S.A. Shchanikov

TL;DR: The concept represents an example of a brain-on-chip system belonging to a more general class of memristive neurohybrid systems for a new-generation robotics, artificial intelligence, and personalized medicine, discussed in the framework of the proposed roadmap for the next decade period.

TL;DR: This work investigates 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 maintaining for a long time or even recovering a memory trace of the image without its direct renewal (or rewriting).

TL;DR: This work demonstrates how machine learning techniques, state-of-art nanoelectronics and microfluidics can combine forces to build and test low-power, adaptable biointerfaces that address both signal stability and power efficiency.

TL;DR: This research is aimed to create a general approach to developing methods and algorithms designed for defining and providing memristor-based artificial neural network (ANNM) components operation accuracy in control and communication systems.

TL;DR: This article offers a general approach to developing methods of determining operation tolerances for the parameters' values of memristor-based artificial neural networks (ANNM), as a system that constitutes an united physical and informational object implemented by the hardware and software learning facilities.