Fault Tolerance of Memristor-Based Perceptron Network for Neural Interface

One of the most prospective applications of artificial neural networks based on memristors (ANNMs) is the development of more compact, fast, and efficient neural interfaces. This goal is achievable, as the results of a literature survey and our own research show. We have designed the ANNM as part of the neural interface for automatic registration and stimulation of bioelectrical activity of a living neuronal culture, but there are many challenges on the way to its hardware implementation, e.g., related to nonidealities of memristive devices, parasitic elements, the limited capabilities of OpAmps, and convertors. The listed above problems can affect the ANNM quality attributes, such as the operation accuracy, fault tolerance, and reliability, and have fundamental importance to practical operation. In this paper, we focus on the investigation of the ANNM fault tolerance and try to find single points of failure. The obtained information can be used in the ANNM reliability engineering.