1
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Yin X, Liu X, Sun M, Xue J. Hypergraph-Based Numerical Spiking Neural Membrane Systems with Novel Repartition Protocols. Int J Neural Syst 2024; 34:2450039. [PMID: 38715253 DOI: 10.1142/s0129065724500394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/13/2024]
Abstract
The classic spiking neural P (SN P) systems abstract the real biological neural network into a simple structure based on graphs, where neurons can only communicate on the plane. This study proposes the hypergraph-based numerical spiking neural membrane (HNSNM) systems with novel repartition protocols. Through the introduction of hypergraphs, the HNSNM systems can characterize the high-order relationships among neurons and extend the traditional neuron structure to high-dimensional nonlinear spaces. The HNSNM systems also abstract two biological mechanisms of synapse creation and pruning, and use plasticity rules with repartition protocols to achieve planar, hierarchical and spatial communications among neurons in hypergraph neuron structures. Through imitating register machines, the Turing universality of the HNSNM systems is proved by using them as number generating and accepting devices. A universal HNSNM system consisting of 41 neurons is constructed to compute arbitrary functions. By solving NP-complete problems using the subset sum problem as an example, the computational efficiency and effectiveness of HNSNM systems are verified.
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Affiliation(s)
- Xiu Yin
- Business School, Shandong Normal University, Jinan 250014, P. R. China
| | - Xiyu Liu
- Business School, Shandong Normal University, Jinan 250014, P. R. China
| | - Minghe Sun
- College of Business, The University of Texas at San Antonio, San Antonio, TX 78249, USA
| | - Jie Xue
- Business School, Shandong Normal University, Jinan 250014, P. R. China
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2
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Hernández-Tello J, Martínez-Del-Amor MÁ, Orellana-Martín D, Cabarle FGC. Sparse Spiking Neural-Like Membrane Systems on Graphics Processing Units. Int J Neural Syst 2024; 34:2450038. [PMID: 38755115 DOI: 10.1142/s0129065724500382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/18/2024]
Abstract
The parallel simulation of Spiking Neural P systems is mainly based on a matrix representation, where the graph inherent to the neural model is encoded in an adjacency matrix. The simulation algorithm is based on a matrix-vector multiplication, which is an operation efficiently implemented on parallel devices. However, when the graph of a Spiking Neural P system is not fully connected, the adjacency matrix is sparse and hence, lots of computing resources are wasted in both time and memory domains. For this reason, two compression methods for the matrix representation were proposed in a previous work, but they were not implemented nor parallelized on a simulator. In this paper, they are implemented and parallelized on GPUs as part of a new Spiking Neural P system with delays simulator. Extensive experiments are conducted on high-end GPUs (RTX2080 and A100 80GB), and it is concluded that they outperform other solutions based on state-of-the-art GPU libraries when simulating Spiking Neural P systems.
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Affiliation(s)
- Javier Hernández-Tello
- Research Group on Natural Computing, Department of Computer Science and Artificial Intelligence, I3US, SCORE Lab, Universidad de Sevilla, Avda. Reina Mercedes s/n, 41012, Sevilla, Spain
| | - Miguel Á Martínez-Del-Amor
- Research Group on Natural Computing, Department of Computer Science and Artificial Intelligence, I3US, SCORE Lab, Universidad de Sevilla, Avda. Reina Mercedes s/n, 41012, Sevilla, Spain
| | - David Orellana-Martín
- Research Group on Natural Computing, Department of Computer Science and Artificial Intelligence, I3US, SCORE Lab, Universidad de Sevilla, Avda. Reina Mercedes s/n, 41012, Sevilla, Spain
| | - Francis George C Cabarle
- Research Group on Natural Computing, Department of Computer Science and Artificial Intelligence, I3US, SCORE Lab, Universidad de Sevilla, Avda. Reina Mercedes s/n, 41012, Sevilla, Spain
- Department of Computer Science, University of the Philippines Diliman, Quezon City, Philippines 1101, Philippines
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3
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Fu J, Peng H, Li B, Liu Z, Lugu R, Wang J, Ramírez-de-Arellano A. Multitask Adversarial Networks Based on Extensive Nonlinear Spiking Neuron Models. Int J Neural Syst 2024; 34:2450032. [PMID: 38624267 DOI: 10.1142/s0129065724500321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/17/2024]
Abstract
Deep learning technology has been successfully used in Chest X-ray (CXR) images of COVID-19 patients. However, due to the characteristics of COVID-19 pneumonia and X-ray imaging, the deep learning methods still face many challenges, such as lower imaging quality, fewer training samples, complex radiological features and irregular shapes. To address these challenges, this study first introduces an extensive NSNP-like neuron model, and then proposes a multitask adversarial network architecture based on ENSNP-like neurons for chest X-ray images of COVID-19, called MAE-Net. The MAE-Net serves two tasks: (i) converting low-quality CXR images to high-quality images; (ii) classifying CXR images of COVID-19. The adversarial architecture of MAE-Net uses two generators and two discriminators, and two new loss functions have been introduced to guide the optimization of the network. The MAE-Net is tested on four benchmark COVID-19 CXR image datasets and compared them with eight deep learning models. The experimental results show that the proposed MAE-Net can enhance the conversion quality and the accuracy of image classification results.
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Affiliation(s)
- Jun Fu
- School of Computer and Software Engineering, Xihua University, Chengdu 610039, P. R. China
| | - Hong Peng
- School of Computer and Software Engineering, Xihua University, Chengdu 610039, P. R. China
| | - Bing Li
- School of Computer and Software Engineering, Xihua University, Chengdu 610039, P. R. China
| | - Zhicai Liu
- School of Computer and Software Engineering, Xihua University, Chengdu 610039, P. R. China
| | - Rikong Lugu
- School of Computer and Software Engineering, Xihua University, Chengdu 610039, P. R. China
| | - Jun Wang
- School of Electrical Engineering and Electronic Information, Xihua University, Chengdu 610039, P. R. China
| | - Antonio Ramírez-de-Arellano
- Research Group of Natural Computing, Department of Computer Science and Artificial Intelligence, University of Seville, Sevilla 41012, Spain
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4
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Peng H, Xiong X, Wu M, Wang J, Yang Q, Orellana-Martín D, Pérez-Jiménez MJ. Reservoir computing models based on spiking neural P systems for time series classification. Neural Netw 2024; 169:274-281. [PMID: 37918270 DOI: 10.1016/j.neunet.2023.10.041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 09/12/2023] [Accepted: 10/25/2023] [Indexed: 11/04/2023]
Abstract
Nonlinear spiking neural P (NSNP) systems are neural-like membrane computing models with nonlinear spiking mechanisms. Because of this nonlinear spiking mechanism, NSNP systems can show rich nonlinear dynamics. Reservoir computing (RC) is a novel recurrent neural network (RNN) and can overcome some shortcomings of traditional RNNs. Based on NSNP systems, we developed two RC variants for time series classification, RC-SNP and RC-RMS-SNP, which are without and integrated with reservoir model space (RMS), respectively. The two RC variants use NSNP systems as the reservoirs and can be easily implemented in the RC framework. The proposed two RC variants were evaluated on 17 benchmark time series classification datasets and compared with 16 state-of-the-art or baseline classification models. The comparison results demonstrate the effectiveness of the proposed two RC variants for time series classification tasks.
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Affiliation(s)
- Hong Peng
- School of Computer and Software Engineering, Xihua University, Chengdu, 610039, China.
| | - Xin Xiong
- School of Computer and Software Engineering, Xihua University, Chengdu, 610039, China
| | - Min Wu
- School of Computer and Software Engineering, Xihua University, Chengdu, 610039, China
| | - Jun Wang
- School of Electrical Engineering and Electronic Information, Xihua University, Chengdu, 610039, China
| | - Qian Yang
- School of Computer and Software Engineering, Xihua University, Chengdu, 610039, China
| | - David Orellana-Martín
- Research Group of Natural Computing, Department of Computer Science and Artificial Intelligence, University of Seville, Sevilla, 41012, Spain
| | - Mario J Pérez-Jiménez
- Research Group of Natural Computing, Department of Computer Science and Artificial Intelligence, University of Seville, Sevilla, 41012, Spain
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5
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Liu Q, Long L, Peng H, Wang J, Yang Q, Song X, Riscos-Nunez A, Perez-Jimenez MJ. Gated Spiking Neural P Systems for Time Series Forecasting. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023; 34:6227-6236. [PMID: 34936560 DOI: 10.1109/tnnls.2021.3134792] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Spiking neural P (SNP) systems are a class of neural-like computing models, abstracted by the mechanism of spiking neurons. This article proposes a new variant of SNP systems, called gated spiking neural P (GSNP) systems, which are composed of gated neurons. Two gated mechanisms are introduced in the nonlinear spiking mechanism of GSNP systems, consisting of a reset gate and a consumption gate. The two gates are used to control the updating of states in neurons. Based on gated neurons, a prediction model for time series is developed, known as the GSNP model. Several benchmark univariate and multivariate time series are used to evaluate the proposed GSNP model and to compare several state-of-the-art prediction models. The comparison results demonstrate the availability and effectiveness of GSNP for time series forecasting.
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6
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Liu Q, Huang Y, Yang Q, Peng H, Wang J. An Attention-Aware Long Short-Term Memory-Like Spiking Neural Model for Sentiment Analysis. Int J Neural Syst 2023; 33:2350037. [PMID: 37303084 DOI: 10.1142/s0129065723500375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
LSTM-SNP model is a recently developed long short-term memory (LSTM) network, which is inspired from the mechanisms of spiking neural P (SNP) systems. In this paper, LSTM-SNP is utilized to propose a novel model for aspect-level sentiment analysis, termed as ALS model. The LSTM-SNP model has three gates: reset gate, consumption gate and generation gate. Moreover, attention mechanism is integrated with LSTM-SNP model. The ALS model can better capture the sentiment features in the text to compute the correlation between context and aspect words. To validate the effectiveness of the ALS model for aspect-level sentiment analysis, comparison experiments with 17 baseline models are conducted on three real-life data sets. The experimental results demonstrate that the ALS model has a simpler structure and can achieve better performance compared to these baseline models.
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Affiliation(s)
- Qian Liu
- School of Computer and Software Engineering, Xihua University, P. R. China
| | - Yanping Huang
- School of Computer and Software Engineering, Xihua University, P. R. China
| | - Qian Yang
- School of Computer and Software Engineering, Xihua University, P. R. China
| | - Hong Peng
- School of Computer and Software Engineering, Xihua University, P. R. China
| | - Jun Wang
- School of Electrical Engineering and Electronic Information, Xihua University, P. R. China
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7
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Wang L, Liu X, Sun M, Zhao Y. Evolution-communication spiking neural P systems with energy request rules. Neural Netw 2023; 164:476-488. [PMID: 37201308 DOI: 10.1016/j.neunet.2023.05.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 03/07/2023] [Accepted: 05/03/2023] [Indexed: 05/20/2023]
Abstract
Evolution-communication spiking neural P systems with energy request rules (ECSNP-ER systems) are proposed and developed as a new variant of evolution-communication spiking neural P systems. In ECSNP-ER systems, in addition to spike-evolution rules and spike-communication rules, neurons also have energy request rules. Energy request rules are used to obtain energy from the environment needed for spike evolution and communication in neurons. The definition, structure and operations of ECSNP-ER systems are presented in detail. ECSNP-ER systems are proved to have the same computing capabilities as Turing machines by using them as number generating/accepting devices and function computing devices. Working non-deterministically, ECSNP-ER systems are used to solve NP-complete problems, using the SAT problem as an example, in linear time.
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Affiliation(s)
- Liping Wang
- College of Business, Shandong Normal University, Jinan, China
| | - Xiyu Liu
- College of Business, Shandong Normal University, Jinan, China.
| | - Minghe Sun
- Carlos Alvarez College of Business, The University of Texas at San Antonio, San Antonio, USA
| | - Yuzhen Zhao
- College of Business, Shandong Normal University, Jinan, China.
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8
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Xian R, Lugu R, Peng H, Yang Q, Luo X, Wang J. Edge Detection Method Based on Nonlinear Spiking Neural Systems. Int J Neural Syst 2023; 33:2250060. [PMID: 36328966 DOI: 10.1142/s0129065722500605] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Nonlinear spiking neural P (NSNP) systems are a class of neural-like computational models inspired from the nonlinear mechanism of spiking neurons. NSNP systems have a distinguishing feature: nonlinear spiking mechanism. To handle edge detection of images, this paper proposes a variant, nonlinear spiking neural P (NSNP) systems with two outputs (TO), termed as NSNP-TO systems. Based on NSNP-TO system, an edge detection framework is developed, termed as ED-NSNP detector. The detection ability of ED-NSNP detector relies on two convolutional kernels. To obtain good detection performance, particle swarm optimization (PSO) is used to optimize the parameters of the two convolutional kernels. The proposed ED-NSNP detector is evaluated on several open benchmark images and compared with seven baseline edge detection methods. The comparison results indicate the availability and effectiveness of the proposed ED-NSNP detector.
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Affiliation(s)
- Ronghao Xian
- School of Computer and Software Engineering, Xihua University, Chengdu 610039, P. R. China
| | - Rikong Lugu
- School of Computer and Software Engineering, Xihua University, Chengdu 610039, P. R. China
| | - Hong Peng
- School of Computer and Software Engineering, Xihua University, Chengdu 610039, P. R. China
| | - Qian Yang
- School of Computer and Software Engineering, Xihua University, Chengdu 610039, P. R. China
| | - Xiaohui Luo
- School of Computer and Software Engineering, Xihua University, Chengdu 610039, P. R. China
| | - Jun Wang
- School of Electrical Engineering and Electronic Information, Xihua University, Chengdu 610039, P. R. China
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9
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An Optimization Numerical Spiking Neural P System for Solving Constrained Optimization Problems. Inf Sci (N Y) 2023. [DOI: 10.1016/j.ins.2023.01.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
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10
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Huang Y, Peng H, Liu Q, Yang Q, Wang J, Orellana-Martín D, Pérez-Jiménez MJ. Attention-enabled gated spiking neural P model for aspect-level sentiment classification. Neural Netw 2022; 157:437-443. [DOI: 10.1016/j.neunet.2022.11.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2022] [Revised: 10/19/2022] [Accepted: 11/03/2022] [Indexed: 11/11/2022]
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11
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Long L, Lugu R, Xiong X, Liu Q, Peng H, Wang J, Orellana-Martín D, Pérez-Jiménez MJ. Echo spiking neural P systems. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.109568] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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12
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Multivariate time series forecasting method based on nonlinear spiking neural P systems and non-subsampled shearlet transform. Neural Netw 2022; 152:300-310. [DOI: 10.1016/j.neunet.2022.04.030] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2022] [Revised: 04/01/2022] [Accepted: 04/28/2022] [Indexed: 11/23/2022]
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13
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14
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Long L, Liu Q, Peng H, Yang Q, Luo X, Wang J, Song X. A Time Series Forecasting Approach Based on Nonlinear Spiking Neural Systems. Int J Neural Syst 2022; 32:2250020. [PMID: 35258438 DOI: 10.1142/s0129065722500204] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Nonlinear spiking neural P (NSNP) systems are a recently developed theoretical model, which is abstracted by nonlinear spiking mechanism of biological neurons. NSNP systems have a nonlinear structure and the potential to describe nonlinear dynamic systems. Based on NSNP systems, a novel time series forecasting approach is developed in this paper. During the training phase, a time series is first converted to frequency domain by using a redundant wavelet transform, and then according to the frequency data, an NSNP system is automatically constructed and adaptively trained in frequency domain. Then, the well-trained NSNP system can automatically generate sequence data for future time as the prediction results. Eight benchmark time series data sets and two real-life time series data sets are utilized to compare the proposed approach with several state-of-the-art forecasting approaches. The comparison results demonstrate availability and effectiveness of the proposed forecasting approach.
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Affiliation(s)
- Lifan Long
- School of Computer and Software Engineering, Xihua University, Chengdu 610039, P. R. China
| | - Qian Liu
- School of Computer and Software Engineering, Xihua University, Chengdu 610039, P. R. China
| | - Hong Peng
- School of Computer and Software Engineering, Xihua University, Chengdu 610039, P. R. China
| | - Qian Yang
- School of Computer and Software Engineering, Xihua University, Chengdu 610039, P. R. China
| | - Xiaohui Luo
- School of Computer and Software Engineering, Xihua University, Chengdu 610039, P. R. China
| | - Jun Wang
- School of Computer and Software Engineering, Xihua University, Chengdu 610039, P. R. China
| | - Xiaoxiao Song
- School of Computer and Software Engineering, Xihua University, Chengdu 610039, P. R. China
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15
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An unsupervised segmentation method based on dynamic threshold neural P systems for color images. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2021.12.058] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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16
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Liu Q, Long L, Yang Q, Peng H, Wang J, Luo X. LSTM-SNP: A long short-term memory model inspired from spiking neural P systems. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2021.107656] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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17
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Wang L, Liu X, Zhao Y. Universal Nonlinear Spiking Neural P Systems with Delays and Weights on Synapses. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2021; 2021:3285719. [PMID: 34484319 PMCID: PMC8413071 DOI: 10.1155/2021/3285719] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Accepted: 08/06/2021] [Indexed: 12/04/2022]
Abstract
The nonlinear spiking neural P systems (NSNP systems) are new types of computation models, in which the state of neurons is represented by real numbers, and nonlinear spiking rules handle the neuron's firing. In this work, in order to improve computing performance, the weights and delays are introduced to the NSNP system, and universal nonlinear spiking neural P systems with delays and weights on synapses (NSNP-DW) are proposed. Weights are treated as multiplicative constants by which the number of spikes is increased when transiting across synapses, and delays take into account the speed at which the synapses between neurons transmit information. As a distributed parallel computing model, the Turing universality of the NSNP-DW system as number generating and accepting devices is proven. 47 and 43 neurons are sufficient for constructing two small universal NSNP-DW systems. The NSNP-DW system solving the Subset Sum problem is also presented in this work.
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Affiliation(s)
- Liping Wang
- Business School, Shandong Normal University, Jinan, China
| | - Xiyu Liu
- Business School, Shandong Normal University, Jinan, China
| | - Yuzhen Zhao
- Business School, Shandong Normal University, Jinan, China
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18
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Abstract
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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19
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Garcia L, Sanchez G, Vazquez E, Avalos G, Anides E, Nakano M, Sanchez G, Perez H. Small universal spiking neural P systems with dendritic/axonal delays and dendritic trunk/feedback. Neural Netw 2021; 138:126-139. [PMID: 33639581 DOI: 10.1016/j.neunet.2021.02.010] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Revised: 12/18/2020] [Accepted: 02/09/2021] [Indexed: 10/22/2022]
Abstract
In spiking neural P (SN P) systems, neurons are interconnected by means of synapses, and they use spikes to communicate with each other. However, in biology, the complex structure of dendritic tree is also an important part in the communication scheme between neurons since these structures are linked to advanced neural process such as learning and memory formation. In this work, we present a new variant of the SN P systems inspired by diverse dendrite and axon phenomena such as dendritic feedback, dendritic trunk, dendritic delays and axonal delays, respectively. This new variant is referred to as a spiking neural P system with dendritic and axonal computation (DACSN P system). Specifically, we include experimentally proven biological features in the current SN P systems to reduce the computational complexity of the soma by providing it with stable firing patterns through dendritic delays, dendritic feedback and axonal delays. As a consequence, the proposed DACSN P systems use the minimum number of synapses and neurons with simple and homogeneous standard spiking rules. Here, we study the computational capabilities of a DACSN P system. In particular, we prove that DACSN P systems with dendritic and axonal behavior are universal as both number-accepting/generating devices. In addition, we constructed a small universal SN P system using 39 neurons with standard spiking rules to compute any Turing computable function.
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Affiliation(s)
- Luis Garcia
- Instituto Politécnico Nacional ESIME Culhuacan, Av. Santana 1000, Coyoacan, 04260, Ciudad de México, Mexico
| | - Giovanny Sanchez
- Instituto Politécnico Nacional ESIME Culhuacan, Av. Santana 1000, Coyoacan, 04260, Ciudad de México, Mexico.
| | - Eduardo Vazquez
- Instituto Politécnico Nacional ESIME Culhuacan, Av. Santana 1000, Coyoacan, 04260, Ciudad de México, Mexico
| | - Gerardo Avalos
- Instituto Politécnico Nacional ESIME Culhuacan, Av. Santana 1000, Coyoacan, 04260, Ciudad de México, Mexico
| | - Esteban Anides
- Instituto Politécnico Nacional ESIME Culhuacan, Av. Santana 1000, Coyoacan, 04260, Ciudad de México, Mexico
| | - Mariko Nakano
- Instituto Politécnico Nacional ESIME Culhuacan, Av. Santana 1000, Coyoacan, 04260, Ciudad de México, Mexico
| | - Gabriel Sanchez
- Instituto Politécnico Nacional ESIME Culhuacan, Av. Santana 1000, Coyoacan, 04260, Ciudad de México, Mexico
| | - Hector Perez
- Instituto Politécnico Nacional ESIME Culhuacan, Av. Santana 1000, Coyoacan, 04260, Ciudad de México, Mexico
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20
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Orellana-Martín D, Martínez-Del-Amor MÁ, Valencia-Cabrera L, Pérez-Hurtado I, Riscos-Núñez A, Pérez-Jiménez MJ. Dendrite P Systems Toolbox: Representation, Algorithms and Simulators. Int J Neural Syst 2020; 31:2050071. [PMID: 33200621 DOI: 10.1142/s0129065720500719] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Dendrite P systems (DeP systems) are a recently introduced neural-like model of computation. They provide an alternative to the more classical spiking neural (SN) P systems. In this paper, we present the first software simulator for DeP systems, and we investigate the key features of the representation of the syntax and semantics of such systems. First, the conceptual design of a simulation algorithm is discussed. This is helpful in order to shade a light on the differences with simulators for SN P systems, and also to identify potential parallelizable parts. Second, a novel simulator implemented within the P-Lingua simulation framework is presented. Moreover, MeCoSim, a GUI tool for abstract representation of problems based on P system models has been extended to support this model. An experimental validation of this simulator is also covered.
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Affiliation(s)
- David Orellana-Martín
- Research Group on Natural Computing, Department of Computer Science and Artificial Intelligence, Universidad de Sevilla, Avenida Reina Mercedes s/n, 41012 Sevilla, Spain
| | - Miguel Á Martínez-Del-Amor
- Research Group on Natural Computing, Department of Computer Science and Artificial Intelligence, Universidad de Sevilla, Avenida Reina Mercedes s/n, 41012 Sevilla, Spain
| | - Luis Valencia-Cabrera
- Research Group on Natural Computing, Department of Computer Science and Artificial Intelligence, Universidad de Sevilla, Avenida Reina Mercedes s/n, 41012 Sevilla, Spain
| | - Ignacio Pérez-Hurtado
- Research Group on Natural Computing, Department of Computer Science and Artificial Intelligence, Universidad de Sevilla, Avenida Reina Mercedes s/n, 41012 Sevilla, Spain
| | - Agustín Riscos-Núñez
- Research Group on Natural Computing, Department of Computer Science and Artificial Intelligence, Universidad de Sevilla, Avenida Reina Mercedes s/n, 41012 Sevilla, Spain
| | - Mario J Pérez-Jiménez
- Research Group on Natural Computing, Department of Computer Science and Artificial Intelligence, Universidad de Sevilla, Avenida Reina Mercedes s/n, 41012 Sevilla, Spain
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Turing Universality of Weighted Spiking Neural P Systems with Anti-spikes. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2020; 2020:8892240. [PMID: 33014033 PMCID: PMC7519440 DOI: 10.1155/2020/8892240] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Revised: 07/19/2020] [Accepted: 08/28/2020] [Indexed: 11/29/2022]
Abstract
Weighted spiking neural P systems with anti-spikes (AWSN P systems) are proposed by adding anti-spikes to spiking neural P systems with weighted synapses. Anti-spikes behave like spikes of inhibition of communication between neurons. Both spikes and anti-spikes are used in the rule expressions. An illustrative example is given to show the working process of the proposed AWSN P systems. The Turing universality of the proposed P systems as number generating and accepting devices is proved. Finally, a universal AWSN P system having 34 neurons is proved to work as a function computing device by using standard rules, and one having 30 neurons is proved to work as a number generator.
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22
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Li B, Peng H, Luo X, Wang J, Song X, Pérez-Jiménez MJ, Riscos-Núñez A. Medical Image Fusion Method Based on Coupled Neural P Systems in Nonsubsampled Shearlet Transform Domain. Int J Neural Syst 2020; 31:2050050. [PMID: 32808852 DOI: 10.1142/s0129065720500501] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Coupled neural P (CNP) systems are a recently developed Turing-universal, distributed and parallel computing model, combining the spiking and coupled mechanisms of neurons. This paper focuses on how to apply CNP systems to handle the fusion of multi-modality medical images and proposes a novel image fusion method. Based on two CNP systems with local topology, an image fusion framework in nonsubsampled shearlet transform (NSST) domain is designed, where the two CNP systems are used to control the fusion of low-frequency NSST coefficients. The proposed fusion method is evaluated on 20 pairs of multi-modality medical images and compared with seven previous fusion methods and two deep-learning-based fusion methods. Quantitative and qualitative experimental results demonstrate the advantage of the proposed fusion method in terms of visual quality and fusion performance.
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Affiliation(s)
- Bo Li
- School of Computer and Software Engineering, Xihua University, Chengdu 610039, P. R. China
| | - Hong Peng
- School of Computer and Software Engineering, Xihua University, Chengdu 610039, P. R. China
| | - Xiaohui Luo
- School of Computer and Software Engineering, Xihua University, Chengdu 610039, P. R. China
| | - Jun Wang
- School of Computer and Software Engineering, Xihua University, Chengdu 610039, P. R. China
| | - Xiaoxiao Song
- School of Computer and Software Engineering, Xihua University, Chengdu 610039, P. R. China
| | - Mario J Pérez-Jiménez
- Research Group of Natural Computing, Department of Computer Science and Artificial Intelligence, University of Seville, Sevilla 41012, Spain
| | - Agustín Riscos-Núñez
- Research Group of Natural Computing, Department of Computer Science and Artificial Intelligence, University of Seville, Sevilla 41012, Spain
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23
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Lv Z, Bao T, Zhou N, Peng H, Huang X, Riscos-Núñez A, Pérez-Jiménez MJ. Spiking Neural P Systems with Extended Channel Rules. Int J Neural Syst 2020; 31:2050049. [PMID: 32808853 DOI: 10.1142/s0129065720500495] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
This paper discusses a new variant of spiking neural P systems (in short, SNP systems), spiking neural P systems with extended channel rules (in short, SNP-ECR systems). SNP-ECR systems are a class of distributed parallel computing models. In SNP-ECR systems, a new type of spiking rule is introduced, called ECR. With an ECR, a neuron can send the different numbers of spikes to its subsequent neurons. Therefore, SNP-ECR systems can provide a stronger firing control mechanism compared with SNP systems and the variant with multiple channels. We discuss the Turing universality of SNP-ECR systems. It is proven that SNP-ECR systems as number generating/accepting devices are Turing universal. Moreover, we provide a small universal SNP-ECR system as function computing devices.
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Affiliation(s)
- Zeqiong Lv
- School of Computer and Software Enginering, Xihua University, Chengdu, 610039, P. R. China
| | - Tingting Bao
- School of Computer and Software Enginering, Xihua University, Chengdu, 610039, P. R. China
| | - Nan Zhou
- School of Computer and Software Enginering, Xihua University, Chengdu, 610039, P. R. China
| | - Hong Peng
- School of Computer and Software Enginering, Xihua University, Chengdu, 610039, P. R. China
| | - Xiangnian Huang
- Research Group of Natural Computing, Department of Computer Science and Artificial Intelligence, University of Seville, Sevilla, 41012, Spain
| | - Agustín Riscos-Núñez
- Research Group of Natural Computing, Department of Computer Science and Artificial Intelligence, University of Seville, Sevilla, 41012, Spain
| | - Mario J Pérez-Jiménez
- Research Group of Natural Computing, Department of Computer Science and Artificial Intelligence, University of Seville, Sevilla, 41012, Spain
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