Recently, the group of Intelligent Information Processing and Computing, College of Computer and Communication Engineering , has new findings on the research of spiking neural P systems. The paper On the Computational Power of Spiking Neural P Systems with Self-Organization was published on Scientific Reports. The research was based on the co-work of researchers of UPC and Swinburne University of Technology. Wang Xun, the doctoral candidate of UPC is the first author.

Neural-like computing models are versatile computing mechanisms in the field of artificial intelligence. Spiking neural P systems (SN P systems for short) are one of the recently developed spiking neural network models inspired by the way neurons communicate. The communications among neurons are essentially achieved by spikes, i. e. short electrical pulses. In terms of motivation, SN P systems fall into the third generation of neural network models. In the paper, a novel variant of SN P systems, namely SN P systems with self-organization, is introduced, and the computational power of the system is investigated and evaluated. It is proved that SN P systems with self-organization are capable of computing and accept the family of sets of Turing computable natural numbers. Moreover, with 87 neurons the system can compute any Turing computable recursive function, thus achieves Turing universality. These results demonstrate promising initiatives to solve an open problem arisen by Gh Păun.

The research has received high recognition from the peers.  It has been listed among 2016 Best Theoretical Achievement by the International Membrane Computing Society.

Editor: Bu Lingduo

Source: UPC News Center