Matrix representation and simulation algorithm of spiking neural P systems with structural plasticity

Jimenez, Zechariah B.; Cabarle, Francis George C.; Cruz, Ren Tristan A. de la; Buño, Kelvin C.; Adorna, Henry N.; Hernandez, Nestine Hope S.; Zeng, Xiangxiang

doi:10.1007/s41965-019-00020-3

Cited by 29 publications

(7 citation statements)

References 21 publications

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“…By using a set of algebraic operations, it is possible to reproduce the transitions of a computation. Although the baseline representation only involves SNP systems without delays and static structure, many extensions have followed such as for enabling delays [21,22], handling non-determinism [24], plasticity rules [36], rules on synapses [37], and dendrite P systems [38]. In this section we briefly introduce the definitions for the matrix representation of the basic model of spiking neural P systems without delays, as defined above.…”

Section: Matrix Representation For Snp Systemsmentioning

confidence: 99%

Simulation of Spiking Neural P Systems with Sparse Matrix-Vector Operations

et al. 2021

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To date, parallel simulation algorithms for spiking neural P (SNP) systems are based on a matrix representation. This way, the simulation is implemented with linear algebra operations, which can be easily parallelized on high performance computing platforms such as GPUs. Although it has been convenient for the first generation of GPU-based simulators, such as CuSNP, there are some bottlenecks to sort out. For example, the proposed matrix representations of SNP systems lead to very sparse matrices, where the majority of values are zero. It is known that sparse matrices can compromise the performance of algorithms since they involve a waste of memory and time. This problem has been extensively studied in the literature of parallel computing. In this paper, we analyze some of these ideas and apply them to represent some variants of SNP systems. We also provide a new simulation algorithm based on a novel compressed representation for sparse matrices. We also conclude which SNP system variant better suits our new compressed matrix representation.

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Section: Matrix Representation For Snp Systemsmentioning

confidence: 99%

Simulation of Spiking Neural P Systems with Sparse Matrix-Vector Operations

et al. 2021

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“…Some bio-inspired computing models, see e.g. spiking neural systems [80]- [83] parallel computing models can be used to abstract features.…”

Section: E Feature Selectionmentioning

confidence: 99%

Improved Prediction of Cell-Penetrating Peptides via Effective Orchestrating Amino Acid Composition Feature Representation

Cai

et al. 2019

IEEE Access

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Cell-penetrating peptides (CPPs) promote the transport of pharmacologically active molecules, such as nanoparticles, plasmid DNA and short interfering RNA. Accurate prediction of new CPPs is a prerequisite for in-depth study of such molecules. Biological experimental predictions can provide an accurate description of the penetrating properties of CPPs. However, predicting CPPs by wet laboratory experiments is both resource-intensive and time-consuming. Therefore, the development of effective calculation method prediction has become an important topic in the study of CPPs. Recently, numerous methods developed for predicting CPPs use amino acid composition, alone and the accuracies of such methods have been limited. In this study, we proposed a new CPP prediction framework, which integrates four amino acid composition features, and utilizes these features to help train Support Vector Machine (SVM) model as a classifier to predict CPPs. When performing on the training dataset CPP924, the proposed method achieves an accuracy of 92.3%, which is significantly better than the state-of-the-art methods. These results suggest that the framework can orchestrate various amino acid composition features predicted models flexibly with good performances.

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“…Therefore, some scholars are currently dedicated to combining membrane computing with neural networks. For example, according to the self-organizing and self-adaptive characteristics of the artificial neural network, SNP systems with a plastic structure have been proposed [41][42][43][44]. Inspired by Eckhorn's neuron model, coupled neural P systems are proposed [45].…”

Section: Introductionmentioning

confidence: 99%

Spiking Neural Membrane Computing Models

Liu

Ren

2021

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As third-generation neural network models, spiking neural P systems (SNP) have distributed parallel computing capabilities with good performance. In recent years, artificial neural networks have received widespread attention due to their powerful information processing capabilities, which is an effective combination of a class of biological neural networks and mathematical models. However, SNP systems have some shortcomings in numerical calculations. In order to improve the incompletion of current SNP systems in dealing with certain real data technology in this paper, we use neural network structure and data processing methods for reference. Combining them with membrane computing, spiking neural membrane computing models (SNMC models) are proposed. In SNMC models, the state of each neuron is a real number, and the neuron contains the input unit and the threshold unit. Additionally, there is a new style of rules for neurons with time delay. The way of consuming spikes is controlled by a nonlinear production function, and the produced spike is determined based on a comparison between the value calculated by the production function and the critical value. In addition, the Turing universality of the SNMC model as a number generator and acceptor is proved.

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Matrix representation and simulation algorithm of spiking neural P systems with structural plasticity

Cited by 29 publications

References 21 publications

Simulation of Spiking Neural P Systems with Sparse Matrix-Vector Operations

Simulation of Spiking Neural P Systems with Sparse Matrix-Vector Operations

Improved Prediction of Cell-Penetrating Peptides via Effective Orchestrating Amino Acid Composition Feature Representation

Spiking Neural Membrane Computing Models

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