Journal of Membrane Computing

Tone is the changing trend of pitch with time. In Chinese, tone plays an essential role for distinguishing meaning. Chinese dialect’s tone is more complex with Mandarin. In the field of Chinese dialect phonetics research, using human earing to recognize the types of tones is still the main method. So batch processing is not possible. In this paper, we construct a GSNP (gated spiking neural P) model with 2 layers which can process time series data to recognize the tones of Chinese dialects. The average accuracy rate of seven cities’ speech is more than 97%. Even in the case of small training samples, compared with other methods, the GSNP model has simpler structure, higher accuracy and more efficiency. It can not only improve the work efficiency of Chinese dialect field investigation, but also help researchers to screen the sounds with special sounds.

P systems were introduced more than two decades ago by Gheorghe Pǎun. They are known as nondeterministic maximally parallel computing models. Most of their variants are proved to be capable of solving NP problems in polynomial time. This work focuses on using neural-like P systems to simulate uniform sequential computing models. In particular, we consider a so-called Spiking Neural P module (SN P module) computing finite-state functions. We define and characterize a so-called (SN) P automatic sequence by SN P modules.

Networks of splicing processors are one of the theoretical computational models that take inspiration from nature to efficiently solve problems that our current computational knowledge is not able to. One of the issues restricting/hindering is practical implementation is the arbitrariness of the underlying graph, since our computational systems usually conform to a predefined topology. We propose simulations of networks of splicing processors having arbitrary underlying graphs by networks whose underlying graphs are of a predefined topology: complete, star, and grid graphs. We show that all of these simulations are time efficient in the meaning that they preserve the time complexity of the original network: each computational step in that network is simulated by a fixed number of computational steps in the new topologic networks. Moreover, these simulations do not modify the order of magnitude of the network size.

P systems are a model of compartmentalized multiset rewriting inspired by the structure of living cells and the way they function. In this paper, we focus of a variant in P systems in which membranes have limited capacity, i.e., the number of objects they may hold is limited by a fixed bound. This feature corresponds to an important physical property of cellular compartments. We propose several possible semantics of limited capacity and show that one of them allows real-time simulations of partially blind register machines, while the other one allows for obtaining computational completeness.

To take full advantage of the information transfer mechanism of biological nervous systems, we consider a new computational model of spiking neural P systems with polarizations (PSN P systems). Compared to spiking neural P systems (SN P systems), PSN P systems use more simple formal language rules, and the behavioral changes of each neuron are jointly controlled by the number of spikes and the polarity state ( $$+$$ , 0, − charge), making systems also have a powerful distributed parallel computing capability. Following the fact that SN P systems can operate as different modes, we consider the computation power of sequential PSN P systems in the accepting mode. In this work, we prove Turing universality of PSN P systems using the min-sequentiality and max-sequentiality strategies as number accepting devices by simulating the deterministic register machine.

In this paper, we are going to solve the inference problem of bounded L systems, namely such L systems which work on filaments having length up to a fixed size. We will show that these bounded L systems have considerable computational power as they can simulate linear-bounded automata. To carry out the inference, we are going to construct a specific polymorphic P system with target indication, which can reproduce the transitions of the examined bounded L system, and which is of size $$O(n|G|^4)$$ , where G is the alphabet of the bounded L system with n as the maximal size of the filaments.

P systems with active membranes have been widely used to attack problems in $${\mathbf{NP}}$$ or even in $${{\mathbf{PSPACE }}}$$; in general, an exponential amount of space is generated in polynomial time by dividing existing membranes. Natural questions arise in this framework, concerning the power of P systems when different bounds are considered for the use of the space resource. We consider in this paper two natural bounds: the amount of available physical space (in terms of the number of objects and membranes) and the organization of the membrane structure (in particular, concerning the depth of the membrane structure). We present the main results obtained so far on this subject.

Search-based testing is widely used for generating test sets. It is also applied in the case of model-based testing, especially for (extended) finite state machines. In this paper, we define such an approach for kernel P system models. We consider a specific kernel P system model and a define a search-based testing method. The test set generated consists of input sequences producing a given computation defined by the model. An example illustrates the use of the introduced method.

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.