Accuracy of Tetrode Spike Separation as Determined by Simultaneous Intracellular and Extracellular Measurements

Abstract

Simultaneous recording from large numbers of neurons is a prerequisite for understanding their cooperative behavior. Various recording techniques and spike separation methods are being used toward this goal. However, the error rates involved in spike separation have not yet been quantified. We studied the separation reliability of "tetrode" (4-wire electrode)-recorded spikes by monitoring simultaneously from the same cell intracellularly with a glass pipette and extracellularly with a tetrode. With manual spike sorting, we found a trade-off between Type I and Type II errors, with errors typically ranging from 0 to 30% depending on the amplitude and firing pattern of the cell, the similarity of the waveshapes of neighboring neurons, and the experience of the operator. Performance using only a single wire was markedly lower, indicating the advantages of multiple-site monitoring techniques over single-wire recordings. For tetrode recordings, error rates were increased by burst activity and during periods of cellular synchrony. The lowest possible separation error rates were estimated by a search for the best ellipsoidal cluster shape. Human operator performance was significantly below the estimated optimum. Investigation of error distributions indicated that suboptimal performance was caused by inability of the operators to mark cluster boundaries accurately in a high-dimensional feature space. We therefore hypothesized that automatic spike-sorting algorithms have the potential to significantly lower error rates. Implementation of a semi-automatic classification system confirms this suggestion, reducing errors close to the estimated optimum, in the range 0-8%.

Automated spike sorting using density grid contour clustering and subtractive waveform decomposition

“…Wood et al (2004) have reported an average of 23% false positives and 30% false negatives for manual spike sorting of synthetic datasets performed by experts using commercial software. Harris et al (2000) have obtained similar estimates using simultaneous intracellular recording to determine the true firing pattern of neurons. Practical automated spike sorting applications must strike a balance between maximizing accuracy and maintaining low computational complexity.…”

Hybrid intracerebral probe with integrated bare LED chips for optogenetic studies

“…Spike sorting was performed with the Klustakwik software suite (Harris et al 2000). Putative GABAergic fast-spiking neurons (expressing ChR2) were identified on the basis of their action potential (AP) waveforms and auto-correlograms.…”

Hierarchical Adaptive Means (HAM) clustering for hardware-efficient, unsupervised and real-time spike sorting

“…There are several methods available (Xu and Wunsch, 2010) for this that include but are not limited to: k-means clustering (Chan et al, 2008), Bayesian clustering (Dai et al, 2008), valley detection (Kim and McNames, 2007;Mahmud et al, 2012), superparamagnetic clustering (Quiroga et al, 2004), expectation maximization (Harris et al, 2000;Pouzat et al, 2002) and artificial neural networks (ANN). k-means is one of the most frequent utilised in offline sorting (Gibson et al, 2012).…”

“…2b), we used PCA features (Harris et al, 2000). This is because this dataset has a high level of environmental noise and separation via FSDE features deteriorates with high levels of white noise.…”

“…ber of highly accurate software-based algorithms, such as WaveClus (Quiroga et al, 2004), Osort (Rutishauser et al, 2006) and KlustaKwik (Harris et al, 2000).…”