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The project presents fundamental research on the development of concept and basic components of physical circuit based on optoelectronic elements (neuromorphic generators, memristors, optical fibers). This network can replace some functions of brain's neural networks (such as memory) through adaptive stimulation of neural interfaces at the cellular and cellular networks level. Developing learning system will perform the electrical stimulation of brain areas based on the neural activity recorded and will include electronic devices for recording and processing signals. In addition, this system will include electronic devices for generating electrical stimuli based on hardware models of neurons and memristive devices. The basic idea is to integrate an artificial network with living microcircuits of the brain. The artificial network is capable of adaptively generating electrical output signals and can stimulate a given area of the brain according to the recorded pattern of input signals. We suppose that such a network will be able to reproduce the functions of brain systems, based on the use of learning algorithms. It can be used to expand the functionality of these brain systems and to replace lost functions in pathology or trauma.

We present a novel integration technology for replacing damaged microcircuits in the rat brain with electronic neuronal networks. This technology will allow simulating important brain functions, such as memory, through the adaptive stimulating effects of neural interfaces at cellular and neuronal network levels. The proposed system includes electronic devices to register and process neuronal signals and to adaptively stimulate damaged brain areas according to the online algorithm decoding the registered neuronal activity. The stimulation algorithm is implemented in hardware neural network with memristive connections that allows both network learning and adaptive re-learning.The basic idea is to develop components of hardware neural network with memristive connections for adaptive generation of electrical signals to stimulate particular brain areas. Moreover, the uniqueness of the system is adaptive closed-loop connection between two hippocampal neuronal networks. Such a system is very promising for restoring functionality of damaged areas in the human brain to replace lost functions in patients with brain pathologies or trauma.

We propose a hybrid memristve neuromorphic system for stimulating hippocampus regions bypassing damaged areas. Synaptic plasticity properties of the system allow close-loop adaptive control of neural dynamics. We implement the simplest version of this system which consists of two neuron-like generators coupled by a memristive device, and two fiber-optic channels to transmit signals from the generators directly to living cells to stimulate hippocampus regions. The adaptive stimulation nature of the neural cells is provided by a stochastic response of the self-learning memristive device to the signal of the neuron-like generator. A biological model of impaired functioning of the perforating pathway in the rat hippocampus is implemented by damaging the CA3 region, on the base of the electrophysiological signal changes in normal and pathological conditions. The proposed adaptive stimulation technology demonstrates the possibility of restoring the functionality of the perforating pathway by introducing the neuromorphic system into the hippocampus to replace lost areas.