Article
Engineering, Chemical
Xiyu Liu, Qianqian Ren
Summary: The spiking neural membrane computing models (SNMC models) proposed in this paper combine neural network structure and data processing methods to improve the shortcomings of current spiking neural P systems (SNP) in numerical calculations. In SNMC models, the state of each neuron is represented by a real number, and there are new rules for neurons with time delay. The Turing universality of the SNMC model as a number generator and acceptor is also demonstrated.
Article
Computer Science, Artificial Intelligence
Tingfang Wu, Ferrante Neri, Linqiang Pan
Summary: Spiking neural P systems (SNP systems) are computational models that mimic the behavior of biological neurons. Recently, a new class of SNP systems called spiking neural P systems with communication on request (SNQP systems) has been developed, in which a neuron actively requests spikes from neighboring neurons. This study investigates the relationship between the number of unbounded neurons and the computation capability of SNQP systems. It is found that the number of unbounded neurons determines the ability of SNQP systems to characterize different sets of numbers and perform Boolean logic gate operations.
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
(2022)
Article
Engineering, Multidisciplinary
Saurabh Balkrishna Tandale, Marcus Stoffel
Summary: The present study aims to introduce an AI algorithm suitable for neuromorphic computing to solve Boundary Value Problems in Engineering Mechanics. By using Spiking Neural Networks (SNNs), the study proposes a surrogate model for mechanical tasks that is more energy-efficient than traditional neural networks. The researchers also propose a hybrid model that combines spiking recurrent cells, the spiking variant of the Legendre Memory Unit (LMU), and classical dense transformations to compute the nonlinear response of shock wave-loaded plate elements.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2023)
Article
Computer Science, Information Systems
Zhang Sun, Luis Valencia -Cabrera, Guimin Ning, Xiaoxiao Song
Summary: Spiking neural P systems are an abstraction of the structure and function of nervous systems and neurons. SNP-WOD systems, a new class of these systems, remove the mechanism of duplication and allow for the amplification of pulses during the firing of spiking rules. These systems have computational properties and can generate numbers.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
Jianping Dong, Gexiang Zhang, Biao Luo, Qiang Yang, Dequan Guo, Haina Rong, Ming Zhu, Kang Zhou
Summary: This paper proposes a distributed adaptive optimization spiking neural P system (DAOSNPS) that can solve combinatorial optimization problems without the help of evolutionary algorithms or swarm intelligence algorithms. Extensive experiments demonstrate its superiority over other methods.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
Jianping Dong, Gexiang Zhang, Biao Luo, Haina Rong
Summary: An extended numerical spiking neural (ENSN P) system is proposed to solve continuous constrained optimization problems. In ENSN P systems, production functions are selected by probability to achieve updated parameters. Experimental results show that the proposed method outperforms or is competitive with other 28 optimization algorithms in five benchmarks.
INFORMATION SCIENCES
(2023)
Article
Chemistry, Multidisciplinary
Sofie J. Studholme, Zachary E. Heywood, Joshua B. Mallinson, Jamie K. Steel, Philip J. Bones, Matthew D. Arnold, Simon A. Brown
Summary: This study investigates the computational capability of percolating networks of nanoparticles (PNNs) based on brain-like criticality. By manipulating the spiking activity, PNNs are able to perform Boolean operations and image classification with near perfect accuracy. The key to successful computation lies in the powerful modulus-like nonlinearity of nanoscale tunnel gaps within the PNNs.
Article
Neurosciences
Yang Li, Dongcheng Zhao, Yi Zeng
Summary: This paper introduces the characteristics and problems of spiking neural networks (SNN) in information processing, and proposes a novel bistable spiking neural network (BSNN) to improve the performance of converted SNNs. By designing synchronous neurons (SN), the performance of ANN conversion based on the ResNet structure can be effectively improved. Experimental results show that this method achieves good conversion results on different datasets.
FRONTIERS IN NEUROSCIENCE
(2022)
Article
Computer Science, Information Systems
Xiaoxiao Song, Luis Valencia-Cabrera, Hong Peng, Jun Wang
Summary: This paper introduces a new neural computing model - spiking neural P systems with autapses (SNP-AU systems) and demonstrates their ability to generate Turing-computable numbers. By building an SNP-AU system with 53 neurons and providing a universal machine, the universality of its computing function is shown.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Zeqiong Lv, Tingting Bao, Nan Zhou, Hong Peng, Xiangnian Huang, Agustin Riscos-Nunez, Mario J. Perez-Jimenez
Summary: The paper introduces a new variant of spiking neural P systems, called SNP-ECR systems, which are distributed parallel computing models with a stronger firing control mechanism. The new spiking rule ECR allows neurons to send different numbers of spikes, providing greater flexibility in computation. It is proven that SNP-ECR systems are Turing universal as number generating/accepting devices and can function as universal function computing devices.
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
(2021)
Article
Computer Science, Information Systems
Tingfang Wu, Luping Zhang, Qiang Lyu, Yu Jin
Summary: Asynchronous spiking neural P (AsynSN P) systems are distributed and parallel computational models inspired by biological neurons. This study introduces a control mechanism of local synchronization at the rule level and examines the computational power of different systems. The results show that local synchronization of rules can improve the computational capability of the systems.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Information Systems
Suxia Jiang, Yijun Liu, Bowen Xu, Junwei Sun, Yanfeng Wang
Summary: In this study, asynchronous numerical spiking neural (ANSN) P systems are investigated by combining set theory and threshold control strategy. It is proved that ANSN P systems are Turing universal and capable of processing information.
INFORMATION SCIENCES
(2022)
Article
Chemistry, Multidisciplinary
Xiong Chen, Ping Guo
Summary: This paper studies four basic arithmetic operations and improves the parallelization of addition and multiplication methods. It designs more effective SNPS for natural number addition, multiplication, subtraction, and division based on multiple subtractions. The proposed SNPS is verified to be effective through examples. Compared with similar SNPS, our system reduces the number of neurons used and the time overhead for addition operation by 50% and 33% respectively, and reduces the number of neurons used for multiplication operation by 40%.
APPLIED SCIENCES-BASEL
(2023)
Article
Computer Science, Theory & Methods
Peng Qu, Hui Lin, Meng Pang, Xiaofei Liu, Weimin Zheng, Youhui Zhang
Summary: This paper proposes an efficient SNN simulation framework, ENLARGE, that utilizes GPU clusters. The framework has a multi-level architecture, efficient communication methods, and optimization techniques to handle SNN characteristics and improve performance and scalability.
IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Mingzhe Liu, Feixiang Zhao, Xin Jiang, Hong Zhang, Helen Zhou
Summary: A parallel binary image encryption framework based on spiking neural networks is proposed in this paper. The framework utilizes SNP-MCP systems for permutation and SNP-ALC systems for diffusion, achieving efficient encryption and parallel computing through the use of neuron rules.
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Cacilda Vilela, Joao Ranhel
COGNITIVE SYSTEMS RESEARCH
(2017)
Proceedings Paper
Computer Science, Information Systems
Jose Rodrigues de Oliveira Neto, Joao Paulo Cerquinho Cajueiro, Joao Ranhel
25TH INTERNATIONAL CONFERENCE ON ELECTRONICS, COMMUNICATIONS AND COMPUTERS (CONIELECOMP 2015)
(2015)
Proceedings Paper
Computer Science, Artificial Intelligence
Joao Paulo Cerquinho Cajueiro, Joao Ranhel
2015 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN)
(2015)
Proceedings Paper
Computer Science, Artificial Intelligence
Jose Rodrigues de Oliveira-Neto, Felipe Duque Belfort, Rafael Cavalcanti-Neto, Joao Ranhel
PROCEEDINGS OF THE 2014 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN)
(2014)