Robotics & Machine Learning Daily News2024,Issue(Feb.16) :17-18.DOI:10.3389/fninf.2024.1303993

New Machine Learning Research from Arizona State University Discussed (Discovering optimal features for neuron-type identification from extracellular recordings)

Robotics & Machine Learning Daily News2024,Issue(Feb.16) :17-18.DOI:10.3389/fninf.2024.1303993

New Machine Learning Research from Arizona State University Discussed (Discovering optimal features for neuron-type identification from extracellular recordings)

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Abstract

Fresh data on artificial intelligence are presented in a new report. According to news reporting originating from Tempe, Arizona, by NewsRx correspondents, research stated, "Advancements in multichannel recordings of single-unit activity (SUA) in vivo present an opportunity to discover novel features of spatially-varying extracellularly-recorded action potentials (EAPs) that are useful for identifying neuron-types. Traditional approaches to classifying neuron-types often rely on computing EAP waveform features based on conventions of single-channel recordings and thus inherit their limitations." Funders for this research include National Institutes of Health.

Key words

Arizona State University/Tempe/Arizona/United States/North and Central America/Cyborgs/Emerging Technologies/Machine Learning

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出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

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