Resistive memories are outstanding electron devices that have displayed a large potential in
a plethora of applications such as nonvolatile data storage, neuromorphic computing …

The design of modern convolutional artificial neural networks (ANNs) composed of formal
neurons copies the architecture of the visual cortex. Signals proceed through a hierarchy …

We investigate the constructive role of an external noise signal, in the form of a low-rate
Poisson sequence of pulses supplied to all inputs of a spiking neural network, consisting in …

Here we provide a perspective concept of neurohybrid memristive chip based on the
combination of living neural networks cultivated in microfluidic/microelectrode system, metal …

Building bidirectional biointerfaces is one of the key challenges of modern engineering and
medicine, with dramatic potential impact on bioprosthetics. Two of the major challenges of …

Variability of resistive switching is a key problem for application of memristive devices in
emerging information‐computing systems. Achieving a stable switching between the …

Development of spiking neural networks (SNNs) controlling mobile robots is one of the
modern challenges in computational neuroscience and artificial intelligence. Such networks …

Neuromorphic systems consisting of artificial neurons and memristive synapses could
provide a much better performance and a significantly more energy-efficient approach to the …

The development of artificial synaptic devices is a crucial step for the realization of efficient
bio-inspired neuromorphic computing systems. In this work, the bilayer ZrO 2/ZTO-based …

Given the limitations of von Neumann computing systems, we propose a high-performance
reservoir computing system as an alternative. These systems operate as neural networks …