Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Inductively Coupled Plasma Atomic Emission Spectroscopy: Instrumentation

Inductively Coupled Plasma Atomic Emission Spectroscopy: Instrumentation

Inductively coupled plasma (ICP) is the common plasma source used in atomic emission spectroscopy (AES), a technique that detects and analyzes various elements in a sample. This method is often called inductively coupled plasma atomic emission spectroscopy (ICP-AES).
There are three main types of inductively coupled plasma atomic emission spectroscopy (ICP-AES) instruments: sequential, simultaneous multichannel, and Fourier transform instruments, with the latter being less commonly used....

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Accurate Ground States of SU(2) Lattice Gauge Theory in 2+1D and 3+1D.

Physical review letters·2026

Same author

Purkinje cell intrinsic activity shapes cerebellar development and function.

Nature communications·2026

Same author

Refined affinity purification protocol for CAMs using minimal mouse brain material.

Journal of neuroscience methods·2026

Same author

Phosphoproteomic Analysis of Cortical Tissue from Mice Lacking Both CaMKIIα and CaMKIIβ Identifies Novel In Vivo Substrates.

ACS chemical neuroscience·2026

Same author

Mapping spatial organization of in vitro neuronal networks using high-content imaging.

Scientific reports·2025

Same author

Investigating the neuronal role of the proteasomal ATPase subunit gene PSMC5 in neurodevelopmental proteasomopathies.

Nature communications·2025

Search research articles

Related Experiment Video

Updated: May 6, 2026

Multi-electrode Array Recordings of Neuronal Avalanches in Organotypic Cultures

Multi-electrode Array Recordings of Neuronal Avalanches in Organotypic Cultures

Published on: August 1, 2011

autoMEA: machine learning-based burst detection for multi-electrode array datasets.

Vinicius Hernandes¹, Anouk M Heuvelmans^2,3, Valentina Gualtieri¹

¹Department of Quantum Nanoscience, Kavli Institute of Nanoscience, Delft University of Technology, Delft, Netherlands.

Frontiers in Neuroscience

|December 20, 2024

Summary

This summary is machine-generated.

autoMEA software uses machine learning for automated burst detection in multi-electrode array (MEA) data. This tool accurately analyzes complex neuronal network activity, outperforming manual methods and aiding neurodevelopmental disorder research.

Keywords:

automated analysis burst detection machine learning multi-electrode array (MEA)neuronal network activity reverberations

More Related Videos

Multi-electrode Array Recordings of Human Epileptic Postoperative Cortical Tissue

Multi-electrode Array Recordings of Human Epileptic Postoperative Cortical Tissue

Published on: October 26, 2014

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Published on: March 8, 2024

Related Experiment Videos

Last Updated: May 6, 2026

Multi-electrode Array Recordings of Neuronal Avalanches in Organotypic Cultures

Multi-electrode Array Recordings of Neuronal Avalanches in Organotypic Cultures

Published on: August 1, 2011

Multi-electrode Array Recordings of Human Epileptic Postoperative Cortical Tissue

Multi-electrode Array Recordings of Human Epileptic Postoperative Cortical Tissue

Published on: October 26, 2014

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Published on: March 8, 2024

Area of Science:

Neuroscience
Computational Neuroscience
Bioinformatics

Background:

Neuronal network activity is fundamental to brain function, including perception, motor control, and cognition.
Understanding neuronal connectivity and activity regulation is key to deciphering brain mechanisms.
Multi-electrode arrays (MEAs) enable high-throughput, real-time monitoring of neuronal network dynamics, but data analysis is complex and time-consuming.

Purpose of the Study:

To introduce autoMEA, a novel software for automated burst detection in MEA datasets using machine learning.
To demonstrate the efficacy of autoMEA in analyzing neuronal network activity from primary hippocampal neurons.
To validate autoMEA's performance in detecting network phenotypes in both wild-type and neurodevelopmental disorder models.

Main Methods:

Development and application of autoMEA, a machine learning-based software for automated burst detection in MEA data.
Experimental validation using primary hippocampal neurons from wild-type mice cultured on 24-well MEA plates.
Benchmarking against manual analysis and application to neuronal networks modeling neurodevelopmental disorders.

Main Results:

autoMEA accurately detects key network characteristics like synchronicity and rhythmicity, comparable to expert manual analysis.
The software successfully identifies complex burst dynamics, such as reverberations, in hippocampal cultures.
autoMEA demonstrates sensitivity in detecting alterations in network synchronicity, rhythmicity, and burst dynamics in models of neurodevelopmental disorders.

Conclusions:

autoMEA provides a reliable, precise, and accurate automated analysis of neural network activity from multi-well MEA recordings.
The software overcomes limitations of traditional semi-automated methods, offering a user-friendly and efficient alternative.
autoMEA is a valuable tool for advancing research in basic neuroscience and neurodevelopmental disorder phenotyping.