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Stochastic Memristive Interface for Neural Signal Processing.

Svetlana A Gerasimova1,2, Alexey I Belov2, Dmitry S Korolev2

  • 1Institute of Biology and Biomedicine, National Research Lobachevsky State University of Nizhny Novgorod, 603950 Nizhny Novgorod, Russia.

Sensors (Basel, Switzerland)
|August 28, 2021
PubMed
Summary

We developed a simple, real-time memristive interface connecting two electronic neurons. This device mimics real synaptic connections, showing adaptive modulation for potential neuroprosthetic applications.

Keywords:
FitzHugh–Nagumo neuronmemristive deviceneuromorphic circuitneuron-like oscillatorstochastic dynamicssynchronization

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Area of Science:

  • Neuroscience
  • Materials Science
  • Electronic Engineering

Background:

  • Electronic neurons offer a platform for studying neural dynamics.
  • Memristive devices show promise for emulating synaptic plasticity.
  • Complex neural dynamics, such as chaos and synchronization, are crucial for brain function.

Purpose of the Study:

  • To propose and validate a novel memristive interface for connecting electronic neurons.
  • To investigate the complex dynamics and adaptive capabilities of the memristive synaptic device.
  • To assess the potential of the system for neuroprosthetic applications.

Main Methods:

  • Constructed a hardware-software complex using a metal-oxide memristive device (Au/Zr/ZrO2(Y)/TiN/Ti) to link two FitzHugh-Nagumo electronic neurons.
  • Utilized a commercial data acquisition system for signal transmission between neurons via the memristor.
  • Performed numerical simulations and experimental validations to analyze system dynamics and synchronization.

Main Results:

  • Demonstrated complex neural dynamics, including chaos and various types of neural synchronization, in the coupled electronic neuron system.
  • Observed adaptive modulation of the postsynaptic neuron output through memristive potentiation triggered by presynaptic signal amplitude changes.
  • Showcased the system's simplicity, real-time performance, and stochastic nature, simulating a biological synapse.

Conclusions:

  • The developed memristive interface provides a simple and efficient method for connecting electronic neurons.
  • The system exhibits adaptive plasticity and complex dynamics, mimicking biological synapses.
  • This memristive interface holds significant promise for advancing neuroprosthetic technologies.