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Related Experiment Video

Updated: Jun 19, 2026

A Fully Automated and Highly Versatile System for Testing Multi-cognitive Functions and Recording Neuronal Activities in Rodents
09:13

A Fully Automated and Highly Versatile System for Testing Multi-cognitive Functions and Recording Neuronal Activities in Rodents

Published on: May 3, 2012

Self-calibrating neuromorphic system for adaptive environmental sensing.

Anantharaman Prasad1, S Sofana Reka2, Prakash Venugopal2

  • 1School of Electronics Engineering, Vellore Institute of Technology, Chennai, India.

Frontiers in Artificial Intelligence
|June 18, 2026
PubMed
Summary

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Neuroplasticity01:01

Neuroplasticity

Neuroplasticity reflects the brain's remarkable capacity to adapt and evolve, responding dynamically to learning, experiences, or injury by reorganizing its neural circuitry. This reorganization involves creating new neural connections and refining old ones through a series of biological processes that contribute to the brain's lifelong development and adaptability.

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This study introduces a novel self-calibrating neuromorphic system for adaptive soil moisture sensing. It uses Spiking Neural Networks (SNN) on microcontrollers to reduce energy use and improve accuracy in precision agriculture.

Area of Science:

  • Neuromorphic Computing
  • Internet of Things (IoT)
  • Precision Agriculture

Background:

  • Conventional soil moisture sensors suffer from drift, high energy use, and poor adaptability.
  • These issues lead to inefficient irrigation and unreliable data, especially in remote areas.
  • Frequent recalibration is often impractical for existing sensor technologies.

Purpose of the Study:

  • To develop a novel self-calibrating neuromorphic system for adaptive soil moisture sensing.
  • To address the limitations of conventional sensors in precision agriculture.
  • To enable accurate, real-time environmental monitoring with reduced energy consumption.

Main Methods:

  • Leveraged Spiking Neural Networks (SNN) deployed on a low-power STM32H563ZI microcontroller.
Keywords:
event-driven computationlow-power microcontrollerneuromorphic computingprecision agriculturesoil moisture sensingspiking neural networks

Related Experiment Videos

Last Updated: Jun 19, 2026

A Fully Automated and Highly Versatile System for Testing Multi-cognitive Functions and Recording Neuronal Activities in Rodents
09:13

A Fully Automated and Highly Versatile System for Testing Multi-cognitive Functions and Recording Neuronal Activities in Rodents

Published on: May 3, 2012

  • Implemented autonomous sensor recalibration to mitigate drift.
  • Utilized event-driven computation for reduced energy consumption.

    • Main Results:

    • Achieved a Mean Absolute Error (MAE) of 0.4557 and Root Mean Squared Error (RMSE) of 0.5850.
    • Reduced baseline sensor drift from 5.3% to 1.6% over two months.
    • Outperformed Isolation Forests and Autoencoders in predictive accuracy for soil moisture sensing.

    Conclusions:

    • The proposed SNN-based system offers a scalable, low-power solution for precision agriculture and environmental monitoring.
    • Demonstrated effective deployment of neuromorphic learning on constrained microcontroller hardware.
    • Opens new avenues for resilient, decentralized intelligence in IoT applications.