This exposition shows that the potassium ion-channels and the sodium ion-channels that are distributed over the entire length of the axons of our neurons are in fact locally-active memristors. In particular, they exhibit all of the fingerprints of memristors, including the characteristic pinched hysteresis Lissajous figures in the voltage-current plane, whose loop areas shrink as the frequency of the periodic excitation signal increases. Moreover, the pinched hysteresis loops for the potassium ion-channel memristor, and the sodium ion-channel memristor, from the Hodgkin-Huxley axon circuit model are unique for each periodic excitation signal. An in-depth circuit-theoretic analysis and characterizations of these two classic biological memristors are presented via their small-signal memristive equivalent circuits, their frequency response, and their Nyquist plots. Just as the Hodgkin-Huxley circuit model has stood the test of time, its constituent potassium ion-channel and sodium ion-channel memristors are destined to be classic examples of locally-active memristors in future textbooks on circuit theory and bio-physics.

Brains Are Made of Memristors.

Electronic synapses have been widely designed to construct neuromorphic systems and artificial neural networks. However, neuromorphic systems are facing enormous challenges, with electronic synapses failing to rival biological synapses, whose connections form the unique learning and memory methods. In the biological learning methods, habituation, the most basic learning method, is the ability of animals to ignore innocuous stimuli to adapt to environmental changes. Mimicking habituation characteristic by synaptic device has important implications for intrducing biological learning into neuromorphic systems. In this paper, a synapse-like device based on memristor is designed to implement the habituation characteristic in Aplysia gill-withdrawal reflex. Firstly, a method for calculating the memristance of HP memristor by flux is improved. Based on this, synaptic device is designed using HP memristor as synapse. The habituation characteristic, including short-term habituation, long-term habituation, dishabituation and frequency-dependent habituation are implemented by the designed device. The realization of more complete habituation characteristic by unipolar pulses broadens the road for the study of neuromorphic systems based on synaptic devices.

Habituation characteristic implementation by synapse-like device based on memristor

Al/PMMA/CS/PMMA/ITO structure devices are fabricated using polymethyl methacrylate (PMMA) and chitosan (CS) as dielectric layer materials. Its electrical characteristics and conductive mechanism are studied using its own physical characteristics to build a memristor true random number generator (TRNG). The generated true random sequence is optimized by a field programmable gate array (FPGA), and the optimized random number sequence is tested by the National Institute of Standards and Technology (NIST) to analyze its randomness. The pipeline structure is introduced to optimize the whole image encryption system, and the optimized random number sequence is used to realize image encryption inside the FPGA. Then, the encrypted image is displayed on a liquid crystal display (LCD) screen to realize random number generation and image encryption of all hardware. Finally, the encrypted image is analyzed.

Full Hardware Image Encryption for Oscillating Memristor Circuits

Memristors have many advantages, such as small size, fast speed, and low power consumption. If the cross array is used as the memristor, a structure similar to the matrix can be obtained, which can store data and do calculation. Based on the characteristics of memristor, the realization of various control functions has a wide range of application prospects and advantages. The memristor matrix supply can be used to achieve relevant control, so that the control becomes simple and efficient. At the same time, memristor has outstanding advantages in arithmetic calculation, neural network, and artificial intelligence. This article introduces memristor and shows the theory needed to apply memristor to the control of robot arm, improve MAC calculation by memristor and the application in neural network. There is no doubt that the research in this paper will contribute to the further exploration of the control method of memristor and lay a solid foundation for its practical potential application theory in the future.

Control method and analysis of potential application based on memristor

As the acquisition, transmission, storage and conversion of images become more efficient, image data are increasing explosively. At the same time, the limitations of conventional computational processing systems based on the Von Neumann architecture continue to emerge, and thus, improving the efficiency of image processing has become a key issue that has bothered scholars working on images for a long time. Memristors with non-volatile, synapse-like, as well as integrated storage-and-computation properties can be used to build intelligent processing systems that are closer to the structure and function of biological brains. They are also of great significance when constructing new intelligent image processing systems with non-Von Neumann architecture and for achieving the integrated storage and computation of image data. Based on this, this paper analyses the mathematical models of memristors and discusses their applications in conventional image processing based on memristive systems as well as image processing based on memristive neural networks, to investigate the potential of memristive systems in image processing. In addition, recent advances and implications of memristive system-based image processing are presented comprehensively, and its development opportunities and challenges in different major areas are explored as well. By establishing a complete spectrum of image processing technologies based on memristive systems, this review attempts to provide a reference for future studies in the field, and it is hoped that scholars can promote its development through interdisciplinary academic exchanges and cooperation.

Memristive System Based Image Processing Technology: A Review and Perspective

With the discovery of the memristor devices, most researchers have focused on the simulation and application of memristive characteristics, especially the nonvolatile storage potential of the memristor. However, the fusion of image storage and operation is seldom discussed. Therefore, this paper focuses on the electrical characterization of silver chalcogenide based on memristor devices, which can be used as a synapse to mimic the STDP and SRDP learning rules in neuromorphic systems. Then on this basis, a improved memristive cross array structure is designed, whose storage and operation are set in a single unit. Furthermore, this improved memristive cross array and image processing are combined with adjusting the memristor resistance by synaptic plasticity to achieve the integration of storage and processing. Finally, the effectiveness of the design is verified by the LTSPICE experiments of the nonlinear grayscale transformation and logic operation of the image. The design breaks the gap between the processing and storage in the Von Neumann architecture, which will be helpful to the development of the parallel computing in the future.

Fusion of Image Storage and Operation Based on Ag-Chalcogenide Memristor with Synaptic Plasticity

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Fusion of Image Storage and Operation Based on Ag-Chalcogenide Memristor with Synaptic Plasticity