Parylene-MoO crossbar memristors as a volatile reservoir and non-volatile readout: a homogeneous reservoir computing system.

From the very beginning, the emulation of biological principles has been the primary avenue for the development of energy-efficient artificial intelligence systems. Reservoir computing, which has a solid biological basis, is particularly appealing due to its simplicity and efficiency. So-called memristors, resistive switching elements with complex dynamics, have proved beneficial for replicating both principal parts of a reservoir computing system.

Reliable Memristive Synapses Based on Parylene-MoO Nanocomposites for Neuromorphic Applications.

Memristive devices, known for their nonvolatile resistive switching, are promising components for next-generation neuromorphic computing systems, which mimic the brain's neural architecture. Specifically, these devices are well-suited for functioning as artificial synapses due to their analogue tunability and low energy consumption. However, the improvement of their performance and reliability remains a pressing challenge.

Memristor-based spiking neural network with online reinforcement learning.

Neural networks implemented in memristor-based hardware can provide fast and efficient in-memory computation, but traditional learning methods such as error back-propagation are hardly feasible in it. Spiking neural networks (SNNs) are highly promising in this regard, as their weights can be changed locally in a self-organized manner without the demand for high-precision changes calculated with the use of information almost from the entire network. This problem is rather relevant for solving control tasks with neural-network reinforcement learning methods, as those are highly sensitive to any source of stochasticity in a model initialization, training, or decision-making procedure.

Dynamics of the Proton Exchange Process in Benzoic Acid Interacting with Lithium Niobate Crystals.

This paper provides a numerical analysis of the behavior of the ion boundary layer formed during proton exchange. Thermal dissociation of benzoic acid melt molecules leads to the formation of benzoate ions and hydrogen ions. The latter can be absorbed by a lithium niobate wafer, with subsequent diffusion of lithium ions in the acid.