Homogeneous spiking neural P systems with structural plasticity

被引:0
|
作者
Ren Tristan A. de la Cruz
Francis George C. Cabarle
Ivan Cedric H. Macababayao
Henry N. Adorna
Xiangxiang Zeng
机构
[1] University of the Philippines - Diliman,Department of Computer Science, Algorithms and Complexity Laboratory
[2] Hunan University,School of Information Science and Engineering
来源
关键词
Membrane computing; Spiking neural P systems; Homogeneous neurons; Structural plasticity;
D O I
暂无
中图分类号
学科分类号
摘要
Spiking neural P system (SNP system) is a model of computation inspired by the mechanism of spiking neurons. An SNP system is a directed graph of neurons that can communicate with each other using an object known as a spike (the object spike represents action potential or nerve impulse). Spiking neural P systems with structural plasticity (SNPSP system) is a variant of the SNP system model. It incorporates the concept of structural plasticity to the SNP system model. SNPSP systems have the ability to add and delete connections between neurons. In SNPSP systems, the behavior of a neuron can be “programmed” by giving it a set of rules. Different set of rules will result in different behaviors. In this work, we show that it is possible to construct a universal SNPSP system where all the neurons in the system use the same set of rules. Such systems are called homogeneous SNPSP systems.
引用
收藏
页码:10 / 21
页数:11
相关论文
共 50 条
  • [1] Homogeneous spiking neural P systems with structural plasticity
    de la Cruz, Ren Tristan A.
    Cabarle, Francis George C.
    Macababayao, Ivan Cedric H.
    Adorna, Henry N.
    Zeng, Xiangxiang
    JOURNAL OF MEMBRANE COMPUTING, 2021, 3 (01) : 10 - 21
  • [2] Spiking neural P systems with structural plasticity
    Francis George C. Cabarle
    Henry N. Adorna
    Mario J. Pérez-Jiménez
    Tao Song
    Neural Computing and Applications, 2015, 26 : 1905 - 1917
  • [3] Spiking neural P systems with structural plasticity
    Cabarle, Francis George C.
    Adorna, Henry N.
    Perez-Jimenez, Mario J.
    Song, Tao
    NEURAL COMPUTING & APPLICATIONS, 2015, 26 (08): : 1905 - 1917
  • [4] Spiking neural P systems with structural plasticity and mute rules
    Wu, Ruina
    Zhao, Yuzhen
    THEORETICAL COMPUTER SCIENCE, 2024, 1000
  • [5] Homogeneous Spiking Neural P Systems
    Zeng, Xiangxiang
    Zhang, Xingyi
    Pan, Linqiang
    FUNDAMENTA INFORMATICAE, 2009, 97 (1-2) : 275 - 294
  • [6] Weighted Spiking Neural P Systems with Structural Plasticity Working in Maximum Spiking Strategy
    Sun, Mingming
    Qu, Jinhua
    2016 8TH INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY IN MEDICINE AND EDUCATION (ITME), 2016, : 355 - 359
  • [7] Spiking neural P systems with structural plasticity and anti-spikes
    Yang, Qian
    Li, Bo
    Huang, Yue
    Peng, Hong
    Wang, Jun
    THEORETICAL COMPUTER SCIENCE, 2020, 801 (801) : 143 - 156
  • [8] On String Languages Generated by Spiking Neural P Systems With Structural Plasticity
    Cabarle, Francis George C.
    de la Cruz, Ren Tristan A.
    Zhang, Xingyi
    Jiang, Min
    Liu, Xiangrong
    Zeng, Xiangxiang
    IEEE TRANSACTIONS ON NANOBIOSCIENCE, 2018, 17 (04) : 560 - 566
  • [9] Matrix representation and simulation algorithm of spiking neural P systems with structural plasticity
    Jimenez, Zechariah B.
    Cabarle, Francis George C.
    de la Cruz, Ren Tristan A.
    Buno, Kelvin C.
    Adorna, Henry N.
    Hernandez, Nestine Hope S.
    Zeng, Xiangxiang
    JOURNAL OF MEMBRANE COMPUTING, 2019, 1 (03) : 145 - 160
  • [10] Spiking Neural P Systems with Structural Plasticity: Attacking the Subset Sum Problem
    Cabarle, Francis George C.
    Hernandez, Nestine Hope S.
    Angel Martinez-del-Amor, Miguel
    MEMBRANE COMPUTING (CMC 2015), 2015, 9504 : 106 - 116