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引用次数: 0
摘要
尖峰神经P系统(SNP系统)是具有生物可解释性的并行和分布式计算模型之一,是近年来生物启发计算模型的热门研究课题。为了提高模型的稳定性,本研究将生物神经系统中的小胶质细胞引入 SNP 系统,提出了带小胶质细胞的 SNP 系统(MSNP 系统)。在 MSNP 系统中,除了神经元,还引入了另一种名为小胶质细胞的细胞类型。小胶质细胞可以帮助作用范围内的神经元保持平衡,防止兴奋毒性,即过度兴奋。具体来说,当作用范围内神经元的尖峰数量过高时,小胶质细胞会使用一种新的小胶质细胞维持规则来降低尖峰数量。MSNP 系统的计算能力和效率也得到了证明。这项研究使 SNP 系统更加稳定,并在一定程度上避免了数据溢出或数据爆炸问题。
Spiking neural P systems (SNP systems), one of the parallel and distributed computing models with biological interpretability, have been a hot research topic in bio-inspired computational models in recent years. To improve the stability of the models, this study introduces microglia in the biological nervous system into SNP systems and proposes SNP systems with microglia (MSNP systems). In MSNP systems, besides neurons, another cell type named microglia is introduced. Microglia can help neurons in the range of action maintain homeostasis and prevent excitotoxicity, i.e., excessive excitability. Specifically, microglia use a new microglial maintenance rule to lower the number of spikes in neurons within their range of action when it is too high. The computational capability and efficiency of MSNP systems are also proved. This study makes SNP systems more stable and avoids data overflow or data explosion problems to some degree.
期刊介绍:
IEEE Transactions on Parallel and Distributed Systems (TPDS) is published monthly. It publishes a range of papers, comments on previously published papers, and survey articles that deal with the parallel and distributed systems research areas of current importance to our readers. Particular areas of interest include, but are not limited to:
a) Parallel and distributed algorithms, focusing on topics such as: models of computation; numerical, combinatorial, and data-intensive parallel algorithms, scalability of algorithms and data structures for parallel and distributed systems, communication and synchronization protocols, network algorithms, scheduling, and load balancing.
b) Applications of parallel and distributed computing, including computational and data-enabled science and engineering, big data applications, parallel crowd sourcing, large-scale social network analysis, management of big data, cloud and grid computing, scientific and biomedical applications, mobile computing, and cyber-physical systems.
c) Parallel and distributed architectures, including architectures for instruction-level and thread-level parallelism; design, analysis, implementation, fault resilience and performance measurements of multiple-processor systems; multicore processors, heterogeneous many-core systems; petascale and exascale systems designs; novel big data architectures; special purpose architectures, including graphics processors, signal processors, network processors, media accelerators, and other special purpose processors and accelerators; impact of technology on architecture; network and interconnect architectures; parallel I/O and storage systems; architecture of the memory hierarchy; power-efficient and green computing architectures; dependable architectures; and performance modeling and evaluation.
d) Parallel and distributed software, including parallel and multicore programming languages and compilers, runtime systems, operating systems, Internet computing and web services, resource management including green computing, middleware for grids, clouds, and data centers, libraries, performance modeling and evaluation, parallel programming paradigms, and programming environments and tools.