A Memristor Neural Network Using Synaptic Plasticity and Its Associative Memory

The passivity, low power consumption, memory characteristics and nanometer size of memristors make them the best choice to simulate synapses in artificial neural networks. In this paper, based on the proposed associative memory rules, we design a memristor neural network with plasticity synapses, which can perform analog operations similar to its biological behavior. For the memristor neural network circuit, we also construct a relatively simple Pavlov’s dog experiment simulation circuit, which can effectively reduce the complexity and power consumption of the network. Some advanced neural activities including learning, associative memory and three kinds of forgetting are realized based on the spiking-rate-dependent plasticity rule. Finally, the Simulation program with integrated circuit emphasis is used to simulate the circuit. The simulation results not only prove the correctness of the design, but also help to realize more efficient, simpler and more complex analog circuit of memristor neural network and then help to realize more intelligent, smaller and low-power brain chips.

[1]  M. Alexander,et al.  Principles of Neural Science , 1981 .

[2]  Hyongsuk Kim,et al.  Memristive Imitation of Synaptic Transmission and Plasticity , 2019, IEEE Transactions on Neural Networks and Learning Systems.

[3]  Tadashi Shibata,et al.  A neuron-MOS neural network using self-learning-compatible synapse circuits , 1995, IEEE J. Solid State Circuits.

[4]  D. B. Strukov,et al.  Programmable CMOS/Memristor Threshold Logic , 2013, IEEE Transactions on Nanotechnology.

[5]  Richard A. Chapman,et al.  Neural Learning Circuits Utilizing Nano-Crystalline Silicon Transistors and Memristors , 2012, IEEE Transactions on Neural Networks and Learning Systems.

[6]  Eby G. Friedman,et al.  Memristor-Based Circuit Design for Multilayer Neural Networks , 2018, IEEE Transactions on Circuits and Systems I: Regular Papers.

[7]  Evangelos Eleftheriou,et al.  The Role of Short-Term Plasticity in Neuromorphic Learning: Learning from the Timing of Rate-Varying Events with Fatiguing Spike-Timing-Dependent Plasticity , 2018, IEEE Nanotechnology Magazine.

[8]  Kinam Kim,et al.  A fast, high-endurance and scalable non-volatile memory device made from asymmetric Ta2O(5-x)/TaO(2-x) bilayer structures. , 2011, Nature materials.

[9]  Timothée Masquelier,et al.  Deep Learning in Spiking Neural Networks , 2018, Neural Networks.

[10]  Wei Lu,et al.  Short-term Memory to Long-term Memory Transition in a Nanoscale Memristor , 2022 .

[11]  Wang Guangyi,et al.  Dynamical Behaviors of a TiO2 Memristor Oscillator , 2013 .

[12]  Guangyi Wang,et al.  Extreme multistability in a memristor-based multi-scroll hyper-chaotic system. , 2016, Chaos.

[13]  X. Miao,et al.  Activity-Dependent Synaptic Plasticity of a Chalcogenide Electronic Synapse for Neuromorphic Systems , 2014, Scientific Reports.

[14]  L. Chua Memristor-The missing circuit element , 1971 .

[15]  Xin Wang,et al.  Associate learning and correcting in a memristive neural network , 2013, Neural Computing and Applications.

[16]  Sumio Hosaka,et al.  Associative memory realized by a reconfigurable memristive Hopfield neural network , 2015, Nature Communications.

[17]  D. Stewart,et al.  The missing memristor found , 2008, Nature.

[18]  Pinaki Mazumder,et al.  Learning in Memristor Crossbar-Based Spiking Neural Networks Through Modulation of Weight-Dependent Spike-Timing-Dependent Plasticity , 2018, IEEE Transactions on Nanotechnology.

[19]  Zhigang Zeng,et al.  Implementation of Memristive Neural Network With Full-Function Pavlov Associative Memory , 2016, IEEE Transactions on Circuits and Systems I: Regular Papers.

[20]  M. Ziegler,et al.  An Electronic Version of Pavlov's Dog , 2012 .

[21]  Leon O. Chua,et al.  Brains Are Made of Memristors , 2014, IEEE Circuits and Systems Magazine.

[22]  Massimiliano Di Ventra,et al.  Experimental demonstration of associative memory with memristive neural networks , 2009, Neural Networks.

[23]  Garrett S. Rose,et al.  A bi-memristor synapse with spike-timing-dependent plasticity for on-chip learning in memristive neuromorphic systems , 2018, 2018 19th International Symposium on Quality Electronic Design (ISQED).

[24]  Guangyi Wang,et al.  A Memristive Neural Network Model With Associative Memory for Modeling Affections , 2018, IEEE Access.

[25]  Leon O. Chua,et al.  Everything You Wish to Know About Memristors But Are Afraid to Ask , 2015 .

[26]  Jacques-Olivier Klein,et al.  Supervised learning with organic memristor devices and prospects for neural crossbar arrays , 2015, Proceedings of the 2015 IEEE/ACM International Symposium on Nanoscale Architectures (NANOARCH´15).

[27]  René Schüffny,et al.  VLSI implementation of a conductance-based multi-synapse using switched-capacitor circuits , 2014, 2014 IEEE International Symposium on Circuits and Systems (ISCAS).

[28]  A. F. Adzmi,et al.  Memristor Spice model for designing analog circuit , 2012, 2012 IEEE Student Conference on Research and Development (SCOReD).

[29]  Valeri Mladenov,et al.  A Memristor Model with a Modified Window Function and Activation Thresholds , 2018, 2018 IEEE International Symposium on Circuits and Systems (ISCAS).

[30]  Fabien Alibart,et al.  Pavlov's Dog Associative Learning Demonstrated on Synaptic-Like Organic Transistors , 2013, Neural Computation.

[31]  Fatima Tuz Zohora,et al.  Memristor-CMOS Hybrid Implementation of Leaky Integrate and Fire Neuron Model , 2019, 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE).

[32]  Zhigang Zeng,et al.  Implementation of memristive neural networks with spike-rate-dependent plasticity synapses , 2014, 2014 International Joint Conference on Neural Networks (IJCNN).

[33]  P. I. Fierens,et al.  Mimicking Spike-Timing-Dependent Plasticity with Emulated Memristors , 2019, 2019 Argentine Conference on Electronics (CAE).

[34]  Ennio Mingolla,et al.  From Synapses to Circuitry: Using Memristive Memory to Explore the Electronic Brain , 2011, Computer.

[35]  S. M. Rezaul Hasan,et al.  A VLSI Circuit Emulation of Chemical Synaptic Transmission Dynamics and Postsynaptic DNA Transcription , 2016, IEEE Transactions on Very Large Scale Integration (VLSI) Systems.

[36]  Sijian Yuan,et al.  A Flexible Artificial Synapse for Neuromorphic System , 2018, 2018 IEEE International Conference on Electron Devices and Solid State Circuits (EDSSC).

[37]  Amit Saha,et al.  Memristors act as synapses in neuromorphic architectures , 2016, 2016 International Conference on Communication and Electronics Systems (ICCES).

[38]  C. Gamrat,et al.  An Organic Nanoparticle Transistor Behaving as a Biological Spiking Synapse , 2009, 0907.2540.

[39]  Stephen J. Wolf,et al.  The elusive memristor: properties of basic electrical circuits , 2008, 0807.3994.

[40]  Le Yang,et al.  An Associative-Memory-Based Reconfigurable Memristive Neuromorphic System With Synchronous Weight Training , 2020, IEEE Transactions on Cognitive and Developmental Systems.

[41]  Xiaoping Wang,et al.  A Novel Design for Memristor-Based Logic Switch and Crossbar Circuits , 2015, IEEE Transactions on Circuits and Systems I: Regular Papers.

[42]  Zhigang Zeng,et al.  Adjusting Learning Rate of Memristor-Based Multilayer Neural Networks via Fuzzy Method , 2019, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.