Revealing neuronal function through microelectrode array recordings

By Marie Engelene J. Obien, Kosmas Deligkaris, T. Bullmann, Douglas J. Bakkum, Urs Frey. Frontiers in Neuroscience, 2015

668 citations recorded by the source corpus.

Microelectrode arrays and microprobes have been widely utilized to measure neuronal activity, both in vitro and in vivo. The key advantage is the capability to record and stimulate neurons at multiple sites simultaneously. However, unlike the single-cell or single-channel resolution of intracellular recording, microelectrodes detect signals from all possible sources around every sensor. Here, we review the current understanding of microelectrode signals and the techniques for analyzing them. We introduce the ongoing advancements in microelectrode technology, with focus on achieving higher resolution and quality of recordings by means of monolithic integration with on-chip circuitry. We show how recent advanced microelectrode array measurement methods facilitate the understanding of single neurons as well as network function.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Electrochemistry

View Paper: https://www.semanticscholar.org/paper/9a5711e67cfa9951567f50aa509b806872cbdd29

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In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

In Electrochemistry

George G. Malliaras

Citations in the corpus

Next-generation probes, particles, and proteins for neural interfacing (2017, background): “2017;3 : e1601649 9 June 2017 silicon complementary metal-oxide-semiconductor (CMOS) technology, this can be readily achieved and has been implemented in in vitro multielectrode arrays (MEAs) (11).”
Recent Advances in Physical Reservoir Computing: A Review (2018, neutral): “13 (Obien, Deligkaris, Bullmann, Bakkum, & Frey, 2015).”
Inverse statistical problems: from the inverse Ising problem to data science (2017, background): “Multi-electrodes were developed, allowing to record multiple simultaneous neural signals independently over long time periods [163, 209].”
Review of Multimodal Data Acquisition Approaches for Brain–Computer Interfaces (2024, unknown): “A significant challenge is the low signal-to-noise ratio (SNR) inherent in the recordings [9].”