In this paper, we design a double layers self-organized spiking neural P system with anti-spikes for fingerprint recognition. The system can self-adaptively create and delete synapse between the neurons in different layers and recognize fingerprints by the spike trains emitted out of the output neurons. Data experiments are conducted on FVC2002 and FVC2004 Databases with EER (Equal Error Rate) 9.5% around, and much less parameters are involved in our SN P systems than Capsule Neural Networks. To our best knowledge, it is the first attempt of using SN P systems to do fingerprint recognition, which can also provide theoretical models for spiking neural circuits recognizing fingerprints.

Bacterial computing is a known candidate in natural computing, the aim being to construct “bacterial computers” for solving complex problems. In this paper, a new kind of bacterial computing system, named the bacteria and plasmid computing system (BP system), is proposed. We investigate the computational power of BP systems with finite numbers of bacteria and plasmids. Specifically, it is obtained in a constructive way that a BP system with 2 bacteria and 34 plasmids is Turing universal. The results provide a theoretical cornerstone to construct powerful bacterial computers and demonstrate a concept of paradigms using a “reasonable” number of bacteria and plasmids for such devices.