages = {105064},
	abstract = {Motivated by the mechanism of inhibitory synapses, a new kind of spiking neural P (SNP) system rules, called inhibitory rules, is introduced in this paper. Based on this, a new variant of SNP systems is proposed, called spiking neural P systems with inhibitory rules (SNP-IR systems). Different from the usual firing rules in SNP systems, the firing condition of an inhibitory rule not only depends on the state of the neuron associated with the rule but also is related to the states of other neurons. Moreover, from the perspective of topological structure, the new variant is shown as a directed graph with inhibitory arcs, and therefore seems to have more powerful control. The computational completeness of SNP-IR systems is discussed. In particular, it is proved that SNP-IR systems are Turing universal number accepting/generating devices. Moreover, we obtain a small universal function-computing device for SNP-IR systems consisting of 100 neurons.},
	keywords = {Inhibitory synapse, Membrane computing, spiking neural P systems, Spiking neural P systems with inhibitory rules},
	issn = {0950-7051},
	doi = {https://doi.org/10.1016/j.knosys.2019.105064},
	url = {http://www.sciencedirect.com/science/article/pii/S0950705119304514},
	author = {Hong Peng and Bo Li and Jun Wang and Xiaoxiao Song and Tao Wang and Luis Valencia-Cabrera and Ignacio P{\'e}rez-Hurtado and Agust{\'\i}n Riscos-N{\'u}{\~n}ez and Mario J. P{\'e}rez-Jim{\'e}nez}
}
@article {WANG2020103680,
	title = {A weighted corrective fuzzy reasoning spiking neural P system for fault diagnosis in power systems with variable topologies},
	journal = {Engineering Applications of Artificial Intelligence},
	volume = {92},
	year = {2020},
	pages = {103680},
	abstract = {This paper focuses on power system fault diagnosis based on Weighted Corrective Fuzzy Reasoning Spiking Neural P Systems with real numbers (rWCFRSNPSs) to propose a graphic fault diagnosis method, called FD-WCFRSNPS. In the FD-WCFRSNPS, an rWCFRSNPS is proposed to model the logical relationships between faults and potential warning messages triggered by the corresponding protective devices. In addition, a matrix-based reasoning algorithm for the rWCFRSNPS is devised to reason about the fault alarm messages using parallel representations. Besides, a layered modeling method based on rWCFRSNPSs is developed to adapt to topological changes in power systems and a Temporal Order Information Processing Method based on Cause{\textendash}Effect Networks is designed to correct fault alarm messages before the fault reasoning. Finally, in a case study considering a local subsystem of a 220kV power system, the diagnosis results of five test cases prove that the proposed FD-WCFRSNPS is viable and effective.},
	keywords = {Cause{\textendash}effect network, Fault diagnosis, Fuzzy reasoning, Membrane computing, power system, Spiking neural P system},
	issn = {0952-1976},
	doi = {https://doi.org/10.1016/j.engappai.2020.103680},
	url = {http://www.sciencedirect.com/science/article/pii/S0952197620301172},
	author = {Tao Wang and Xiaoguang Wei and Jun Wang and Tao Huang and Hong Peng and Xiaoxiao Song and Luis Valencia Cabrera and Mario J. P{\'e}rez-Jim{\'e}nez}
}
@article {PENG2019105064,
	title = {Spiking neural P systems with inhibitory rules},
	journal = {Knowledge-Based Systems},
	year = {2019},
	pages = {105064},
	abstract = {Motivated by the mechanism of inhibitory synapses, a new kind of spiking neural P (SNP) system rules, called inhibitory rules, is introduced in this paper. Based on this, a new variant of SNP systems is proposed, called spiking neural P systems with inhibitory rules (SNP-IR systems). Different from the usual firing rules in SNP systems, the firing condition of an inhibitory rule not only depends on the state of the neuron associated with the rule but also is related to the states of other neurons. Moreover, from the perspective of topological structure, the new variant is shown as a directed graph with inhibitory arcs, and therefore seems to have more powerful control. The computational completeness of SNP-IR systems is discussed. In particular, it is proved that SNP-IR systems are Turing universal number accepting/generating devices. Moreover, we obtain a small universal function-computing device for SNP-IR systems consisting of 100 neurons.},
	keywords = {Inhibitory synapse, Membrane computing, spiking neural P systems, Spiking neural P systems with inhibitory rules},
	issn = {0950-7051},
	doi = {https://doi.org/10.1016/j.knosys.2019.105064},
	url = {http://www.sciencedirect.com/science/article/pii/S0950705119304514},
	author = {Hong Peng and Bo Li and Jun Wang and Xiaoxiao Song and Tao Wang and Luis Valencia-Cabrera and Ignacio P{\'e}rez-Hurtado and Agust{\'\i}n Riscos-N{\'u}{\~n}ez and Mario J. P{\'e}rez-Jim{\'e}nez}
}
