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Tissue P systems with evolutional symport/antiport rules are a variant of tissue P systems, where objects are communicated through regions by symport/antiport rules, and objects may evolve during this process. It is known that such systems are able to solve NP problems in a polynomial time (and exponential space), when cell division is allowed. In this work, we continue to investigate the computational complexity aspects for tissue P systems with evolutional symport/antiport rules. We prove that problems beyond NP can also be solved. In particular, we show that deterministic systems of this type are able to solve all problems in the complexity class PP. Moreover, if non-deterministic systems are considered, then all problems in the class PSPACE can be solved.

Spiking neural P systems are a class of distributed parallel computing models, inspired by the way in which neurons process information and communicate with each other by means of spikes. In 2007, Freund and Verlan developed a formal framework for P systems to capture most of the essential features of P systems and to define their functioning in a formal way. In this work, we present an extension of the formal framework related to spiking neural P systems by considering the applicability of each rule to be controlled by specific conditions on the current contents of the cells. The main objective of this extension is to also capture spiking neural P systems in the formal framework. Another goal of our extension is to incorporate the notions of input and output. Finally, we also show that in the case of spiking neural P systems, the rules have a rather simple form and in that way spiking neural P systems correspond to vector addition systems where the application of rules is controlled by semi-linear sets.

Spiking neural P systems (SN P systems) refer to a type of distributed parallel neural computation model in the membrane computing framework. In this work, a new type of SN P system is examined, i.e., spiking neural P systems with polarizations (PSN P systems), by taking inspiration from the polarized cell membrane of a neuron. For the PSN P system, the firing condition of rules is the neuron-associated polarization. This work focuses on examining the computation power of the PSN P systems adopting the sequential mode induced by the minimum spike number, where at a computation step, one (resp., all) of neurons that hold the minimum spike number within the neurons which can fire will fire, i.e., the min-sequentiality strategy (resp., min-pseudo-sequentiality strategy). We prove that PSN P systems adopting the min-sequentiality strategy or min-pseudo-sequentiality strategy are Turing universal as the number generating devices. These results indicate the computation power of PSN P systems is robust regarding their strategies of sequentiality.

This paper proposes a novel connected components labeling (CCL) approach that introduces a gamma signal to record certain mask pixels’ values to eliminate duplicated pixel checking and regulate the labeling process for higher efficiency. A new block-based two-scan CCL algorithm, Eight-Connected Gamma-Signal-regulated (ECGS) algorithm, is designed and developed by applying this approach to evaluate a block of 2 × 2 pixels (with just 6 mask pixels) in each iteration such that the total number of operations is considerably reduced and the labeling efficiency is significantly improved. The experiments conducted on a public benchmark, YACCLAB (Yet Another Connected Components Labeling Benchmark), have demonstrated that the proposed ECGS algorithm can outperform current state-of-the-art CCL algorithms for a number of digital images.

Spiking neural P systems (SN P systems) are a class of distributed and parallel neural-like computing models, inspired from the way neurons communicate by means of spikes. In this paper, a new variant of the systems, called SN P systems with learning functions, is introduced. Such systems can dynamically strengthen and weaken connections among neurons during the computation. A class of specific SN P systems with simple Hebbian learning function is constructed to recognize English letters. The experimental results show that the SN P systems achieve average accuracy rate 98.76% in the test case without noise. In the test cases with low, medium, and high noises, the SN P systems outperform back propagation neural networks and probabilistic neural networks. Moreover, comparing with spiking neural networks, SN P systems perform a little better in recognizing letters with noise. The result of this paper is promising in terms of the fact that it is the first attempt to use SN P systems in pattern recognition after many theoretical advancements of SN P systems, and SN P systems exhibit the feasibility for tackling pattern recognition problems.

Traditional neuron models use analog values for information representation and computation, while all-or-nothing spikes are employed in the spiking ones. With a more brain-like processing paradigm, spiking neurons are more promising for improvements on efficiency and computational capability. They extend the computation of traditional neurons with an additional dimension of time carried by all-or-nothing spikes. Could one benefit from both the accuracy of analog values and the time-processing capability of spikes? In this paper, we introduce a concept of augmented spikes to carry complementary information with spike coefficients in addition to spike latencies. New augmented spiking neuron model and synaptic learning rules are proposed to process and learn patterns of augmented spikes. We provide systematic insight into the properties and characteristics of our methods, including classification of augmented spike patterns, learning capacity, construction of causality, feature detection, robustness and applicability to practical tasks such as acoustic and visual pattern recognition. The remarkable results highlight the effectiveness and potential merits of our methods. Importantly, our augmented approaches are versatile and can be easily generalized to other spike-based systems, contributing to a potential development for them including neuromorphic computing., Comment: 13 pages

Tissue P systems with symport/antiport rules are a class of distributed parallel computing models inspired by the cell intercommunication in tissues, where objects are never modified in the process of communication, just changing their place within the system. In this work, a variant of tissue P systems, called tissue P systems with evolutional symport/antiport rules is introduced, where objects are moved from one region to another region and may be evolved during this process. The computational power of such P systems is studied. Specifically, it is proved that such P systems with one cell and using evolutional symport rules of length at most 3 or using evolutional antiport rules of length at most 4 are Turing universal (only the family of all finite sets of positive integers can be generated by such P systems if standard symport/antiport rules are used). Moreover, cell division rules are considered in tissue P systems with evolutional symport/antiport rules, and a limit on the efficiency of such P systems is provided with evolutional communication rules of length at most 2. The computational efficiency of this kind of models is shown when using evolutional communication rules of length at most 4.

Spiking neural P systems, shortly called SN P systems, are a class of distributed and parallel neural-like computing models, inspired from the way of neurons spiking and communicating with each other by means of spikes. In this work, we propose a new variant of SN P systems, called SN P systems with request rules. In such a system, besides spiking and forgetting rules, a neuron can have request rules, with which the neuron can sense "stimulus" from the environment by receiving a certain number of spikes. We investigate the computation power of SN P systems with request rules. It is obtained that such systems are Turing universal, even with a small number of neurons. Specifically, (i) SN P systems with request rules having 4 neurons can compute any set of Turing computable natural numbers and (ii) with 47 neurons such systems can compute any Turing computable function.

With mathematical motivation, we consider a combination of basic features of multiset-rewriting P systems and of spiking neural P systems, that is, we consider cell-like P systems with spiking rules in their membranes (hence dealing with only one kind of objects, the spikes). The universality of these systems as number generating devices is proved for the two usual ways to define the output (internally or externally) and for various restrictions on the spiking rules. Several research topics are also pointed out.

Spiking neural P systems (SN P systems, for short) are a class of distributed parallel computing devices inspired by spiking neurons. SN P systems with anti-spikes (ASN P systems) are a variant of SN P systems, which were inspired by inhibitory impulses/spikes or inhibitory synapses. In this work, we consider ASN P systems with the restrictions: (1) systems are simple in the sense that each neuron has only one rule; (2) at each step the neuron(s) with the maximum number of spikes among the neurons that are active (can spike) will fire; (3) the feature of delays and forgetting rules are not used. These restrictions make the systems working in a sequential way. We investigate the computational power of ASN P systems under these restrictions. Specifically, we prove that such systems with spiking rules of categories ( a , a ) and ( a , a ? ) are universal as both number generating and accepting devices. We also prove that ASN P systems with inhibitory synapses using spiking rules of category ( a , a ) are universal as both generating and accepting devices. These results show that ASN P systems are still powerful working with these restrictions.

Spiking neural P systems (SN P systems, for short) are a class of distributed parallel computing devices inspired from the way neurons process information and communicate by means of spikes, where neurons are different in the sense that they can have different sets of spiking rules. In this work, we consider a variant of SN P systems with two restrictions: (1) all neurons contain only spikes (neurons are homogeneous in this case, since they can be considered as containers of spikes), while the spiking rules are moved on the synapses; (2) all synapses are homogeneous in the sense that each synapse has the same set of rules. These restrictions correspond to the fact that the SN P system consists of only one kind of neurons and one kind of synapses. The computational power of SN P systems with homogeneous neurons and synapses is investigated. Specifically, it is proved that such systems are Turing universal as both number generating and number accepting devices.

Axon P systems are computing models with a linear structure in the sense that all nodes (i.e., computing units) are arranged one by one along the axon. Such models have a good biological motivation: an axon in a nervous system is a complex information processor of impulse signals. Because the structure of axon P systems is linear, the computational power of such systems has been proved to be greatly restricted; in particular, axon P systems are not universal as language generators. It remains open whether axon P systems are universal as number generators. In this paper, we prove that axon P systems are universal as both number generators and function computing devices, and investigate the number of nodes needed to construct a universal axon P system. It is proved that four nodes (respectively, nine nodes) are enough for axon P systems to achieve universality as number generators (respectively, function computing devices). These results illustrate that the simple linear structure is enough for axon P systems to achieve a desired computational power.

Cell-like spiking neural (cSN) P systems are a class of distributed and parallel computation models inspired by both the way in which neurons process information and communicate to each other by means of spikes and the compartmentalized structures of living cells. cSN P systems have been proved to be Turing universal if more spikes can be produced by consuming some spikes or spikes can be replicated. In this paper, in order to answer the open problem whether this functioning of producing more spikes and replicating spikes can be avoided by using some strategy without the loss of computation power, we introduce cSN P systems with request rules, which have classical spiking rules and forgetting rules, and also request rules in the skin membrane. The skin membrane can receive spikes from the environment by the application of request rules. cSN P systems with request rules are proved to be Turing universal. The results show that the decrease of computation power caused by removing the internal functioning of producing more spikes and replicating spikes can be compensated by request rules, which suggests that the communication between a cell and the environment is an essential ingredient of systems in terms of computation power.

Spiking neural P (SN P) systems are a class of parallel computation models inspired by neurons, where the firing condition of a neuron is described by a regular expression associated with spiking rules. However, it is NP-complete to decide whether the number of spikes is in the length set of the language associated with the regular expression. In this paper, in order to avoid using regular expressions, two major and rather natural modifications in their form and functioning are proposed: the spiking rules no longer check the number of spikes in a neuron, but, in exchange, a polarization is associated with neurons and rules, one of the three electrical charges −, 0,+. Surprisingly enough, the computing devices obtained are still computationally complete, which are able to compute all Turing computable sets of natural numbers. On this basis, the number of neurons in a universal SN P system with polarizations is estimated. Several research directions are mentioned at the end of this paper.

National Natural Science Foundation of China [61202011, 61272152, 61033003, 91130034, 61320106005]; Ph.D. Programs Foundation of Ministry of Education of China [20120121120039, 20120142130008]; Natural Science Foundation of Hubei Province [2011CDA027]

Tissue P systems are a class of distributed and parallel models of computation inspired by the way of communication among living cells or between cells and their environment. In this work, we investigate the computational power of tissue P systems, where each rule is assigned either with a label chosen from an alphabet or with the empty label λ . The sequence of labels of rules applied during a halting computation is defined as the result of the computation, and the set of all results computed by a given tissue P system is called a control language. We prove that tissue P systems with antiport rules of weight one and without symport rules characterize regular languages; tissue P systems with antiport rules of weight at most two (resp., symport rules of weight at most two) without symport rules (resp., antiport rules) are universal. Tissue P systems with antiport rules of weight one and symport rules of weight one are also proved to be universal. These results show that the rule complexity is crucial for tissue P systems to achieve a desired computational power.

Spiking neural P systems (SN P systems, for short) with rules on synapses are a new variant of SN P systems, where the spiking and forgetting rules are placed on synapses instead of in neurons. Recent studies illustrated that this variant of SN P systems is universal working in the way that the synapses starting from the same neuron work in parallel (i.e., all synapses starting from the same neuron should apply their rules if they have rules to be applied). In this work, we consider SN P systems with rules on synapses working in another way: the synapses starting from the same neuron are restricted to work in a sequential way (i.e., at each step at most one synapse starting from the same neuron applies its rule). It is proved that the computational power of SN P systems with rules on synapses working in this way is reduced; specifically, they can only generate finite sets of numbers. Such SN P systems with rules on synapses are proved to be universal, if synapses are allowed to have weight at most 2 (if a rule which can generate n spikes is applied on a synapse with weight k, then the neuron linking to this synapse will receive totally nk spikes). Two small universal SN P systems with rules on synapses for computing functions are also constructed: a universal system with 26 neurons when using extended rules and each synapse having weight at most 2, and a universal system with 26 neurons when using standard rules and each synapse having weight at most 12. These results illustrate that the weight is an important feature for the computational power of SN P systems.

Spiking neural P systems (SN P systems, for short) are a class of distributed parallel computing devices inspired from the way neurons communicate by means of spikes. Asynchronous SN P systems are non-synchronized systems, where the use of spiking rules (even if they are enabled by the contents of neurons) is not obligatory. It remains open whether asynchronous SN P systems with standard spiking rules are equivalent with Turing machines. In this paper, with a biological inspiration (in order to achieve some specific biological functioning, neurons from the same functioning motif or community work synchronously to cooperate with each other), we introduce the notion of local synchronization into asynchronous SN P systems. The computation power of asynchronous SN P systems with local synchronization is investigated. Such systems consisting of general neurons (respectively, unbounded neurons) and using standard spiking rules are proved to be universal. Asynchronous SN P systems with local synchronization consisting of bounded neurons and using standard spiking rules characterize the semilinear sets of natural numbers. These results show that the local synchronization is useful, it provides some ''programming capacity'' useful for achieving a desired computation power.

Background Deciphering the genetic basis of human diseases is an important goal in biomedical research. There is increasing evidence suggesting that microRNAs play critical roles in many key biological processes. So the identification of microRNAs associated with disease is very important for understanding the pathogenesis of diseases. Methods Two multipath methods are introduced to predict the associations between microRNAs and diseases based on microRNA-disease heterogeneous network. The first method, HeteSim_MultiPath (HSMP), uses the HeteSim measure to calculate the similarity between objects and combines the HeteSim scores of different paths with a constant that dampens the contributions of longer paths. The second one, HeteSim_SVM (HSSVM), uses the HeteSim measure and the machine learning method used to combine HeteSim scores instead of a constant. Results We use the leave-one-out cross-validation to evaluate our novel methods, and find that our methods are better than other methods. We achieve an area under the ROC curve of 0.981 and 0.984 respectively. We also check the top-10 most similarity of microRNAs-diseases associations and find that our predictions are reasonable and credible. Conclusions The encouraging results suggest that multipath methods can provide help in identifying novel microRNA-disease associations, and guide biological experiments for scientific research. This article is part of a Special Issue entitled “System Genetics”. Guest Editor: Dr. Yudong Cai and Dr. Tao Huang.

Spiking neural P systems are a class of distributed parallel computing models inspired from the way neurons communicate with each other by means of electrical impulses, where there is a synapse between each pair of connected neurons. However, in a biological system, there can be several synapses for each pair of connected neurons. In this study, inspired by this biological observation, synapses in a spiking neural P system are endowed with integer weight denoting the number of synapses for each pair of connected neurons. With the price of weight on synapses, quite small universal spiking neural P systems can be constructed. Specifically, a universal spiking neural P system with standard rules and weight having 38 neurons is produced as device of computing functions; as generator of sets of numbers, we find a universal system with standard rules and weight having 36 neurons.

Spiking neural P systems are a class of distributed and parallel computing models inspired by spiking neurons. In this work, the features of neuron division and neuron budding are introduced into the framework of spiking neural P systems, which are processes inspired by neural stem cell division. With neuron division and neuron budding, a spiking neural P system can generate exponential work space in polynomial time as the case for P systems with active membranes. In this way, spiking neural P systems can efficiently solve computationally hard problems by means of a space-time tradeoff, which is illustrated with an efficient solution to SAT problem.

Different biological processes take different times to be completed, which can also be influenced by many environmental factors. In this work, a realistic definition of nonsynchronized spiking neural P systems (SN P systems, for short) is considered: during the work of an SN P system, the execution times of spiking rules cannot be known exactly (i.e., they are arbitrary). In order to establish robust systems against the environmental factors, a special class of SN P systems, called time-free SN P systems, is introduced, which always produce the same computation result independent of the execution times of the rules. The universality of time-free SN P systems is investigated. It is proved that these P systems with extended rules (several spikes can be produced by a rule) are equivalent to register machines. However, if the number of spikes present in the system is bounded, then the power of time-free SN P systems falls, and in this case, a characterization of semilinear sets of natural numbers is obtained.

Looking for small universal computing devices is a natural and well investigated topic in computer science. Recently, this topic was also investigated in the framework of spiking neural P systems. One of the small universality results is that a small weakly universal extended spiking neural P system with 12 neurons was constructed. In this paper, a new way is introduced for simulating register machines by spiking neural P systems, where only one neuron is used for all instructions of the register machine; in this way, we can use less neurons to construct universal spiking neural P system. Specifically, we give a smaller weakly universal spiking neural P system that uses extended rules and has only 9 neurons.

Syntactic models constitute one of the main areas of mathematical studies on picture array generation. A number of 2D grammar models have been proposed motivated by a variety of applications. A P system is a biologically motivated new computing model, proposed by Paun in the area of membrane computing. It is a rich framework for dealing with different problems, including the problem of handling picture array generation. In this paper, the generative power of the array-rewriting P system with pure 2D context-free rules is investigated by comparing it with other 2D grammar models, thus bringing out the suitability of this P system model for picture array generation.

To attack block cipher systems, it is inevitable to handle the bit-substitutions, such as S-box, cycle left shift, and expansion substitutions. In this paper, a DNA sticker algorithm for bit-substitution such as cycle left shift is proposed. The proposed algorithm requires two equal long memory strands. The first memory strand stores the original binary string, while the second memory strand stores the resulting binary string. With the proposed algorithm, cycle left shift is performed on a mass of equal long binary strings, and the computation complexity of the cycle left shift is O(n) for all n-bit binary strings. Furthermore, this work indicates that block cipher systems with a 64-bit key are perhaps insecure. This paper presents clear evidence of the ability of DNA computing to perform intractable computation problems.

Simulated annealing technique has mostly been used to solve various optimization and learning problems, and it is well known that the maximum clique problem is one of the most studied NP-hard optimization problems owing to its numerous applications. In this note, a simple simulated annealing algorithm for the maximum clique problem is proposed and tested on all 80 DIMACS maximum clique instances. Although it is simple, the proposed simulated annealing algorithm is efficient on most of the DIMACS maximum clique instances. The simulation results show that the proposed simulated annealing algorithm outperforms a recent efficient simulated annealing algorithm proposed by Xu and Ma, and the solutions obtained by the proposed simulated annealing algorithm have the equal quality with those obtained by a recent trust region heuristic algorithm of Stanislav Busygin.

P systems (or membrane systems) are a class of distributed parallel computing devices of a biochemical type. In this paper, some restrictions on the general form of the developing rules are considered, under which it is still possible to solve NP-complete problems. We present an algorithm for deterministically deciding SAT in linear time by P systems with active membranes using two polarizations and rules of restricted versions of types (a), (c), (e). The result obtained in this paper answered an open problem proposed by Alhazov and Freund in the aspect of computing efficiency.

P systems are parallel molecular computing models based on pro- cessing multisets of objects in cell-like membrane structures. In this paper we give a membrane algorithm to multidimensional 0-1 knapsack problem in lin- ear time by recognizer P systems with input and with active membranes using 2-division. This algorithm can also be modi¯ed to solve general 0-1 integer programming problem.

In DNA computing, similar thermodynamic stability of the encoding DNA sequences is conduced to improve the reliability and precision of the computing process. The melting temperature is a suitable parameter used to evaluating the stability of DNA duplex. Traditional method to predict Tm in biological engineering may exist lager error for a few sequences. Thus it misfits the lager amount of DNA sequences in DNA computing. In this paper, we introduced artificial neural network to predict the Tm based on Next-Nearest-Neighbor model. Our result shows that the methods have a higher precision than TP methods based on nearest-neighbor model.