Maheshwar Pd. Sah

Neural Synaptic Weighting With a Pulse-Based Memristor Circuit

A pulse-based programmable memristor circuit for implementing synaptic weights for artificial neural networks is proposed. In the memristor weighting circuit, both positive and negative multiplications are performed via a charge-dependent Ohms law (). The circuit is composed of five memristors with bridge-like connections and operates like an artificial synapse with pulse-based processing and adjustability. The sign switching pulses, weight setting pulses and synaptic processing pulses are applied through a shared input terminal. Simulations are done with both linear memristor and window-based nonlinear memristor models.

Three Fingerprints of Memristor

This paper illustrates that for a device to be a memristor it should exhibit three characteristic fingerprints: 1) When driven by a bipolar periodic signal the device must exhibit a “pinched hysteresis loop” in the voltage-current plane, assuming the response is periodic. 2) Starting from some critical frequency, the hysteresis lobe area should decrease monotonically as the excitation frequency increases, and 3) the pinched hysteresis loop should shrink to a single-valued function when the frequency tends to infinity. Examples of memristors exhibiting these three fingerprints, along with non-memristors exhibiting only a subset of these fingerprints are also presented. In addition, two different types of pinched hysteresis loops; the transversal (self-crossing) and the non-transversal (tangential) loops exhibited by memristors are also discussed with its identification criterion.

Memristor Bridge Synapses

In this paper, we propose a memristor bridge circuit consisting of four identical memristors that is able to perform zero, negative, and positive synaptic weightings. Together with three additional transistors, the memristor bridge weighting circuit is able to perform synaptic operation for neural cells. It is compact as both weighting and weight programming are performed in a memristor bridge synapse. It is power efficient, since the operation is based on pulsed input signals. Its input terminals are utilized commonly for applying both weight programming and weight processing signals via time sharing. In this paper, features of the memristor bridge synapses are investigated using the TiO memristor model via simulations.

Memristor-based multilevel memory

A method to utilize the memristor as a multilevel memory has been proposed. There are several roadblocks in the practical use of memristors for multilevel memory. A difficulty comes from the nonlinearity in the ¿ vs. q curve which makes it difficult to determine the proper pulse width for desired resistance values. Another one comes from the property of the memristor which integrates any kind of signals including noise that appeared at the memristor and causes memristors to be perturbed from their original values. The proposed method enables the memristor to be used as multilevel memory using a reference resistance array by forcing the memristor to stick at a set of predetermined fixed reference resistance values. We propose the write-in (programming) circuit and the readout/restoration circuit which share the information storing technique using the reference resistance array.

Brains Are Made of Memristors

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.

A Voltage Mode Memristor Bridge Synaptic Circuit with Memristor Emulators

A memristor bridge neural circuit which is able to perform signed synaptic weighting was proposed in our previous study, where the synaptic operation was verified via software simulation of the mathematical model of the HP memristor. This study is an extension of the previous work advancing toward the circuit implementation where the architecture of the memristor bridge synapse is built with memristor emulator circuits. In addition, a simple neural network which performs both synaptic weighting and summation is built by combining memristor emulators-based synapses and differential amplifier circuits. The feasibility of the memristor bridge neural circuit is verified via SPICE simulations.

Composite Behavior of Multiple Memristor Circuits

Composite characteristics of the parallel and serial connections of memristors are investigated. The memristor is one of the fundamental electrical elements, which has recently been successfully built. However, its electrical characteristics are not yet fully understood. When multiple memristors are connected to each other, the composite behavior of the devices becomes complicated and is difficult to predict, due to the polarity dependent nonlinear variation in the memristance of individual memristors. In this work, we investigate the relationships among flux, charge, and memristance of diverse composite memristors, using both linear and nonlinear memristor models, and analyze the characteristics of complex memristor circuits.

A memristor emulator as a replacement of a real memristor

In this paper, we propose a memristor emulator that embraces most of features of a real memristor. The important features that a memristor emulator should include are a sufficiently wide range of memristance, bimodal operability of pulse and continuous signal inputs, a long period of nonvolatility, floating operation, operability with other devices, and the ability to be implemented with off-the-shelf devices. The proposed memristor emulator circuit contains all of these features. Specifically, the small variation range of memristance and the nonfloating operation that limit conventional memristor emulators are improved significantly. It is designed to be built with off-the-shelf electronics devices.

Transient Behaviors of Multiple Memristor Circuits Based on Flux Charge Relationship

Memristor, a new electrical element, can have various configurations of multiple memristors, including serial and parallel connections like previous elements R, L and C. When input voltage/current is supplied to a circuit with multiple memristors, the composite behavior of the memristor circuit exhibits transient states before it enters a steady state. During the transient state period, the behavior is very complex and not predictable due to each memristors different action depending upon its connection polarity and initial state. In this paper, the transient characteristics of a composite memristor are analyzed via the relationships of charge, flux and memristance of each memristor. Also, the behavior of an individual memristor is formulated mathematically and a general computation method of composite memristance for multiple-memristor circuits of diverse configurations is proposed. Various simulations have also been performed to verify the effectiveness of the proposed method for differently configured memristor circuits, in terms of polarities and initial states.

Mutator-Based Meminductor Emulator for Circuit Applications

A mutator-based meminductor emulator whose inductance can be varied by an external current source is proposed. The implementation of a meminductor emulator is very important, since real meminductors are not physically realizable with current technology. Though there is active research on memristor or memcapacitor emulators, no significant contribution for the meminductor emulator has been presented yet. In this paper, a meminductor emulator has been built using the principle of a mutator, in that a memristor with