Memory Analysis for Memristors and Memristive Recurrent Neural Networks

Traditional recurrent neural networks are composed of capacitors, inductors, resistors, and operational amplifiers.Memristive neural networks are constructed by replacing resistors with memristors. This paper focuses on the memory analysis,i.e. the initial value computation, of memristors. Firstly, we present the memory analysis for a single memristor based on memristors’ mathematical models with linear and nonlinear drift.Secondly, we present the memory analysis for two memristors in series and parallel. Thirdly, we point out the difference between traditional neural networks and those that are memristive. Based on the current and voltage relationship of memristors, we use mathematical analysis and SPICE simulations to demonstrate the validity of our methods.

Finite-time Mittag-Leffler synchronization of fractional-order complex-valued memristive neural networks with time delay

Without dividing the complex-valued systems into two real-valued ones, a class of fractional-order complex-valued memristive neural networks(FCVMNNs) with time delay is investigated. Firstly, based on the complex-valued sign function, a novel complex-valued feedback controller is devised to research such systems. Under the framework of Filippov solution, differential inclusion theory and Lyapunov stability theorem, the finite-time Mittag-Leffler synchronization(FTMLS) of FCVMNNs with time delay can be realized. Meanwhile, the upper bound of the synchronization settling time(SST) is less conservative than previous results. In addition, by adjusting controller parameters, the global asymptotic synchronization of FCVMNNs with time delay can also be realized, which improves and enrich some existing results. Lastly,some simulation examples are designed to verify the validity of conclusions.

Synchronization of Stochastic Memristive Neural Networks with Retarded and Advanced Argument

In this paper, we discuss the driving-response synchronization problem for two memristive neural networks with retarded and advanced arguments under the condition of additional noise. The control law is related to the linear time-delay feedback term, and the discontinuous feedback term. Moreover, the random different equation is used to prove the stability of this theory. At the end, the simulation results verify the correctness of the theoretical results.

Memristive network-based genetic algorithm and its application to image edge detection

This paper proposes a mem-computing model of memristive network-based genetic algorithm(MNGA)by building up the relationship between the memristive network(MN)and the genetic algorithm(GA),and a new edge detection algorithm where image pixels are defined as individuals of population.First,the computing model of MNGA is designed to perform mem-computing,which brings new possibility of the hardware implementation of GA.Secondly,MNGA-based edge detection integrating image filter and GA operator deployed by MN is proposed.Finally,simulation results demonstrate that the figure of merit(FoM)of our model is better than the latest memristor-based swarm intelligence.In summary,a new way is found to build proper matching of memristor to GA and aid image edge detection.

FPGA implementation and image encryption application of a new PRNG based on a memristive Hopfield neural network with a special activation gradient

A memristive Hopfield neural network(MHNN)with a special activation gradient is proposed by adding a suitable memristor to the Hopfield neural network(HNN)with a special activation gradient.The MHNN is simulated and dynamically analyzed,and implemented on FPGA.Then,a new pseudo-random number generator(PRNG)based on MHNN is proposed.The post-processing unit of the PRNG is composed of nonlinear post-processor and XOR calculator,which effectively ensures the randomness of PRNG.The experiments in this paper comply with the IEEE 754-1985 high precision32-bit floating point standard and are done on the Vivado design tool using a Xilinx XC7 Z020 CLG400-2 FPGA chip and the Verilog-HDL hardware programming language.The random sequence generated by the PRNG proposed in this paper has passed the NIST SP800-22 test suite and security analysis,proving its randomness and high performance.Finally,an image encryption system based on PRNG is proposed and implemented on FPGA,which proves the value of the image encryption system in the field of data encryption connected to the Internet of Things(Io T).

Mixed H_(∞)/Passive Exponential Synchronization for Delayed Memristive Neural Networks with Switching Event-Triggered Control

This paper is devoted to event-triggered synchronization of delayed memristive neural networks with H∞and passivity performance.The aim is to guarantee the exponential synchronization and mixed H∞and passivity control for memristive neural networks by using event-triggered control.Firstly,a switching system is constructed under the event-triggered control strategy.Then,by adopting a piece-wise Lyapunov functional,a sufficient condition is established for the exponential synchronization and mixed H_(∞)and passivity performance.Moreover,an event-triggered controller design scheme is proposed using matrix decoupling method.Finally,the effectiveness of the designed controller is exemplified by a numerical example.

Almost periodic solutions of memristive multidirectional associative memory neural networks with mixed time delays

Traditional biological neural networks cannot simulate the real situation of the abrupt synaptic connections between neurons while modeling associative memory of human brains.In this paper,the memristive multidirectional associative memory neural networks(MAMNNs)with mixed time-varying delays are investigated in the sense of Filippov solution.First,three steps are given to prove the existence of the almost periodic solution.Two new lemmas are proposed to prove the boundness of the solution and the asymptotical almost periodicity of the solution by constructing Lyapunov function.Second,the uniqueness and global exponential stability of the almost periodic solution of memristive MAMNNs are investigated by a new Lyapunov function.The sufficient conditions guaranteeing the properties of almost periodic solution are derived based on the relevant definitions,Halanay inequality and Lyapunov function.The investigation is an extension of the research on the periodic solution and almost periodic solution of bidirectional associative memory neural networks.Finally,numerical examples with simulations are presented to show the validity of the main results.

Traveling chimera states in locally coupled memristive Hindmarsh-Rose neuronal networks and circuit simulation

Chimera states have been found in many physiology systems as well as nervous systems and may relate to neural information processing. The present work investigates the traveling chimera states in memristive neuronal networks of locally coupled Hindmarsh-Rose neurons, with both excitation and inhibition considered. Various traveling chimera patterns and firing modes are found to exist in the networks. Particularly, for excitatory connection, two kinds of traveling chimera states appear in opposite directions. Besides, a new type of chimera state composed of traveling chimera state and incoherent state is observed, named the semi-traveling chimera state. Multi-head traveling chimera states with several incoherent groups are also observed. For excitatory-inhibitory connection, the network is observed to exhibit an imperfect coherent state under the synergistic effect of strong excitatory and weak inhibitory coupling. Moreover, a firing pattern named mixed-amplitude bursting state is witnessed,consisting of two bursts of different amplitudes in a time sequence. Furthermore, an electric circuit is designed and built on Multisim to realize the above phenomena, suggesting that traveling chimera states could be generated in real circuits. Our findings can deepen the understanding of the electromagnetic induction effect in regulating the dynamics of neuronal networks and may provide useful clues for constructing artificial neural systems.

Memory-centric neuromorphic computing for unstructured data processing

The unstructured data such as visual information,natural language,and human behaviors opens up a wide array of opportunities in the field of artificial intelligence(Al).The memory-centric neuromorphic computing(MNC)has been proposed for the efficient processing of unstructured data,bypassing the von Neumann bottleneck of current computing architecture.The development of MNC would provide massively parallel processing of unstructured data,realizing the cognitive Al in edge and wearable systems.In this review,recent advances in memory-centric neuromorphic devices are discussed in terms of emerging nonvolatile memories,volatile switches,synaptic plasticity,neuronal models,and memristive neural network.