Abstract
Spiking neural P systems are a new candidate in spiking neural network models. By using neuron division and budding, such systems can generate/produce exponential working space in linear computational steps, thus provide a way to solve computational hard problems in feasible (linear or polynomial) time with a “time-space trade-off” strategy. In this work, a new mechanism called neuron dissolution is introduced, by which redundant neurons produced during the computation can be removed. As applications, uniform solutions to two NP-hard problems: SAT problem and Subset Sum problem are constructed in linear time, working in a deterministic way. The neuron dissolution strategy is used to eliminate invalid solutions, and all answers to these two problems are encoded as indices of output neurons. Our results improve the one obtained in Science China Information Sciences, 2011, 1596-1607 by Pan et al.
Highlights
Spiking neural P systems are a class of bio-inspired parallel computing models, initiated by Ionescu, Păun and Yokomori in 2006 [1], which are inspired from information processing strategy and communication strategy between neurons
Uniform solutions to SAT and Subset Sum problems in linear time using the proposed SN P systems with neuron division and dissolution are presented in section 2 and section 3
The new mechanism called neuron dissolution is introduced into the framework of SN P systems in this work
Summary
Spiking neural P systems (in short, SN P systems) are a class of bio-inspired parallel computing models, initiated by Ionescu, Păun and Yokomori in 2006 [1], which are inspired from information processing strategy and communication strategy between neurons. For this purpose, neuron dissolution, which is a basic biological phenomenon aiming to remove unnecessary neurons, is introduced into SN P systems [27, 28], and a new class of SN P systems, SN P systems with neuron division and dissolution (DDSN P systems, for short) is proposed in this work. Invalid solutions are eliminated during the computational process by neuron dissolution, and all solutions are encoded as indices of specific output neurons at halting, which can provide more valuable information for applications. Uniform solutions to SAT and Subset Sum problems in linear time using the proposed SN P systems with neuron division and dissolution are presented in section 2 and section 3.
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