1
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Zahra O, Navarro-Alarcon D, Tolu S. A Neurorobotic Embodiment for Exploring the Dynamical Interactions of a Spiking Cerebellar Model and a Robot Arm During Vision-Based Manipulation Tasks. Int J Neural Syst 2021; 32:2150028. [PMID: 34003083 DOI: 10.1142/s0129065721500283] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
While the original goal for developing robots is replacing humans in dangerous and tedious tasks, the final target shall be completely mimicking the human cognitive and motor behavior. Hence, building detailed computational models for the human brain is one of the reasonable ways to attain this. The cerebellum is one of the key players in our neural system to guarantee dexterous manipulation and coordinated movements as concluded from lesions in that region. Studies suggest that it acts as a forward model providing anticipatory corrections for the sensory signals based on observed discrepancies from the reference values. While most studies consider providing the teaching signal as error in joint-space, few studies consider the error in task-space and even fewer consider the spiking nature of the cerebellum on the cellular-level. In this study, a detailed cellular-level forward cerebellar model is developed, including modeling of Golgi and Basket cells which are usually neglected in previous studies. To preserve the biological features of the cerebellum in the developed model, a hyperparameter optimization method tunes the network accordingly. The efficiency and biological plausibility of the proposed cerebellar-based controller is then demonstrated under different robotic manipulation tasks reproducing motor behavior observed in human reaching experiments.
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Affiliation(s)
- Omar Zahra
- The Hong Kong Polytechnic University, Kowloon, Hong Kong
| | | | - Silvia Tolu
- Technical University of Denmark, Kongens Lyngby, Denmark
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2
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Liu M, Zhao F, Jiang X, Zhang H, Zhou H. Parallel Binary Image Cryptosystem Via Spiking Neural Networks Variants. Int J Neural Syst 2021; 32:2150014. [PMID: 33637028 DOI: 10.1142/s0129065721500143] [Citation(s) in RCA: 3] [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
Due to the inefficiency of multiple binary images encryption, a parallel binary image encryption framework based on the typical variants of spiking neural networks, spiking neural P (SNP) systems is proposed in this paper. More specifically, the two basic units in the proposed image cryptosystem, the permutation unit and the diffusion unit, are designed through SNP systems with multiple channels and polarizations (SNP-MCP systems), and SNP systems with astrocyte-like control (SNP-ALC systems), respectively. Different from the serial computing of the traditional image permutation/diffusion unit, SNP-MCP-based permutation/SNP-ALC-based diffusion unit can realize parallel computing through the parallel use of rules inside the neurons. Theoretical analysis results confirm the high efficiency of the binary image proposed cryptosystem. Security analysis experiments demonstrate the security of the proposed cryptosystem.
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Affiliation(s)
- Mingzhe Liu
- State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610051, P. R. China
| | - Feixiang Zhao
- State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610051, P. R. China
| | - Xin Jiang
- State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610051, P. R. China
| | - Hong Zhang
- State Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610051, P. R. China
| | - Helen Zhou
- School of Engineering, Manukau Institute of Technology, Auckland 1150, New Zealand
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3
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Rashvand P, Ahmadzadeh MR, Shayegh F. Design and Implementation of a Spiking Neural Network with Integrate-and-Fire Neuron Model for Pattern Recognition. Int J Neural Syst 2020; 31:2050073. [PMID: 33353527 DOI: 10.1142/s0129065720500732] [Citation(s) in RCA: 4] [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
In contrast to the previous artificial neural networks (ANNs), spiking neural networks (SNNs) work based on temporal coding approaches. In the proposed SNN, the number of neurons, neuron models, encoding method, and learning algorithm design are described in a correct and pellucid fashion. It is also discussed that optimizing the SNN parameters based on physiology, and maximizing the information they pass leads to a more robust network. In this paper, inspired by the "center-surround" structure of the receptive fields in the retina, and the amount of overlap that they have, a robust SNN is implemented. It is based on the Integrate-and-Fire (IF) neuron model and uses the time-to-first-spike coding to train the network by a newly proposed method. The Iris and MNIST datasets were employed to evaluate the performance of the proposed network whose accuracy, with 60 input neurons, was 96.33% on the Iris dataset. The network was trained in only 45 iterations indicating its reasonable convergence rate. For the MNIST dataset, when the gray level of each pixel was considered as input to the network, 600 input neurons were required, and the accuracy of the network was 90.5%. Next, 14 structural features were used as input. Therefore, the number of input neurons decreased to 210, and accuracy increased up to 95%, meaning that an SNN with fewer input neurons and good skill was implemented. Also, the ABIDE1 dataset is applied to the proposed SNN. Of the 184 data, 79 are used for healthy people and 105 for people with autism. One of the characteristics that can differentiate between these two classes is the entropy of the existing data. Therefore, Shannon entropy is used for feature extraction. Applying these values to the proposed SNN, an accuracy of 84.42% was achieved by only 120 iterations, which is a good result compared to the recent results.
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Affiliation(s)
- Parvaneh Rashvand
- Digital Signal Processing Research Lab, Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan 84156-83111, Iran
| | - Mohammad Reza Ahmadzadeh
- Digital Signal Processing Research Lab, Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan 84156-83111, Iran
| | - Farzaneh Shayegh
- Digital Signal Processing Research Lab, Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan 84156-83111, Iran
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4
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Naudin L, Corson N, Aziz-Alaoui MA, Jiménez Laredo JL, Démare T. On the Modeling of the Three Types of Non-spiking Neurons of the Caenorhabditis elegans. Int J Neural Syst 2020; 31:2050063. [PMID: 33269660 DOI: 10.1142/s012906572050063x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
The nematode Caenorhabditis elegans (C. elegans) is a well-known model organism in neuroscience. The relative simplicity of its nervous system, made up of few hundred neurons, shares some essential features with more sophisticated nervous systems, including the human one. If we are able to fully characterize the nervous system of this organism, we will be one step closer to understanding the mechanisms underlying the behavior of living things. Following a recently conducted electrophysiological survey on different C. elegans neurons, this paper aims at modeling the three non-spiking RIM, AIY and AFD neurons (arbitrarily named with three upper case letters by convention). To date, they represent the three possible forms of non-spiking neuronal responses of the C. elegans. To achieve this objective, we propose a conductance-based neuron model adapted to the electrophysiological features of each neuron. These features are based on current biological research and a series of in-silico experiments which use differential evolution to fit the model to experimental data. From the obtained results, we formulate a series of biological hypotheses regarding currents involved in the neuron dynamics. These models reproduce experimental data with a high degree of accuracy while being biologically consistent with state-of-the-art research.
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Affiliation(s)
- Loïs Naudin
- Normandie Univ, UNIHAVRE, LMAH, FR-CNRS-3335, ISCN, Le Havre 76600, France
| | - Nathalie Corson
- Normandie Univ, UNIHAVRE, LMAH, FR-CNRS-3335, ISCN, Le Havre 76600, France
| | - M A Aziz-Alaoui
- Normandie Univ, UNIHAVRE, LMAH, FR-CNRS-3335, ISCN, Le Havre 76600, France
| | | | - Thibaut Démare
- Normandie Univ, UNIHAVRE, LITIS, FR-CNRS-3638, ISCN, Le Havre 76600, France
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5
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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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6
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Bayro-Corrochano E, Solis-Gamboa S, Altamirano-Escobedo G, Lechuga-Gutierres L, Lisarraga-Rodriguez J. Quaternion Spiking and Quaternion Quantum Neural Networks: Theory and Applications. Int J Neural Syst 2020; 31:2050059. [PMID: 32938264 DOI: 10.1142/s0129065720500598] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Biological evidence shows that there are neural networks specialized for recognition of signals and patterns acting as associative memories. The spiking neural networks are another kind which receive input from a broad range of other brain areas to produce output that selects particular cognitive or motor actions to perform. An important contribution of this work is to consider the geometric processing in the modeling of feed-forward neural networks. Since quaternions are well suited to represent 3D rotations, it is then well justified to extend real-valued neural networks to quaternion-valued neural networks for task of perception and control of robot manipulators. This work presents the quaternion spiking neural networks which are able to control robots, where the examples confirm that these artificial neurons have the capacity to adapt on-line the robot to reach the desired position. Also, we present the quaternionic quantum neural networks for pattern recognition using just one quaternion neuron. In the experimental analysis, we show the excellent performance of both quaternion neural networks.
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Affiliation(s)
| | - Samuel Solis-Gamboa
- Department of Electrical Engineering and Computer Science, CINVESTAV Guadalajara, México
| | | | - Luis Lechuga-Gutierres
- Department of Electrical Engineering and Computer Science, CINVESTAV Guadalajara, México
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7
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Zhu M, Yang Q, Dong J, Zhang G, Gou X, Rong H, Paul P, Neri F. An Adaptive Optimization Spiking Neural P System for Binary Problems. Int J Neural Syst 2020; 31:2050054. [PMID: 32938261 DOI: 10.1142/s0129065720500549] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Optimization Spiking Neural P System (OSNPS) is the first membrane computing model to directly derive an approximate solution of combinatorial problems with a specific reference to the 0/1 knapsack problem. OSNPS is composed of a family of parallel Spiking Neural P Systems (SNPS) that generate candidate solutions of the binary combinatorial problem and a Guider algorithm that adjusts the spiking probabilities of the neurons of the P systems. Although OSNPS is a pioneering structure in membrane computing optimization, its performance is competitive with that of modern and sophisticated metaheuristics for the knapsack problem only in low dimensional cases. In order to overcome the limitations of OSNPS, this paper proposes a novel Dynamic Guider algorithm which employs an adaptive learning and a diversity-based adaptation to control its moving operators. The resulting novel membrane computing model for optimization is here named Adaptive Optimization Spiking Neural P System (AOSNPS). Numerical result shows that the proposed approach is effective to solve the 0/1 knapsack problems and outperforms multiple various algorithms proposed in the literature to solve the same class of problems even for a large number of items (high dimensionality). Furthermore, case studies show that a AOSNPS is effective in fault sections estimation of power systems in different types of fault cases: including a single fault, multiple faults and multiple faults with incomplete and uncertain information in the IEEE 39 bus system and IEEE 118 bus system.
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Affiliation(s)
- Ming Zhu
- School of Control Engineering, Chengdu University of Information Technology, Chengdu 610225, P. R. China
| | - Qiang Yang
- School of Control Engineering, Chengdu University of Information Technology, Chengdu 610225, P. R. China
| | - Jianping Dong
- College of Information Science and Technology, Chengdu University of Technology, Chengdu 610059, P. R. China
| | - Gexiang Zhang
- College of Information Science and Technology, Chengdu University of Technology, Chengdu 610059, P. R. China
| | - Xiantai Gou
- School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, P. R. China
| | - Haina Rong
- School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, P. R. China
| | - Prithwineel Paul
- School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, P. R. China
| | - Ferrante Neri
- COL Laboratory, School of Computer Science, University of Nottingham, Nottingham, UK
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8
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Zhang G, Rong H, Paul P, He Y, Neri F, Pérez-Jiménez MJ. A Complete Arithmetic Calculator Constructed from Spiking Neural P Systems and its Application to Information Fusion. Int J Neural Syst 2020; 31:2050055. [PMID: 32938262 DOI: 10.1142/s0129065720500550] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Several variants of spiking neural P systems (SNPS) have been presented in the literature to perform arithmetic operations. However, each of these variants was designed only for one specific arithmetic operation. In this paper, a complete arithmetic calculator implemented by SNPS is proposed. An application of the proposed calculator to information fusion is also proposed. The information fusion is implemented by integrating the following three elements: (1) an addition and subtraction SNPS already reported in the literature; (2) a modified multiplication and division SNPS; (3) a novel storage SNPS, i.e. a method based on SNPS is introduced to calculate basic probability assignment of an event. This is the first attempt to apply arithmetic operation SNPS to fuse multiple information. The effectiveness of the presented general arithmetic SNPS calculator is verified by means of several examples.
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Affiliation(s)
- Gexiang Zhang
- Research Center for Artificial Intelligence, Chengdu University of Technology, Chengdu 610059, P. R. China
| | - Haina Rong
- School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, P. R. China
| | - Prithwineel Paul
- School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, P. R. China
| | - Yangyang He
- School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, P. R. China
| | - Ferrante Neri
- COL Laboratory, School of Computer Science, University of Nottingham, Nottingham, UK
| | - Mario J Pérez-Jiménez
- Department of Computer Science and Artificial Intelligence, University of Sevilla, Avda. Reina Mercedes s/n 41012, Spain
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9
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Song X, Valencia-Cabrera L, Peng H, Wang J, Pérez-Jiménez MJ. Spiking Neural P Systems with Delay on Synapses. Int J Neural Syst 2020; 31:2050042. [PMID: 32701003 DOI: 10.1142/s0129065720500422] [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
Based on the feature and communication of neurons in animal neural systems, spiking neural P systems (SN P systems) were proposed as a kind of powerful computing model. Considering the length of axons and the information transmission speed on synapses, SN P systems with delay on synapses (SNP-DS systems) are proposed in this work. Unlike the traditional SN P systems, where all the postsynaptic neurons receive spikes at the same instant from their presynaptic neuron, the postsynaptic neurons in SNP-DS systems would receive spikes at different instants, depending on the delay time on the synapses connecting them. It is proved that the SNP-DS systems are universal as number generators. Two small universal SNP-DS systems, with standard or extended rules, are constructed to compute functions, using 56 and 36 neurons, respectively. Moreover, a simulator has been provided, in order to check the correctness of these two SNP-DS systems, thus providing an experimental validation of the universality of the systems designed.
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Affiliation(s)
- Xiaoxiao Song
- School of Electrical Engineering and Electronic Information and Key Laboratory of Fluid and Power Machinery, Ministry of Education, Xihua University, Chengdu, Sichuan 610039, P. R. China
| | - Luis Valencia-Cabrera
- Research Group on Natural Computing, Department of Computer Science and Artificial Intelligence, University of Sevilla, Sevilla, Andalucía 41004, Spain
| | - Hong Peng
- School of Computer and Software Engineering, Xihua University, Chengdu, Sichuan 610039, P. R. China
| | - Jun Wang
- School of Electrical Engineering and Electronic Information and Key Laboratory of Fluid and Power Machinery, Ministry of Education, Xihua University, Chengdu, Sichuan 610039, P. R. China
| | - Mario J Pérez-Jiménez
- Research Group on Natural Computing, Department of Computer Science and Artificial Intelligence, University of Sevilla, Sevilla, Andalucía 41004, Spain
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10
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Caligiore D, Mirino P. How the Cerebellum and Prefrontal Cortex Cooperate During Trace Eyeblinking Conditioning. Int J Neural Syst 2020; 30:2050041. [PMID: 32618205 DOI: 10.1142/s0129065720500410] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Several data have demonstrated that during the widely used experimental paradigm for studying associative learning, trace eye blinking conditioning (TEBC), there is a strong interaction between cerebellum and medial prefrontal cortex (mPFC). Despite this evidence, the neural mechanisms underlying this interaction are still not clear. Here, we propose a neurophysiologically plausible computational model to address this issue. The model is constrained on the basis of two critical anatomo-physiological features: (i) the cerebello-cortical organization through two circuits, respectively, targeting M1 and mPFC; (ii) the different timing in the plasticity mechanisms of these parallel circuits produced by the granule cells time sensitivity according to which different subpopulations are active at different moments during conditioned stimuli. The computer simulations run with the model suggest that these features are critical to understand how the cooperation between cerebellum and mPFC supports motor areas during TEBC. In particular, a greater trace interval produces greater plasticity changes at the slow path synapses involving mPFC with respect to plasticity changes at the fast path involving M1. As a consequence, the greater is the trace interval, the stronger is the mPFC involvement. The model has been validated by reproducing data collected through recent real mice experiments.
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Affiliation(s)
- Daniele Caligiore
- Computational and Translational Neuroscience Laboratory (CTNLab), Institute of Cognitive Sciences and Technologies, National Research Council, Via San Martino della Battaglia 44, Rome, 00185, Italy
| | - Pierandrea Mirino
- Department of Psychology, Sapienza University of Rome, Via dei Marsi 78, Rome, 00185, Italy
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11
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Kheradpisheh SR, Masquelier T. Temporal Backpropagation for Spiking Neural Networks with One Spike per Neuron. Int J Neural Syst 2020; 30:2050027. [DOI: 10.1142/s0129065720500276] [Citation(s) in RCA: 57] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
We propose a new supervised learning rule for multilayer spiking neural networks (SNNs) that use a form of temporal coding known as rank-order-coding. With this coding scheme, all neurons fire exactly one spike per stimulus, but the firing order carries information. In particular, in the readout layer, the first neuron to fire determines the class of the stimulus. We derive a new learning rule for this sort of network, named S4NN, akin to traditional error backpropagation, yet based on latencies. We show how approximated error gradients can be computed backward in a feedforward network with any number of layers. This approach reaches state-of-the-art performance with supervised multi-fully connected layer SNNs: test accuracy of 97.4% for the MNIST dataset, and 99.2% for the Caltech Face/Motorbike dataset. Yet, the neuron model that we use, nonleaky integrate-and-fire, is much simpler than the one used in all previous works. The source codes of the proposed S4NN are publicly available at https://github.com/SRKH/S4NN .
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Affiliation(s)
- Saeed Reza Kheradpisheh
- Department of Computer and Data Sciences, Faculty of Mathematical Sciences, Shahid Beheshti University, Tehran, Iran
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12
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Peng H, Lv Z, Li B, Luo X, Wang J, Song X, Wang T, Pérez-Jiménez MJ, Riscos-Núñez A. Nonlinear Spiking Neural P Systems. Int J Neural Syst 2020; 30:2050008. [DOI: 10.1142/s0129065720500082] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
This paper proposes a new variant of spiking neural P systems (in short, SNP systems), nonlinear spiking neural P systems (in short, NSNP systems). In NSNP systems, the state of each neuron is denoted by a real number, and a real configuration vector is used to characterize the state of the whole system. A new type of spiking rules, nonlinear spiking rules, is introduced to handle the neuron’s firing, where the consumed and generated amounts of spikes are often expressed by the nonlinear functions of the state of the neuron. NSNP systems are a class of distributed parallel and nondeterministic computing systems. The computational power of NSNP systems is discussed. Specifically, it is proved that NSNP systems as number-generating/accepting devices are Turing-universal. Moreover, we establish two small universal NSNP systems for function computing and number generator, containing 117 neurons and 164 neurons, respectively.
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Affiliation(s)
- Hong Peng
- School of Computer and Software Engineering, Xihua University, Chengdu, Sichuan 610039, P. R. China
| | - Zeqiong Lv
- School of Computer and Software Engineering, Xihua University, Chengdu, Sichuan 610039, P. R. China
| | - Bo Li
- School of Computer and Software Engineering, Xihua University, Chengdu, Sichuan 610039, P. R. China
| | - Xiaohui Luo
- School of Computer and Software Engineering, Xihua University, Chengdu, Sichuan 610039, P. R. China
| | - Jun Wang
- School of Electrical Engineering and Electronic Information, Xihua University, Chengdu, Sichuan 610039, P. R. China
| | - Xiaoxiao Song
- School of Electrical Engineering and Electronic Information, Xihua University, Chengdu, Sichuan 610039, P. R. China
| | - Tao Wang
- School of Electrical Engineering and Electronic Information, Xihua University, Chengdu, Sichuan 610039, P. R. China
| | - Mario J. Pérez-Jiménez
- Research Group of Natural Computing, Department of Computer Sciences and Artificial Intelligence, School of Computer Engineering, University of Seville, 41012, C. P. Sevilla, Spain
| | - Agustín Riscos-Núñez
- Research Group of Natural Computing, Department of Computer Sciences and Artificial Intelligence, School of Computer Engineering, University of Seville, 41012, C. P. Sevilla, Spain
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