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Somatosensation

Somatosensation

The somatosensory system relays sensory information from the skin, mucous membranes, limbs, and joints. Somatosensation is more familiarly known as the sense of touch. A typical somatosensory pathway includes three types of long neurons: primary, secondary, and tertiary. Primary neurons have cell bodies located near the spinal cord in groups of neurons called dorsal root ganglia. The sensory neurons of ganglia innervate designated areas of skin called dermatomes.

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

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Crossmodal sensory neurons based on high-performance flexible memristors for human-machine in-sensor computing

Zhiyuan Li^1,2, Zhongshao Li^3,4, Wei Tang¹

¹School of Integrated Circuits, Huazhong University of Science and Technology, Wuhan, China.

Nature Communications

|August 23, 2024

Summary

Researchers developed a flexible bio-inspired sensor that processes multiple signals in real-time for wearable devices. This novel crossmodal in-sensor system enhances human-machine interfaces with efficient, integrated processing capabilities.

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Area of Science:

Materials Science
Neuroscience
Computer Engineering

Background:

Wearable human-machine interfaces require efficient processing of multimodal signals.
Conventional systems face challenges with data conversion and transmission.
Bio-inspired in-sensor computing offers a solution for real-time, energy-efficient processing.

Purpose of the Study:

To develop a bio-inspired crossmodal in-sensor computing system for wearable human-machine interfaces.
To utilize a flexible vanadium dioxide (VO2) memristor for creating a crossmodal spiking sensory neuron (CSSN).
To demonstrate the system's capability for direct perception, encoding, and real-time feedback.

Main Methods:

Fabrication of a flexible VO2 memristor exhibiting high endurance, uniformity, speed, and flexibility.
Implementation of a flexible hardware processing system based on the CSSN.
Construction of a crossmodal in-sensor spiking reservoir computing system using CSSNs.

Main Results:

The VO2 memristor demonstrated excellent performance metrics (endurance >10^12, speed <30 ns, bendable to 1 mm radius).
The CSSN-based system directly encoded pressure and temperature into spikes for real-time haptic feedback.
The spiking reservoir computing system achieved 98.1% accuracy in dynamic object identification with real-time feedback.

Conclusions:

A flexible, bio-inspired crossmodal spiking sensory neuron (CSSN) based on VO2 memristors was successfully developed.
The demonstrated system enables efficient, real-time in-sensor processing of multimodal sensory information for human-machine interfaces.
This approach offers a viable pathway for advanced, flexible, and bio-inspired wearable computing systems.