Several variants of spiking neural P systems (SNPS) have been presented in the literature to perform arithmetic operations However, each of these variants was designed only for one specific arithm

A Complete Arithmetic Calculator Constructed from Spiking Neural P Systems and its Application to Information Fusion

Optimization Spiking Neural P System (OSNPS) is the first membrane computing model to directly derive an approximate solution of combinatorial problems with a specific reference to the 0/1 knapsack

An Adaptive Optimization Spiking Neural P System for Binary Problems.

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A membrane parallel rapidly-exploring random tree algorithm for robotic motion planning

A distributed adaptive optimization spiking neural P system for approximately solving combinatorial optimization problems

Spiking neural P systems are a class of distributed parallel computing models, inspired by the way in which neurons process information and communicate with each other by means of spikes. In 2007, Freund and Verlan developed a formal framework for P systems to capture most of the essential features of P systems and to define their functioning in a formal way. In this work, we present an extension of the formal framework related to spiking neural P systems by considering the applicability of each rule to be controlled by specific conditions on the current contents of the cells. The main objective of this extension is to also capture spiking neural P systems in the formal framework. Another goal of our extension is to incorporate the notions of input and output. Finally, we also show that in the case of spiking neural P systems, the rules have a rather simple form and in that way spiking neural P systems correspond to vector addition systems where the application of rules is controlled by semi-linear sets.

A formal framework for spiking neural P systems

Taken inspiration from biological phenomenon that neurons communicate via spikes, spiking neural P systems (SN P systems, for short) are a class of distributed and parallel computing devices. So far firing rules in most of the SN P systems usually work in a sequential way or in an exhaustive way. Recently, a combination of the two ways mentioned above is considered in SN P systems. This new strategy of using rules, which is called a generalized way of using rules, is applicable for both firing rules and forgetting rules. In SN P systems with generalized use of rules (SNGR P systems, for short), if a rule is used at some step, it can be applied any possible number of times, nondeterministically chosen. In this work, the computational completeness of SNGR P systems is investigated. Specifically, a universal SNGR P system is constructed, where each neuron contains at most 5 rules, and for each time each firing rule consumes at most 6 spikes and each forgetting rule removes at most 4 spikes. This result makes an improvement regarding to these related parameters, thus provides an answer to the open problem mentioned in original work. Moreover, with this improvement we can use less resources (neurons and spikes involved in the evolution of system) to construct universal SNGR P systems.

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

Multi-hop Knowledge Graph Question Answering (KGQA) requires reasoning about multi-hop inference relations between topic entities and answers on the knowledge graph(KG) and returning correct answers. The difficulty of obtaining the implied inference relations in multi-hop questions described in natural language and the sparse knowledge graph bring challenges to the multi-hop KGQA. In this paper, we propose an embedded knowledge graph multi-hop Q&A model based on relational paths, which exploits the relational chains in the knowledge graph and the semantic similarity of multiple questions to improve the accuracy of multi-hop KGQA task. The comparative experiments prove that our method significantly outperforms the state-of-the-art counterparts. Comprehensive ablation experiments also validate the effectiveness of our approach on multi-hop KGQA tasks.

Yun Jiang

Papers

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

Improve embedded knowledge graph multi-hop question answering based on relational path