Stochastic Memristive Interface for Neural Signal Processing
Svetlana A. Gerasimova,
Alexey I. Belov,
Dmitry S. Korolev,
Davud V. Guseinov,
Albina V. Lebedeva,
Maria N. Koryazhkina,
Alexey N. Mikhaylov,
Victor B. Kazantsev,
Alexander N. Pisarchik
Affiliations
Svetlana A. Gerasimova
Institute of Biology and Biomedicine, National Research Lobachevsky State University of Nizhny Novgorod, 603950 Nizhny Novgorod, Russia
Alexey I. Belov
Research Institute and Technology, National Research Lobachevsky State University of Nizhny Novgorod, 603950 Nizhny Novgorod, Russia
Dmitry S. Korolev
Research Institute and Technology, National Research Lobachevsky State University of Nizhny Novgorod, 603950 Nizhny Novgorod, Russia
Davud V. Guseinov
Research Institute and Technology, National Research Lobachevsky State University of Nizhny Novgorod, 603950 Nizhny Novgorod, Russia
Albina V. Lebedeva
Institute of Biology and Biomedicine, National Research Lobachevsky State University of Nizhny Novgorod, 603950 Nizhny Novgorod, Russia
Maria N. Koryazhkina
Research and Educational Center “Physics of Solid State Nanostructures”, National Research Lobachevsky State University of Nizhny Novgorod, 603950 Nizhny Novgorod, Russia
Alexey N. Mikhaylov
Research Institute and Technology, National Research Lobachevsky State University of Nizhny Novgorod, 603950 Nizhny Novgorod, Russia
Victor B. Kazantsev
Institute of Biology and Biomedicine, National Research Lobachevsky State University of Nizhny Novgorod, 603950 Nizhny Novgorod, Russia
Alexander N. Pisarchik
Research and Educational Center “Physics of Solid State Nanostructures”, National Research Lobachevsky State University of Nizhny Novgorod, 603950 Nizhny Novgorod, Russia
We propose a memristive interface consisting of two FitzHugh–Nagumo electronic neurons connected via a metal–oxide (Au/Zr/ZrO2(Y)/TiN/Ti) memristive synaptic device. We create a hardware–software complex based on a commercial data acquisition system, which records a signal generated by a presynaptic electronic neuron and transmits it to a postsynaptic neuron through the memristive device. We demonstrate, numerically and experimentally, complex dynamics, including chaos and different types of neural synchronization. The main advantages of our system over similar devices are its simplicity and real-time performance. A change in the amplitude of the presynaptic neurogenerator leads to the potentiation of the memristive device due to the self-tuning of its parameters. This provides an adaptive modulation of the postsynaptic neuron output. The developed memristive interface, due to its stochastic nature, simulates a real synaptic connection, which is very promising for neuroprosthetic applications.
