3,476 research outputs found
Heuristic Spike Sorting Tuner (HSST), a framework to determine optimal parameter selection for a generic spike sorting algorithm
Extracellular microelectrodes frequently record neural activity from more than one neuron in the vicinity of the electrode. The process of labeling each recorded spike waveform with the identity of its source neuron is called spike sorting and is often approached from an abstracted statistical perspective. However, these approaches do not consider neurophysiological realities and may ignore important features that could improve the accuracy of these methods. Further, standard algorithms typically require selection of at least one free parameter, which can have significant effects on the quality of the output. We describe a Heuristic Spike Sorting Tuner (HSST) that determines the optimal choice of the free parameters for a given spike sorting algorithm based on the neurophysiological qualification of unit isolation and signal discrimination. A set of heuristic metrics are used to score the output of a spike sorting algorithm over a range of free parameters resulting in optimal sorting quality. We demonstrate that these metrics can be used to tune parameters in several spike sorting algorithms. The HSST algorithm shows robustness to variations in signal to noise ratio, number and relative size of units per channel. Moreover, the HSST algorithm is computationally efficient, operates unsupervised, and is parallelizable for batch processing
Estimating the number of neurons in multi-neuronal spike trains
A common way of studying the relationship between neural activity and
behavior is through the analysis of neuronal spike trains that are recorded
using one or more electrodes implanted in the brain. Each spike train typically
contains spikes generated by multiple neurons. A natural question that arises
is "what is the number of neurons generating the spike train?"; This
article proposes a method-of-moments technique for estimating . This
technique estimates the noise nonparametrically using data from the silent
region of the spike train and it applies to isolated spikes with a possibly
small, but nonnegligible, presence of overlapping spikes. Conditions are
established in which the resulting estimator for is shown to be strongly
consistent. To gauge its finite sample performance, the technique is applied to
simulated spike trains as well as to actual neuronal spike train data.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS371 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Estimating the number of neurons in multi-neuronal spike trains
A common way of studying the relationship between neural activity and
behavior is through the analysis of neuronal spike trains that are recorded
using one or more electrodes implanted in the brain. Each spike train typically
contains spikes generated by multiple neurons. A natural question that arises
is "what is the number of neurons generating the spike train?"; This
article proposes a method-of-moments technique for estimating . This
technique estimates the noise nonparametrically using data from the silent
region of the spike train and it applies to isolated spikes with a possibly
small, but nonnegligible, presence of overlapping spikes. Conditions are
established in which the resulting estimator for is shown to be strongly
consistent. To gauge its finite sample performance, the technique is applied to
simulated spike trains as well as to actual neuronal spike train data.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS371 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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