Online detection and sorting of extracellularly recorded action potentials in human medial temporal lobe recordings, in vivo.
By Ueli Rutishauser, E. Schuman, Adam N. Mamelak. Journal of Neuroscience Methods, 2006
360 citations recorded by the source corpus.
Understanding the function of complex cortical circuits requires the simultaneous recording of action potentials from many neurons in awake and behaving animals. Practically, this can be achieved by extracellularly recording from multiple brain sites using single wire electrodes. However, in densely packed neural structures such as the human hippocampus, a single electrode can record the activity of multiple neurons. Thus, analytic techniques that differentiate action potentials of different neurons are required. Offline spike sorting approaches are currently used to detect and sort action potentials after finishing the experiment. Because the opportunities to record from the human brain are relatively rare, it is desirable to analyze large numbers of simultaneous recordings quickly using online sorting and detection algorithms. In this way, the experiment can be optimized for the particular response properties of the recorded neurons. Here we present and evaluate a method that is capable of detecting and sorting extracellular single-wire recordings in realtime. We demonstrate the utility of the method by applying it to an extensive data set we acquired from chronically implanted depth electrodes in the hippocampus of human epilepsy patients. This dataset is particularly challenging because it was recorded in a noisy clinical environment. This method will allow the development of "closed-loop" experiments, which immediately adapt the experimental stimuli and/or tasks to the neural response observed.
Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience
View Paper: https://www.semanticscholar.org/paper/0623b04be9bcc8a1329bc7578a526d414fe839d3
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In Cellular and Molecular Neuroscience
In Cognitive Neuroscience
Citations in the corpus
- Past, present and future of spike sorting techniques (2015, methodology): “In line, Rutishauser et al. (2006) proposed to compute templates as the spikes are detected, thus avoiding the use an offline sorter.”
- Selective for Whole Faces in the Human Amygdala (2011, neutral): “The raw signal was filtered with zero-phase lag 300-3kHz bandpass filter and spikes were sorted using a semi-automatic template matching algorithm as described previously [6].”
- Source Separation From Single-Channel Recordings by Combining Empirical-Mode Decomposition and Independent Component Analysis (2010, background): “This spiky type of data are representative for a whole class of artifact-removal problems, such as extraction of the ECG artifact from other physiological signals like EEG, the extraction of interictal spikes in EEG signals, spiky-type seizure detection in neonatal EEG [21], as well as spike detecti”
- Single-Neuron Recordings Research in Children: Ethical Considerations, Feasibility, Technical Aspects, and Scientific Opportunities (2025, unknown): “Modern spike sorting algorithms like MountainSort [95] or OSort [96] accommodate waveform drift and overlapping signals, improving single-unit isolation.”