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Related Experiment Video

Updated: Oct 17, 2025

Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes

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Published on: March 9, 2019

Controllable SiO_x Nanorod Memristive Neuron for Probabilistic Bayesian Inference.

Sanghyeon Choi¹, Gwang Su Kim^1,2, Jehyeon Yang¹

¹KU-KIST Graduate School of Converging Science and Technology, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, Republic of Korea.

Advanced Materials (Deerfield Beach, Fla.)

|October 7, 2021

Summary

We developed a novel SiOₓ nanorod memristive device that functions as a controllable stochastic artificial neuron. This device efficiently processes ambiguous data, mimicking biological neurons for advanced artificial intelligence applications.

Keywords:

artificial neurons memristors nanorods neuromorphic computing probabilistic neural networks silicon oxide

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Published on: May 3, 2012

Area of Science:

Materials Science
Neuroscience
Computer Science

Background:

Modern artificial neural networks struggle with unstructured, ambiguous data due to deterministic computing limitations.
There is a need for elementary physical devices capable of handling uncertainty in data processing.

Purpose of the Study:

To design and fabricate a novel memristive device acting as a controllable stochastic artificial neuron.
To mimic the integrate-and-fire signaling and stochastic dynamics of biological neurons for AI applications.

Main Methods:

Fabrication of a SiOₓ nanorod memristive device using glancing angle deposition (GLAD).
Characterization of the device's stochastic switching behavior, dynamic range, and energy efficiency.
Implementation and testing of probabilistic activation (ProbAct) functions.

Main Results:

The SiOₓ nanorod device exhibits controllable stochasticity, mimicking biological neuron signaling.
Achieved a high dynamic range (≈5.15 × 10¹⁰) and low energy consumption (≈4.06 pJ) for stochastic switching.
Demonstrated probabilistic Bayesian inference for genetic regulatory networks with low errors (≈2.41 × 10⁻²).

Conclusions:

The developed memristive neuron offers a promising solution for processing ambiguous data in artificial intelligence.
The device's stochastic nature and controllable ProbAct functions enable robust probabilistic computations.
Potential applications include self-resting neural operations and biological system modeling.