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Related Experiment Video

Updated: Jan 13, 2026

In Vivo Wireless Optogenetic Control of Skilled Motor Behavior

In Vivo Wireless Optogenetic Control of Skilled Motor Behavior

Published on: November 22, 2021

Sub-picojoule-per-bit volitional neuromorphic devices for precise targeting and tracking.

Yixuan Huang¹, Qihao Sun¹, Fuxing Dai²

¹Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, 610054, PR China.

Nature Communications

|January 9, 2026

Summary

This summary is machine-generated.

This study introduces a novel neuromorphic device with active attention regulation, significantly improving energy efficiency for artificial intelligence hardware. This innovation enhances feature extraction and offers a sustainable path for hyperspectral imaging and computing.

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Area of Science:

Neuromorphic Engineering
Artificial Intelligence Hardware
Energy-Efficient Computing

Background:

Artificial intelligence hardware faces energy consumption challenges.
Current retinomorphic devices have passive attention mechanisms limiting feature extraction flexibility.

Purpose of the Study:

To develop a volitional neuromorphic device with active attention regulation inspired by the human visual system.
To enhance feature extraction capabilities and energy efficiency in AI hardware.

Main Methods:

Leveraging gate-voltage-tunable photoconductance for adjustable differential spectral response.
Employing neural networks for spectral reconstruction accuracy evaluation.
Implementing active volitional attention regulation.

Main Results:

Achieved selective task perception through adjustable spectral response.
Demonstrated a data compression ratio of 1.17%.
Reached an information energy efficiency of 0.625 pJ/bit.

Conclusions:

The novel device advances retinomorphic hardware design.
Presents a sustainable pathway for energy-efficient hyperspectral imaging.
Enables next-generation neuromorphic computing systems.