%0 Generic
%D 2020
%T Dendrite P systems
%A Hong Peng
%A Tingting Bao
%A Xiaohui Luo
%A Jun Wang
%A Xiaoxiao Song
%A Agustín Riscos-Núñez
%A Mario J. Pérez-Jiménez
%K Computational power
%K Dendrite P systems
%K Neural-like P systems
%K P systems
%P 110 - 120
%R https://doi.org/10.1016/j.neunet.2020.04.014
%U http://www.sciencedirect.com/science/article/pii/S0893608020301349
%V 127
%X 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.