  This article proposes a general approach to the simulation and design of a
multilayer perceptron (MLP) network on the basis of cross-bar arrays of
metal-oxide memristive devices. The proposed approach uses the ANNM theory,
tolerance theory, simulation methodology and experiment design. The tolerances
analysis and synthesis process is performed for the ANNM hardware
implementation on the basis of two arrays of memristive microdevices in the
original 16x16 cross-bar topology being a component of bidirectional adaptive
neural interface for automatic registration and stimulation of bioelectrical
activity of a living neuronal culture used in robotics control system. The ANNM
is trained for solving a nonlinear classification problem of stable information
characteristics registered in the culture grown on a multi-electrode array.
Memristive devices are fabricated on the basis of a newly engineered
Au/Ta/ZrO2(Y)/Ta2O5/TiN/Ti multilayer structure, which contains self-organized
interface oxide layers, nanocrystals and is specially developed to obtain
robust resistive switching with low variation of parameters. An array of
memristive devices is mounted into a standard metal-ceramic package and can be
easily integrated into the neurointerface circuit. Memristive devices
demonstrate bipolar switching of anionic type between the high-resistance state
and low-resistance state and can be programmed to set the intermediate
resistive states with a desired accuracy. The ANNM tuning, testing and control
are implemented by the FPGA-based control subsystem. All developed models and
algorithms are implemented as Python-based software.
