Quality Metrics to Accompany Spike Sorting of Extracellular Signals

By Daniel N. Hill, Samar Bharat Mehta, David Kleinfeld. Journal of Neuroscience, 2011

436 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/21be02b7ce9d6cc8bf2a582f17c4b53d0e8484ea

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Citations in the corpus

Closed-loop deep brain stimulation is superior in ameliorating parkinsonism. (2011, neutral): “Neuronswere considered for acquisition only if they demonstrated stability of the action potential waveform, discharge rate and a consistent refractory period during spontaneous recordings (Hill et al., 2011).”
SpikeInterface, a unified framework for spike sorting (2019, methodology): “L-ratio: the Mahalanobis distance and χ2 inverse cumulative density function (under the assumption that the spikes in the unit distribute normally in each dimension) are used to find the probability of unit membership for each spike [64]. d′: the classification accuracy between units based on linear”
Sleep-Dependent Reactivation of Ensembles in Motor Cortex Promotes Skill Consolidation (2015, methodology): “, both fast and slow changes in activity intensity) [73].”
Fast and accurate spike sorting of high-channel count probes with KiloSort (2016, background): “When multiple neurons close to the electrode fire action potentials, their spikes must be identified and assigned to the correct cell, based on the features of the recorded waveforms, a process known as spike sorting [1, 2, 3, 4, 5, 6, 7].”
Kilosort: realtime spike-sorting for extracellular electrophysiology with hundreds of channels (2016, neutral): “an = 1 + λμ(2)n bn,t = (Kn ? V )(t) + λμn , (3)”
A spike sorting toolbox for up to thousands of electrodes validated with ground truth recordings in vitro and in vivo (2018, background): “Classical approaches to the spike sorting problem involve extracting some features from each detected spike (Hubel, 1957; Meister et al., 1994; Lewicki, 1994; Einevoll et al., 2012; Quiroga et al., 2004; Hill et al., 2011; Pouzat et al., 2002; Litke et al., 2004; Chung et al., 2017) and clustering t”
Automated long-term recording and analysis of neural activity in behaving animals (2016, methodology): “The quality of single unit isolation was assessed by computing quantitative measures of cluster quality, i.e. cluster isolation distance (Harris et al., 2000), L-ratio (Schmitzer-Torbert et al., 2005), and presence of a clean refractory period (Hill et al., 2011; Lewicki, 1998) (Figure 2D).”
Revealing neuronal function through microelectrode array recordings (2015, neutral): “Available spike sorting packages and frameworks include Wave_Clus (Quian Quiroga et al., 2004), NeuroQuest (Kwon et al., 2012), SigMate (Mahmud et al., 2012), UltraMegaSort (Hill et al., 2011), EToS (Takekawa et al., 2010, 2012), and QSpike tools (Mahmud et al., 2014), among others.”