On the Computational Power of Spiking Neural P Systems with Self-Organization

Abstract

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.

A decision support model for investment on P2P lending platform

“…In the future, an interesting work is to investigate P2P lending by considering multi-objective evolutionary algorithms (MOEAs) [ 23 ], since the effectiveness of MOEAs have been verified on a variety of real-world problems [ 24 – 26 ]. And machine learning classification model [ 27 – 29 ] such as the state-of-art v-support vector machine methods [ 30 – 32 ], dimension reduction techniques [ 33 – 35 ], neural-like computing models [ 36 – 38 ] and spiking neural networks [ 39 , 40 ] could be tested. Community detection methods, especially overlapping communities detection [ 41 ] also can be considered for the further improvement.…”

Hyperparameter optimization in learning systems

“…There is a recent tendency to create bridges between adaptive P systems and machine learning learning paradigms, motivated by the huge success of deep learning in current technologies. Some recent P systems are adaptive [13,55], and [3]. During the Brainstorming Week on Membrane Computing Sevilla, 2018, ideas about evolving spiking neural (SN) P systems (introduced in [27]) were discussed 1 .…”

“…In the above-cited references, the plasticity of P systems refers to the parameters only. For example, in [55], a spiking neural P system with self-organization has no initially designed synapses. The synapses can be created or deleted according to the information contained in involved neurons during the computation.…”

An improved universal spiking neural P system with generalized use of rules

“…The theoretical and practical usefulness of these variants were also investigated: regarding computing power the relationship between these variants and well-known models of computation, e.g. finite automata, register machines, grammars, computing numbers or strings was investigated in [23][24][25][26][27][28][29][30][31][32][33][34][35]; computing efficiency of these variants in solving hard problems was investigated in [36,37].…”