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A Habituation Sensory Nervous System with Memristors
Zuheng Wu1,2, Jikai Lu1,3, Tuo Shi1,4
1Key Laboratory of Microelectronic Devices & Integrated Technology, Institute of Microelectronics, Chinese Academy of Sciences, Beijing, 100029, China.
Advanced Materials (Deerfield Beach, Fla.)
|October 16, 2020
Summary
Researchers developed a Lix SiOy memristor to emulate sensory nervous system habituation in electronic devices. This neuromorphic hardware enables obstacle avoidance in robots, demonstrating memristor potential for bio-inspired learning.
Area of Science:
- Neuromorphic Engineering
- Materials Science
- Computational Neuroscience
Background:
- The sensory nervous system (SNS) integrates external stimuli and organism responses.
- Habituation, a key SNS characteristic, filters repetitive stimuli and aids environmental adaptation.
- Emulating biological learning processes in electronic systems is crucial for advanced AI.
Purpose of the Study:
- To develop a memristor-based device that mimics the habituation process in the SNS.
- To construct a fully memristive SNS with habituation capabilities.
- To demonstrate the application of this neuromorphic system in robot navigation.
Main Methods:
- Fabrication of a Lix SiOy -based memristor (TiN/Lix SiOy /Pt) exhibiting habituation-like temporal responses.
- Integration of the habituation memristor with a leaky integrate-and-fire neuron memristor (Ag/SiO2 :Ag/Au).
- Construction of a habituation spiking neural network (SNN) using the developed SNS.
- Experimental validation of the SNN for obstacle avoidance in robot navigation.
Main Results:
- Successful emulation of habituation using a Lix SiOy memristor.
- Experimental demonstration of a fully memristive SNS capable of habituation.
- Effective application of the habituation SNN in robot obstacle avoidance tasks.
Conclusions:
- Memristor-based emulation of biological habituation is feasible for neuromorphic hardware.
- The developed memristive SNS provides a pathway for bio-inspired learning in electronic systems.
- This approach offers a promising solution for implementing adaptive learning in robotic systems.
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