Tracking neurons recorded from tetrodes across time

Emondi, Alfred A; Rebrik, Sergei P.; Kurgansky, A. V.; Miller, Kenneth D.

doi:10.1016/j.jneumeth.2003.12.022

Cited by 45 publications

(39 citation statements)

References 6 publications

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“…8 Note that the PCA feature space is recalculated at every interval to find the best PC features for that dataset. Thus, the spike waveforms from T k −1 must be projected to the PCA space of T k , and then, the prior clusters' statistics are calculated in this space.…”

Section: B Prior On Cluster Locationmentioning

confidence: 99%

“…During these recordings, the spike waveforms often evolve over time due to electrode drift and other causes, even without active electrode movement [6]. Dividing these long recordings into short time intervals for analysis can improve spike sorting results, as the data are apt to be effectively stationary over these brief intervals [7], [8].…”

Section: Introductionmentioning

confidence: 99%

“…Due to our interest in autonomous electrode positioning algorithms, we require an unsupervised method that may be used in real time 2 ; thus, noncausal methods such as [7] and [8] are not applicable. Additionally, the short, successive intervals inherent to our problem can complicate the clustering operation because fewer data points (spikes) are available to process, thus exacerbating issues of volatility and variations due to noise.…”

Section: Introductionmentioning

confidence: 99%

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A Bayesian Clustering Method for Tracking Neural Signals Over Successive Intervals

Wolf

Burdick

2009

IEEE Trans. Biomed. Eng.

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Abstract-This paper introduces a new, unsupervised method for sorting and tracking the action potentials of individual neurons in multiunit extracellular recordings. Presuming the data are divided into short, sequential recording intervals, the core of our strategy relies upon an extension of a traditional mixture model approach that incorporates clustering results from the preceding interval in a Bayesian manner, while still allowing for signal nonstationarity and changing numbers of recorded neurons. As a natural byproduct of the sorting method, current and prior signal clusters can be matched over time in order to track persisting neurons. We also develop techniques to use prior data to appropriately seed the clustering algorithm and select the model class. We present results in a principal components space; however, the algorithm may be applied in any feature space where the distribution of a neuron's spikes may be modeled as Gaussian. Applications of this signal classification method to recordings from macaque parietal cortex show that it provides significantly more consistent clustering and tracking results than traditional methods based on expectation-maximization optimization of mixture models. This consistent tracking ability is crucial for intended applications of the method.

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Section: B Prior On Cluster Locationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Bayesian Clustering Method for Tracking Neural Signals Over Successive Intervals

Wolf

Burdick

2009

IEEE Trans. Biomed. Eng.

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“…The clusters' statistics are used to evaluate the probability of such matches as follows, a method bearing strong resemblance to the technique in [19].…”

Section: Tracking Clusters Across Stepsmentioning

confidence: 99%

“…The non-stationarity of spike waveforms, primarily due to electrode drift, is a commonly cited culprit for neuron tracking difficulties (or "clustering difficulties" for long T ) [10], [18], [19]. However, when the recording application involves repeated sampling over time (or when a long T is split into many intervals of length ∆t for analysis), our experience has shown that the inconsistency of conventional clustering method's output is a crucial issue, as each sampling step's clusters must be matched to those in the preceeding and subsequent step(s).…”

Section: Introductionmentioning

confidence: 99%

Spike Clustering and Neuron Tracking over Successive Time Windows

Wolf

Burdick

2007

2007 3rd International IEEE/EMBS Conference on Neural Engineering

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Abstract-This paper introduces a new methodology for tracking signals from individual neurons over time in multiunit extracellular recordings. The core of our strategy relies upon an extension of a traditional mixture model approach, with parameter optimization via expectation-maximimization (EM), to incorporate clustering results from the preceding time period in a Bayesian manner. EM initialization is also achieved by utilizing these prior clustering results. After clustering, we match the current and prior clusters to track persisting neurons. Applications of this spike sorting method to recordings from macaque parietal cortex show that it provides significantly more consistent clustering and tracking results.

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