On the Computational Power of Spiking Neural P Systems with Self-Organization.
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 this study, 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.
College of Computer and Communication Engineering, China University of Petroleum, Qingdao 266580, Shandong, China.
Xun
X
Wang
College of Computer and Communication Engineering, China University of Petroleum, Qingdao 266580, Shandong, China.|Faculty of Engineering, Computing and Science, Swinburne University of Technology Sarawak Campus, Kuching, 93350, Malaysia.
Tao
T
Song
College of Computer and Communication Engineering, China University of Petroleum, Qingdao 266580, Shandong, China.
Faming
F
Gong
Faculty of Engineering, Computing and Science, Swinburne University of Technology Sarawak Campus, Kuching, 93350, Malaysia.
Pan
P
Zheng
D016571:Neural Networks, Computer