Analysis, classification, and coding of multielectrode spike trains with hidden Markov models

Radons, Günter; Becker, Jacqueline; Dulfer, Bernd G; Krüger, Jürgen

doi:10.1007/bf00239623

Cited by 83 publications

(57 citation statements)

References 15 publications

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“…In particular our focus is on the process of determining from which neuron, out of an unknown number of neurons, each action potential arose based on action potential waveshape alone. In doing so we overlook the problem of spike detection and instead refer readers to Radons et al (1994) for treatment of that topic. Additionally we ignore the problem of detecting overlapping spikes (deconvolving coincident action potentials) here referring readers to the work of Fee et al (1996) and Görür et al (2004).…”

Section: Introductionmentioning

confidence: 99%

A nonparametric Bayesian alternative to spike sorting

Wood

Black

2008

Journal of Neuroscience Methods

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a b s t r a c tThe analysis of extra-cellular neural recordings typically begins with careful spike sorting and all analysis of the data then rests on the correctness of the resulting spike trains. In many situations this is unproblematic as experimental and spike sorting procedures often focus on well isolated units. There is evidence in the literature, however, that errors in spike sorting can occur even with carefully collected and selected data. Additionally, chronically implanted electrodes and arrays with fixed electrodes cannot be easily adjusted to provide well isolated units. In these situations, multiple units may be recorded and the assignment of waveforms to units may be ambiguous. At the same time, analysis of such data may be both scientifically important and clinically relevant. In this paper we address this issue using a novel probabilistic model that accounts for several important sources of uncertainty and error in spike sorting. In lieu of sorting neural data to produce a single best spike train, we estimate a probabilistic model of spike trains given the observed data. We show how such a distribution over spike sortings can support standard neuroscientific questions while providing a representation of uncertainty in the analysis. As a representative illustration of the approach, we analyzed primary motor cortical tuning with respect to hand movement in data recorded with a chronic multi-electrode array in non-human primates. We found that the probabilistic analysis generally agrees with human sorters but suggests the presence of tuned units not detected by humans.

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Section: Introductionmentioning

confidence: 99%

A nonparametric Bayesian alternative to spike sorting

Wood

Black

2008

Journal of Neuroscience Methods

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“…The central idea of this dynamical systems approach [36,40,[72][73][74][75][76][77] is that the responses of different neurons reflect different views of a common dynamical process, whose effective dimensionality is much smaller than the number of neurons involved. While the neural trajectory might evolve differently on different trials, the commonalities among trajectories provide clues about properties that do not vary from trial-to-trial.…”

Section: Statistical Methods For Overcoming/exploiting Trial-to-trialmentioning

confidence: 99%

“…The dynamical systems approach has been previously applied to simultaneous spike trains in cat [73] and monkey [72] visual cortex. In those studies, the neural responses were driven on a moment-by-moment basis by visual stimuli.…”

Section: Statistical Methods For Overcoming/exploiting Trial-to-trialmentioning

confidence: 99%

Techniques for extracting single-trial activity patterns from large-scale neural recordings

Churchland

Sahani

et al. 2007

Current Opinion in Neurobiology

142

115

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SummaryLarge, chronically-implanted arrays of microelectrodes are an increasingly common tool for recording from primate cortex, and can provide extracellular recordings from many (order of 100) neurons. While the desire for cortically-based motor prostheses has helped drive their development, such arrays also offer great potential to advance basic neuroscience research. Here we discuss the utility of array recording for the study of neural dynamics. Neural activity often has dynamics beyond that driven directly by the stimulus. While governed by those dynamics, neural responses may nevertheless unfold differently for nominally identical trials, rendering many traditional analysis methods ineffective. We review recent studies -some employing simultaneous recording, some not -indicating that such variability is indeed present both during movement generation, and during the preceding premotor computations. In such cases, large-scale simultaneous recordings have the potential to provide an unprecedented view of neural dynamics at the level of single trials. However, this enterprise will depend not only on techniques for simultaneous recording, but also on the use and further development of analysis techniques that can appropriately reduce the dimensionality of the data, and allow visualization of single-trial neural behavior.

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“…Using hidden Markov models to capture the temporal dynamics with discrete states has also proven useful for neural data analysis [10], [11], [12] A combination of statespace dynamics and a discrete state was shown to capture population responses [13]. In any case, as with purely unsupervised models, there is no guarantee that a state-space model representation, either continuous or discrete, is useful in distinguishing different conditions.…”

Section: A Learning Low-dimensional Representationsmentioning

confidence: 99%

Information-theoretic metric learning: 2-D linear projections of neural data for visualization

Brockmeier

Giraldo

Emigh

et al. 2013

2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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Abstract-Intracortical neural recordings are typically highdimensional due to many electrodes, channels, or units and high sampling rates, making it very difficult to visually inspect differences among responses to various conditions. By representing the neural response in a low-dimensional space, a researcher can visually evaluate the amount of information the response carries about the conditions. We consider a linear projection to 2-D space that also parametrizes a metric between neural responses. The projection, and corresponding metric, should preserve class-relevant information pertaining to different behavior or stimuli. We find the projection as a solution to the information-theoretic optimization problem of maximizing the information between the projected data and the class labels. The method is applied to two datasets using different types of neural responses: motor cortex neuronal firing rates of a macaque during a center-out reaching task, and local field potentials in the somatosensory cortex of a rat during tactile stimulation of the forepaw. In both cases, projected data points preserve the natural topology of targets or peripheral touch sites. Using the learned metric on the neural responses increases the nearest-neighbor classification rate versus the original data; thus, the metric is tuned to distinguish among the conditions.

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Analysis, classification, and coding of multielectrode spike trains with hidden Markov models

Cited by 83 publications

References 15 publications

A nonparametric Bayesian alternative to spike sorting

A nonparametric Bayesian alternative to spike sorting

Techniques for extracting single-trial activity patterns from large-scale neural recordings

Information-theoretic metric learning: 2-D linear projections of neural data for visualization

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