The training of neural networks involves numerous operations on the weight matrix. If neural
networks are implemented in hardware, all weights will be updated in parallel. However …

Brain-inspired computing paradigms have led to substantial advances in the automation of
visual and linguistic tasks by emulating the distributed information processing of biological …

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 …

Here we provide the state-of-the-art of bioelectronic interfacing between biological neuronal
systems and artificial components, focusing the attention on the potentiality offered by …

We report a prospective approach to neural network modeling based on implementation of
metal-oxide heterostructures with non-volatile memory behavior and multilevel resistive …

Memristive technologies promise to have a large impact on modern electronics, particularly
in the areas of reconfigurable computing and artificial intelligence (AI) hardware. Meanwhile …

Bioelectronics aims to design electronic devices which can be fully integrated within tissues
to monitor or stimulate specific cell functions. The main challenge is the engineering of the …

The multistate capabilities as well as the intrinsic integrating properties of memristors deem
them suitable candidates for the realization of novel neuromorphic applications. To date …

Volatility in metal-oxide resistive random access memory (RRAM) families has mostly been
treated as an unwanted side-effect, although recently there are trends to interpret such …