In mid-2009, research was showing that memristors and memcapacitors are similar to synapses and can be made into neural networks
There is an updated version of the paper Experimental demonstration of associative memory with memristive neural networks
Abstract—Synapses are essential elements for computation and information storage in both real and artificial neural systems. An artificial synapse needs to remember its past dynamical history, store a continuous set of states, and be ”plastic” according to the pre-synaptic and post-synaptic neuronal activity. Here we show that all this can be accomplished by a memory-resistor (memristor for short). In particular, by using simple and inexpensive offthe- shelf components we have built a memristor emulator which realizes all required synaptic properties. Most importantly, we have demonstrated experimentally the formation of associative memory in a simple neural network consisting of three electronic neurons connected by two memristor-emulator synapses. This experimental demonstration opens up new possibilities in the understanding of neural processes using memory devices, an important step forward to reproduce complex learning, adaptive and spontaneous behavior with electronic neural networks.
A recently demonstrated resistor with memory (memristor for short) based on TiO2 thin films offers a promising realization of a synapse whose size can be as small as 30×30×2 nm^3. Memristors belong to the larger class of memory-circuit elements (which includes also memcapacitors and meminductors), namely circuit elements whose response depends on the whole dynamical history of the system. Memristors can be realized in many ways, ranging from oxide thin films to spin memristive systems. In the present paper, we describe a flexible platform allowing for simulation of different types of memristors, and experimentally show that a memristor could indeed function as a synapse. We have developed electronic versions of neurons and synapses whose behavior can be easily tuned to the functions found in biological neural cells. Of equal importance, the electronic neurons and synapses were fabricated using inexpensive off-the-shelf electronic components resulting in few dollars cost for each element, and therefore can be realized in any electronic laboratory. Clearly, we do not expect that with such elements one can scale up the resulting electronic neural networks to the actual brain density. However, due to their simplicity reasonably complex neural networks can be constructed from the two elemental blocks developed here and we thus expect several functionalities could be realized and studied.
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Practical approach to programmable analog circuits with memristors by Yuriy V. Pershin and Massimiliano Di Ventra
We suggest an approach to use memristors (resistors with memory) in programmable analog circuits. Our idea consists in a circuit design in which low voltages are applied to memristors during their operation as analog circuit elements and high voltages are used to program the memristor’s states. This way, as it was demonstrated in recent experiments, the state of memristors does not essentially change during analog mode operation. As an example of our approach, we have built several programmable analog circuits demonstrating memristor-based programming of threshold, gain and frequency.
Applications for analog memristor circuits
* Programmable threshold comparator
* Programmable gain amplifier
* Programmable switching thresholds Schmitt trigger
* Programmable frequency relaxation oscillator
Concerning reproducibility of memristive behavior, experiments with TiO2 thin films demonstrate a significant amount of noise in hysteresis curves. Possibly, the resistance change effect in colossal magnetoresistive thin films is more suitable for analog-mode memristor applications
Memristive circuits simulate memcapacitors and meminductors by Yuriy V. Pershin and Massimiliano Di Ventra
Abstract—We suggest electronic circuits with memristors (resistors with memory) that operate as memcapacitors (capacitors with memory) and meminductors (inductors with memory). Using a memristor emulator, the suggested circuits have been built and their operation has been demonstrated, showing a useful and interesting connection between the three memory elements.
We have demonstrated that simple circuits with memristors can exhibit both memcapacitive and meminductive behavior. Memcapacitor and meminductor emulators
have been designed and built using the previously suggested memristor emulator since solid-state memristors are not available yet. These emulators can be created from inexpensive off-the-shelf components, and as such they provide powerful tools to understand the different functionalities of these newly suggested memory elements without the need of expensive material fabrication facilities. We thus expect they will be of use in diverse areas ranging from non-volatile memory applications to neuromorphic circuits.
Solid State Memcapacitor
Solid-state memcapacitor by J. Martinez, M. Di Ventra, Yu. V. Pershin (7 page pdf)
We suggest a possible realization of a solid-state memory capacitive (memcapacitive) system. Our approach relies on the slow polarization rate of a medium between plates of a regular capacitor. To achieve this goal, we consider a multi-layer structure embedded in a capacitor. The multi-layer structure is formed by metallic layers separated by an insulator so that non-linear electronic transport (tunneling) between the layers can occur. The suggested memcapacitor shows hysteretic charge-voltage and capacitance-voltage curves, and both negative and diverging capacitance within certain ranges of the field. This proposal can be easily realized experimentally, and indicates the possibility of information storage in memcapacitive devices.
Ionic Memcapacitive Effects in Nanopores by Matt Krems, Yuriy V. Pershin, Massimiliano Di Ventra
Using molecular dynamics simulations, we show that, when subject to a periodic external electric field, a nanopore in ionic solution acts as a capacitor with memory (memcapacitor) at various frequencies and strengths of the electric field. Most importantly, the hysteresis loop of this memcapacitor shows both negative and diverging capacitance as a function of the voltage. The origin of this effect stems from the slow polarizability of the ionic solution due to the finite mobility of ions in water. We develop a microscopic quantitative model which captures the main features we observe in the simulations and suggest experimental tests of our predictions. These effects may be important in both DNA sequencing proposals using nanopores and possibly in the dynamics of action potentials in neurons.
We have shown, using molecular dynamics simulations, that nanopores act as memcapacitors, namely capacitors with memory. The latter is due to the finite mobility of ions in water and hence the slow polarizability of ions compared to the pore. This phenomenon may potentially play a role in nanopore DNA sequencing proposals, especially those based on acelectric fields, as well as in other nanopore sensing applications. Moreover, the effect of the charge buildup on the nanopore surface may influence DNA translocation and its structure in proximity to the pore. Finally, due to the ubiquitous nature of nanopores in biological processes, these results may be relevant to specific ion dynamics when time-dependent fields are of importance, such as in the action potential formation and propagation during neuronal activity.
Brian Wang is a Futurist Thought Leader and a popular Science blogger with 1 million readers per month. His blog Nextbigfuture.com is ranked #1 Science News Blog. It covers many disruptive technology and trends including Space, Robotics, Artificial Intelligence, Medicine, Anti-aging Biotechnology, and Nanotechnology.
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