<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>13</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Hong Peng</style></author><author><style face="normal" font="default" size="100%">Tingting Bao</style></author><author><style face="normal" font="default" size="100%">Xiaohui Luo</style></author><author><style face="normal" font="default" size="100%">Jun Wang</style></author><author><style face="normal" font="default" size="100%">Xiaoxiao Song</style></author><author><style face="normal" font="default" size="100%">Agustín Riscos-Núñez</style></author><author><style face="normal" font="default" size="100%">Mario J. Pérez-Jiménez</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Dendrite P systems</style></title><secondary-title><style face="normal" font="default" size="100%">Neural Networks</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Computational power</style></keyword><keyword><style  face="normal" font="default" size="100%">Dendrite P systems</style></keyword><keyword><style  face="normal" font="default" size="100%">Neural-like P systems</style></keyword><keyword><style  face="normal" font="default" size="100%">P systems</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2020</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.sciencedirect.com/science/article/pii/S0893608020301349</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">127</style></volume><pages><style face="normal" font="default" size="100%">110 - 120</style></pages><abstract><style face="normal" font="default" size="100%">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–storing process instead of the storing–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.</style></abstract></record></records></xml>