Dynamic threshold spiking neural P systems with weights and multiple channels - Li, Yanyan; Song, Bosheng; Liu, Yuansheng; Zeng, Xiangxiang; Huang, Shengye (Theoretical computer science, 2024)
Membrane computing represents a sophisticated branch of computational science that assimilates the characteristics of cellular membranes and harnesses mathematical principles. It derives inspiration from the intricate structure and functional attributes exhibited by biological cell membranes. This exposition is dedicated to an in-depth exploration of spiking neural P systems (SN P systems), meticulously crafted to emulate the intricate signaling and interaction phenomena between cellular entities. Nevertheless, conventional spiking neural P systems confront inherent constraints pertaining to their excitation rules, which hinder their applicability to real-world challenges. In pursuit of overcoming these limitations, we introduce a novel paradigm encompassing the dynamic thresholds, synaptic weights, and the integration of multiple channels within synapses. This innovative framework culminates in the dynamic threshold spiking neural P systems with weights of synapses and multiple channels in synapses (DSNP-WM systems). It is noteworthy that DSNP-WM systems demonstrate Turing universality, effectively functioning as versatile entities capable of both number generation and acceptance, in addition to performing intricate computations. Importantly, these systems showcase the efficiency in tackling challenges posed by semi-uniform solutions, exemplified by the Subsets Sum problem—a fundamental member of the NP-complete problem class, which employs a nondeterministic approach.