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Related Concept Videos

Neuronal Communication01:28

Neuronal Communication

Neurons, the fundamental units of the brain and nervous system, communicate through complex electrochemical signals that underpin all cognitive and bodily functions. This communication is primarily facilitated by a process involving the generation and propagation of an action potential along the axon of the neuron. When the internal electrical charge of a neuron surpasses a certain threshold, an action potential is triggered. This rapid change in voltage travels swiftly along the axon to the...

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Related Experiment Video

Updated: May 9, 2026

Implantation and Control of Wireless, Battery-free Systems for Peripheral Nerve Interfacing
07:13

Implantation and Control of Wireless, Battery-free Systems for Peripheral Nerve Interfacing

Published on: October 20, 2021

NeuralWISP: A Wirelessly Powered Neural Interface With 1-m Range.

D J Yeager, J Holleman, R Prasad

    IEEE Transactions on Biomedical Circuits and Systems
    |July 16, 2013
    PubMed
    Summary
    This summary is machine-generated.

    NeuralWISP is a novel wireless neural interface powered by radio-frequency energy. This low-power device processes neural signals and transmits data wirelessly up to 1 meter.

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    Last Updated: May 9, 2026

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    A Wireless, Bidirectional Interface for In Vivo Recording and Stimulation of Neural Activity in Freely Behaving Rats
    10:41

    A Wireless, Bidirectional Interface for In Vivo Recording and Stimulation of Neural Activity in Freely Behaving Rats

    Published on: November 7, 2017

    Area of Science:

    • Neuroscience
    • Biomedical Engineering
    • Electrical Engineering

    Background:

    • Wireless neural interfaces are crucial for advanced brain-computer interfaces.
    • Existing systems often face limitations in power consumption, range, and data processing.

    Purpose of the Study:

    • To introduce NeuralWISP, a wireless neural interface powered by far-field radio-frequency (RF) energy.
    • To demonstrate its compatibility with commercial RF identification readers and its operational range.
    • To highlight its low-power consumption and efficient signal processing capabilities.

    Main Methods:

    • Development of a custom low-noise, low-power amplifier integrated circuit for neural signal processing.
    • Integration of an analog spike detection circuit to minimize computational and communication bandwidth demands.
    • Utilizing harvested RF energy for power, operating at 1.8-V supply with an average consumption of 20 μA.

    Main Results:

    • NeuralWISP operates wirelessly from far-field RF energy up to a range of 1 meter.
    • The system successfully processes neural signals using an integrated amplifier and analog spike detection.
    • Achieved a low average power consumption of 20 μA, enabling extended operation.

    Conclusions:

    • NeuralWISP offers a promising solution for wireless neural monitoring with minimal power requirements.
    • Its compatibility with commercial RF readers and extended range facilitate practical applications in neuroscience and beyond.
    • The system's design reduces computational load and communication bandwidth, paving the way for more sophisticated neural interfaces.