Optimizing the Yield of Multi-Unit Activity by Including the Entire Spiking Activity

Frontiers in Neuroscience
Eric DrebitzDetlef Wegener

Abstract

Neurophysiological data acquisition using multi-electrode arrays and/or (semi-) chronic recordings frequently has to deal with low signal-to-noise ratio (SNR) of neuronal responses and potential failure of detecting evoked responses within random background fluctuations. Conventional methods to extract action potentials (spikes) from background noise often apply thresholds to the recorded signal, usually allowing reliable detection of spikes when data exhibit a good SNR, but often failing when SNR is poor. We here investigate a threshold-independent, fast, and automated procedure for analysis of low SNR data, based on fullwave-rectification and low-pass filtering the signal as a measure of the entire spiking activity (ESA). We investigate the sensitivity and reliability of the ESA-signal for detecting evoked responses by applying an automated receptive field (RF) mapping procedure to semi-chronically recorded data from primary visual cortex (V1) of five macaque monkeys. For recording sites with low SNR, the usage of ESA improved the detection rate of RFs by a factor of 2.5 in comparison to MUA-based detection. For recording sites with medium and high SNR, ESA delivered 30% more RFs than MUA. This significantly higher yield of E...Continue Reading

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Citations

Aug 15, 2019·Journal of Neurophysiology·Benjamin FischerDetlef Wegener
Jan 22, 2021·Journal of Neural Engineering·Nur AhmadiChristos-Savvas Bouganis
Sep 26, 2021·Scientific Reports·Nur AhmadiChristos-Savvas Bouganis

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Software Mentioned

MATLAB
- scripts
MUA
ESA

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