A simple method to simultaneously detect and identify spikes from raw extracellular recordings
Frontiers in Neuroscience, ISSN: 1662-453X, Vol: 9, Issue: DEC, Page: 452
2015
- 9Citations
- 76Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations9
- Citation Indexes9
- CrossRef4
- Captures76
- Readers76
- 76
Article Description
The ability to track when and which neurons fire in the vicinity of an electrode, in an efficient and reliable manner can revolutionize the neuroscience field. The current bottleneck lies in spike sorting algorithms; existing methods for detecting and discriminating the activity of multiple neurons rely on inefficient, multi-step processing of extracellular recordings. In this work, we show that a single-step processing of raw (unfiltered) extracellular signals is sufficient for both the detection and identification of active neurons, thus greatly simplifying and optimizing the spike sorting approach. The efficiency and reliability of our method is demonstrated in both real and simulated data.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84954567580&origin=inward; http://dx.doi.org/10.3389/fnins.2015.00452; http://www.ncbi.nlm.nih.gov/pubmed/26696813; http://journal.frontiersin.org/Article/10.3389/fnins.2015.00452/abstract; https://dx.doi.org/10.3389/fnins.2015.00452; https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2015.00452/full
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