1
|
Ghasemi M, Raeissi ZM, Foroutannia A, Mohammadian M, Shakeriaski F. Dynamic Effects Analysis in Fractional Memristor-Based Rulkov Neuron Model. Biomimetics (Basel) 2024; 9:543. [PMID: 39329565 PMCID: PMC11430206 DOI: 10.3390/biomimetics9090543] [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: 08/13/2024] [Revised: 09/03/2024] [Accepted: 09/06/2024] [Indexed: 09/28/2024] Open
Abstract
Mathematical models such as Fitzhugh-Nagoma and Hodgkin-Huxley models have been used to understand complex nervous systems. Still, due to their complexity, these models have made it challenging to analyze neural function. The discrete Rulkov model allows the analysis of neural function to facilitate the investigation of neuronal dynamics or others. This paper introduces a fractional memristor Rulkov neuron model and analyzes its dynamic effects, investigating how to improve neuron models by combining discrete memristors and fractional derivatives. These improvements include the more accurate generation of heritable properties compared to full-order models, the treatment of dynamic firing activity at multiple time scales for a single neuron, and the better performance of firing frequency responses in fractional designs compared to integer models. Initially, we combined a Rulkov neuron model with a memristor and evaluated all system parameters using bifurcation diagrams and the 0-1 chaos test. Subsequently, we applied a discrete fractional-order approach to the Rulkov memristor map. We investigated the impact of all parameters and the fractional order on the model and observed that the system exhibited various behaviors, including tonic firing, periodic firing, and chaotic firing. We also found that the more I tend towards the correct order, the more chaotic modes in the range of parameters. Following this, we coupled the proposed model with a similar one and assessed how the fractional order influences synchronization. Our results demonstrated that the fractional order significantly improves synchronization. The results of this research emphasize that the combination of memristor and discrete neurons provides an effective tool for modeling and estimating biophysical effects in neurons and artificial neural networks.
Collapse
Affiliation(s)
- Mahdieh Ghasemi
- Neural Engineering Laboratory, Department of Biomedical Engineering, University of Neyshabur, Neyshabur 9319774446, Iran
| | - Zeinab Malek Raeissi
- Neural Engineering Laboratory, Department of Biomedical Engineering, University of Neyshabur, Neyshabur 9319774446, Iran
| | - Ali Foroutannia
- Faculty of Science and Technology, University of Canberra, Canberra, ACT 2617, Australia
| | - Masoud Mohammadian
- Faculty of Science and Technology, University of Canberra, Canberra, ACT 2617, Australia
| | - Farshad Shakeriaski
- Faculty of Science and Technology, University of Canberra, Canberra, ACT 2617, Australia
| |
Collapse
|
2
|
Zhang Y, Lv J, Zeng Z. The Framework and Memristive Circuit Design for Multisensory Mutual Associative Memory Networks. IEEE TRANSACTIONS ON CYBERNETICS 2023; 53:7844-7857. [PMID: 37015462 DOI: 10.1109/tcyb.2022.3227161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
In this work, we propose a multisensory mutual associative memory networks framework and memristive circuit to mimic the ability of the biological brain to make associations of information received simultaneously. The circuit inspired by neural mechanisms of associative memory cells mainly consists of three modules: 1) the storage neurons module, which encodes external multimodal information into the firing rate of spikes; 2) the synapse module, which uses the nonvolatility memristor to achieve weight adjustment and associative learning; and 3) the retrieval neuron module, which feeds the retrieval signal output from each sensory pathway to other sensory pathways, so that achieve mutual association and retrieval between multiple modalities. Different from other one-to-one or many-to-one unidirectional associative memory work, this circuit achieves bidirectional association from multiple modalities to multiple modalities. In addition, we simulate the acquisition, extinction, recovery, transmission, and consolidation properties of associative memory. The circuit is applied to cross-modal association of image and audio recognition results, and episodic memory is simulated, where multiple images in a scene are intramodal associated. With power and area analysis, the circuit is validated as hardware-friendly. Further research to extend this work into large-scale associative memory networks, combined with visual-auditory-tactile-gustatory sensory sensors, is promising for application in intelligent robotic platforms to facilitate the development of neuromorphic systems and brain-like intelligence.
Collapse
|
3
|
Xu C, Liao M, Wang C, Sun J, Lin H. Memristive competitive hopfield neural network for image segmentation application. Cogn Neurodyn 2023; 17:1061-1077. [PMID: 37522050 PMCID: PMC10374519 DOI: 10.1007/s11571-022-09891-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Revised: 09/06/2022] [Accepted: 09/18/2022] [Indexed: 11/30/2022] Open
Abstract
Image segmentation implementation provides simplified and effective feature information of image. Neural network algorithms have made significant progress in the application of image segmentation task. However, few studies focus on the implementation of hardware circuits with high-efficiency analog calculations and parallel operations for image segmentation problem. In this paper, a memristor-based competitive Hopfield neural network circuit is proposed to deal with the image segmentation problem. In this circuit, the memristive cross array is applied to store synaptic weights and perform matrix operations. The competition module based on the Winner-take-all mechanism is composed of the competition neurons and the competition control circuit, which simplifies the energy function of the Hopfield neural network and realizes the output function. Operational amplifiers and ABM modules are used to integrate operations and process external input information, respectively. Based on these designs, the circuit can automatically implement iteration and update of data. A series of PSPICE simulations are designed to verify the image segmentation capability of this circuit. Comparative experimental results and analysis show that this circuit has effective improvements both in processing speed and segmentation accuracy compared with other methods. Moreover, the proposed circuit shows good robustness to noise and memristive variation.
Collapse
Affiliation(s)
- Cong Xu
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082 China
| | - Meiling Liao
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082 China
| | - Chunhua Wang
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082 China
| | - Jingru Sun
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082 China
| | - Hairong Lin
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082 China
| |
Collapse
|
4
|
Wang C, Xu C, Sun J, Deng Q. A memristor-based associative memory neural network circuit with emotion effect. Neural Comput Appl 2023. [DOI: 10.1007/s00521-023-08275-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
|
5
|
Zhang X, Wang X, Ge Z, Li Z, Wu M, Borah S. A Novel Memristive Neural Network Circuit and Its Application in Character Recognition. MICROMACHINES 2022; 13:2074. [PMID: 36557373 PMCID: PMC9782966 DOI: 10.3390/mi13122074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 11/16/2022] [Accepted: 11/19/2022] [Indexed: 06/17/2023]
Abstract
The memristor-based neural network configuration is a promising approach to realizing artificial neural networks (ANNs) at the hardware level. The memristors can effectively simulate the strength of synaptic connections between neurons in neural networks due to their diverse significant characteristics such as nonvolatility, nanoscale dimensions, and variable conductance. This work presents a new synaptic circuit based on memristors and Complementary Metal Oxide Semiconductor(CMOS), which can realize the adjustment of positive, negative, and zero synaptic weights using only one control signal. The relationship between synaptic weights and the duration of control signals is also explained in detail. Accordingly, Widrow-Hoff algorithm-based memristive neural network (MNN) circuits are proposed to solve the recognition of three types of character pictures. The functionality of the proposed configurations is verified using SPICE simulation.
Collapse
Affiliation(s)
- Xinrui Zhang
- School of Electronics and Information, Hangzhou Dianzi University, Hangzhou 310018, China
| | - Xiaoyuan Wang
- School of Electronics and Information, Hangzhou Dianzi University, Hangzhou 310018, China
| | - Zhenyu Ge
- School of Electronics and Information, Hangzhou Dianzi University, Hangzhou 310018, China
| | - Zhilong Li
- School of Electronics and Information, Hangzhou Dianzi University, Hangzhou 310018, China
| | - Mingyang Wu
- School of Electronics and Information, Hangzhou Dianzi University, Hangzhou 310018, China
| | - Shekharsuman Borah
- Department of Electronics and Communication Engineering, Indian Institute of Information Technology, Guwahati 781015, India
| |
Collapse
|
6
|
Sun C, Wang C, Xu C. A full-function memristive pavlov associative memory circuit with inter-stimulus interval effect. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.07.044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
|
7
|
Zamri NE, Azhar SA, Mansor MA, Alway A, Kasihmuddin MSM. Weighted Random k Satisfiability for k=1,2 (r2SAT) in Discrete Hopfield Neural Network. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.109312] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
|
8
|
Lu Y, Wang C, Deng Q. Rulkov neural network coupled with discrete memristors. NETWORK (BRISTOL, ENGLAND) 2022; 33:214-232. [PMID: 36200906 DOI: 10.1080/0954898x.2022.2131921] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Revised: 09/15/2022] [Accepted: 09/26/2022] [Indexed: 06/16/2023]
Abstract
The features of memristive-coupled neural networks have been studied extensively in the continuous field. However, the particularities of the discrete domain are rarely mentioned. This paper constructs a discrete memristor with sine-type conductance and applies the discrete memristor to coupling the Rulkov neuron maps for the first time. The properties of the proposed memristive-coupled bi-neuron Rulkov map and multi-neuron Rulkov neural network are probed. In order to better characterize the discrete system, many numerical simulation methods are used. Such as the normalized mean synchronization error, bifurcation diagrams, phase portraits, spatiotemporal patterns and so on. Numerical studies have shown that in discrete memristor-coupled neural networks, both parameters and coupling factors affect the dynamics of the system, resulting in complex and interesting behavioural changes.
Collapse
Affiliation(s)
- Yanmei Lu
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, China
| | - Chunhua Wang
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, China
| | - Quanli Deng
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, China
| |
Collapse
|
9
|
Shen H, Yu F, Kong X, Mokbel AAM, Wang C, Cai S. Dynamics study on the effect of memristive autapse distribution on Hopfield neural network. CHAOS (WOODBURY, N.Y.) 2022; 32:083133. [PMID: 36049931 DOI: 10.1063/5.0099466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Accepted: 07/05/2022] [Indexed: 06/15/2023]
Abstract
As the shortest feedback loop of the nervous system, autapse plays an important role in the mode conversion of neurodynamics. In particular, memristive autapses can not only facilitate the adjustment of the dynamical behavior but also enhance the complexity of the nervous system, in view of the fact that the dynamics of the Hopfield neural network has not been investigated and studied in detail from the perspective of memristive autapse. Based on the traditional Hopfield neural network, this paper uses a locally active memristor to replace the ordinary resistive autapse so as to construct a 2 n-dimensional memristive autaptic Hopfield neural network model. The boundedness of the model is proved by introducing the Lyapunov function and the stability of the equilibrium point is analyzed by deriving the Jacobian matrix. In addition, four scenarios are established on a small Hopfield neural network with three neurons, and the influence of the distribution of memristive autapses on the dynamics of this small Hopfield neural network is described by numerical simulation tools. Finally, the Hopfield neural network model in these four situations is designed and implemented on field-programmable gate array by using the fourth-order Runge-Kutta method, which effectively verifies the numerical simulation results.
Collapse
Affiliation(s)
- Hui Shen
- School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410114, China
| | - Fei Yu
- School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410114, China
| | - Xinxin Kong
- School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410114, China
| | | | - Chunhua Wang
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China
| | - Shuo Cai
- School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410114, China
| |
Collapse
|
10
|
He S, Zhan D, Wang H, Sun K, Peng Y. Discrete Memristor and Discrete Memristive Systems. ENTROPY (BASEL, SWITZERLAND) 2022; 24:786. [PMID: 35741507 PMCID: PMC9222835 DOI: 10.3390/e24060786] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Revised: 05/27/2022] [Accepted: 05/29/2022] [Indexed: 02/01/2023]
Abstract
In this paper, we investigate the mathematical models of discrete memristors based on Caputo fractional difference and G-L fractional difference. Specifically, the integer-order discrete memristor is a special model of those two cases. The "∞"-type hysteresis loop curves are observed when input is the bipolar periodic signal. Meanwhile, numerical analysis results show that the area of hysteresis decreases with the increase of frequency of input signal and the decrease of derivative order. Moreover, the memory effect, characteristics and physical realization of the discrete memristors are discussed, and a discrete memristor with short memory effects is designed. Furthermore, discrete memristive systems are designed by introducing the fractional-order discrete memristor and integer-order discrete memristor to the Sine map. Chaos is found in the systems, and complexity of the systems is controlled by the parameter of the memristor. Finally, FPGA digital circuit implementation is carried out for the integer-order and fractional-order discrete memristor and discrete memristive systems, which shows the potential application value of the discrete memristor in the engineering application field.
Collapse
Affiliation(s)
- Shaobo He
- School of Physics and Electronics, Central South University, Changsha 410083, China; (S.H.); (D.Z.); (K.S.)
| | - Donglin Zhan
- School of Physics and Electronics, Central South University, Changsha 410083, China; (S.H.); (D.Z.); (K.S.)
| | - Huihai Wang
- School of Physics and Electronics, Central South University, Changsha 410083, China; (S.H.); (D.Z.); (K.S.)
| | - Kehui Sun
- School of Physics and Electronics, Central South University, Changsha 410083, China; (S.H.); (D.Z.); (K.S.)
| | - Yuexi Peng
- School of Cyberspace Science, Xiangtan University, Xiangtan 411105, China;
| |
Collapse
|
11
|
Sun J, Jiang M, Zhou Q, Wang C, Sun Y. Memristive Cluster Based Compact High-Density Nonvolatile Memory Design and Application for Image Storage. MICROMACHINES 2022; 13:mi13060844. [PMID: 35744459 PMCID: PMC9229067 DOI: 10.3390/mi13060844] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 05/23/2022] [Accepted: 05/26/2022] [Indexed: 12/10/2022]
Abstract
As a new type of nonvolatile device, the memristor has become one of the most promising technologies for designing a new generation of high-density memory. In this paper, a 4-bit high-density nonvolatile memory based on a memristor is designed and applied to image storage. Firstly, a memristor cluster structure consisting of a transistor and four memristors is designed. Furthermore, the memristor cluster is used as a memory cell in the crossbar array structure to realize the memory design. In addition, when the designed non-volatile memory is applied to gray scale image storage, only two memory cells are needed for the storage of one pixel. Through the Pspice circuit simulation, the results show that compared with the state-of-the-art technology, the memory designed in this paper has better storage density and read–write speed. When it is applied to image storage, it achieves the effect of no distortion and fast storage.
Collapse
Affiliation(s)
- Jingru Sun
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China; (J.S.); (M.J.); (Q.Z.)
| | - Meiqi Jiang
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China; (J.S.); (M.J.); (Q.Z.)
| | - Qi Zhou
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China; (J.S.); (M.J.); (Q.Z.)
| | - Chunhua Wang
- College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China; (J.S.); (M.J.); (Q.Z.)
- Correspondence:
| | - Yichuang Sun
- School of Engineering and Computer Science, University of Hertfordshire, Hatfield AL10 9AB, UK;
| |
Collapse
|
12
|
Yu F, Chen H, Kong X, Yu Q, Cai S, Huang Y, Du S. Dynamic analysis and application in medical digital image watermarking of a new multi-scroll neural network with quartic nonlinear memristor. EUROPEAN PHYSICAL JOURNAL PLUS 2022; 137:434. [PMID: 35411291 PMCID: PMC8988118 DOI: 10.1140/epjp/s13360-022-02652-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Accepted: 03/24/2022] [Indexed: 05/06/2023]
Abstract
Memristor is widely used in various neural bionic models because of its excellent characteristics in biological neural activity simulation. In this paper, a piecewise nonlinear function is used to transform the quartic memristor, which is introduced into the ternary Hopfield neural network (HNN) with self-feedback, and a piecewise quartic memristive chaotic neural network model with multi-scroll is constructed. Through simulation analysis, the number of scroll layers changes with memristor parameters and has significant coexistence of multi-scroll attractors and high initial value sensitivity has been found. Using its excellent unpredictability, a digital watermarking algorithm based on wavelet transform is improved and used in the protection of personal medical data. The results show that it not only improves the confidentiality and convenience, but also ensures its robustness and has good encryption effect.
Collapse
Affiliation(s)
- Fei Yu
- School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, 410114 China
| | - Huifeng Chen
- School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, 410114 China
| | - Xinxin Kong
- School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, 410114 China
| | - Qiulin Yu
- School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, 410114 China
| | - Shuo Cai
- School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, 410114 China
| | - Yuanyuan Huang
- School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, 410114 China
| | - Sichun Du
- College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082 China
| |
Collapse
|