Two types of cells for bio-inspired computations are neurons and bacteria: the former have “simple” neurons that connect together to become more useful, i.e. structure is important to their overall function; the latter have bacteria “processing” DNA such as plasmids. We combine inspirations from both cell types as neural-like P systems with plasmids or NP P systems: bacteria are nodes in a digraph, with edges as communication links among bacteria. Bacteria use “transmit” and “kill” operations to send or remove plasmids. Another bio-inspiration is homogeneous bacteria, i.e. each bacterium has the same set of rules. Combining a neural-like structure with conjugation using plasmids proves useful: NP P systems are computationally complete even with homogeneous bacteria; bounding the number of plasmids reduces their computing power; we give an estimate of 164 bacteria for homogeneous NP P systems to remain complete.

用于仿生计算的两种类型的细胞是神经元和细菌：前者具有“简单”的神经元，它们连接在一起变得更有用，即结构对其整体功能很重要；后者有细菌“加工”DNA，例如质粒。我们将两种细胞类型的灵感结合为神经样 P 系统与质粒或 NP P 系统：细菌是有向图中的节点，边作为细菌之间的通信链接。细菌使用“传输”和“杀死”操作来发送或移除质粒。另一种生物灵感是同质细菌，即每个细菌都有相同的规则集。将神经样结构与使用质粒结合证明是有用的：即使是同质细菌，NP P 系统在计算上也是完整的；限制质粒的数量会降低它们的计算能力；