Sun, 01/30/2011 - 16:06 — manu

Title | Matrix representation of Spiking Neural P systems |

Publication Type | Journal Papers |

Year of Publication | 2011 |

Authors | Zeng, X. X., Adorna H., Martínez-del-Amor M. A., Pan L., & Pérez-Jiménez M. J. |

Journal Title | Lecture Notes in Computer Science |

ISBN Number | 978-84-9887-518-8 |

Publisher | Springer |

Place Published | Amsterdam, The Netherlands |

Volume | 6501 |

Pages | 377-392 |

Abstract | Spiking neural P systems (SN P systems, for short) are a class of distributed parallel computing devices inspired from the way neurons communicate by means of spikes. In this work, a discrete structure representation of SN P systems with extended rules and without delay is proposed. Specifically, matrices are used to represent SN P systems. In order to represent the computations of SN P systems by matrices, configuration vectors are defined to monitor the number of spikes in each neuron at any given configuration; transition net gain vectors are also introduced to quantify the total amount of spikes consumed and produced after the chosen rules are applied. Nondeterminism of the systems is assured by a set of spiking transition vectors that could be used at any given time during the computation. With such matrix representation, it is quite convenient to determine the next configuration from a given configuration, since it involves only multiplication and addition of matrices after deciding the spiking transition vector. |

URL | http://link.springer.com/chapter/10.1007/978-3-642-18123-8_29 |

ISSN Number | 0302-9743 |

DOI | 10.1007/978-3-642-18123-8_29 |