Topic
Spike sorting
About: Spike sorting is a research topic. Over the lifetime, 844 publications have been published within this topic receiving 25124 citations.
Papers published on a yearly basis
Papers
TL;DR: A new method for detecting and sorting spikes from multiunit recordings that combines the wave let transform with super paramagnetic clustering, which allows automatic classification of the data without assumptions such as low variance or gaussian distributions is introduced.
Abstract: This study introduces a new method for detecting and sorting spikes from multiunit recordings The method combines the wavelet transform, which localizes distinctive spike features, with superparamagnetic clustering, which allows automatic classification of the data without assumptions such as low variance or gaussian distributions Moreover, an improved method for setting amplitude thresholds for spike detection is proposed We describe several criteria for implementation that render the algorithm unsupervised and fast The algorithm is compared to other conventional methods using several simulated data sets whose characteristics closely resemble those of in vivo recordings For these data sets, we found that the proposed algorithm outperformed conventional methods
2,331 citations
TL;DR: Large-scale recordings from neuronal ensembles now offer the opportunity to test competing theoretical frameworks and require further development of the neuron–electrode interface, automated and efficient spike-sorting algorithms for effective isolation and identification of single neurons, and new mathematical insights for the analysis of network properties.
Abstract: How does the brain orchestrate perceptions, thoughts and actions from the spiking activity of its neurons? Early single-neuron recording research treated spike pattern variability as noise that needed to be averaged out to reveal the brain's representation of invariant input. Another view is that variability of spikes is centrally coordinated and that this brain-generated ensemble pattern in cortical structures is itself a potential source of cognition. Large-scale recordings from neuronal ensembles now offer the opportunity to test these competing theoretical frameworks. Currently, wire and micro-machined silicon electrode arrays can record from large numbers of neurons and monitor local neural circuits at work. Achieving the full potential of massively parallel neuronal recordings, however, will require further development of the neuron–electrode interface, automated and efficient spike-sorting algorithms for effective isolation and identification of single neurons, and new mathematical insights for the analysis of network properties.
1,992 citations
TL;DR: This article reviews algorithms and methods for detecting and classifying action potentials, a problem commonly referred to as spike sorting and discusses the advantages and limitations of each and the applicability of these methods for different types of experimental demands.
Abstract: The detection of neural spike activity is a technical challenge that is a prerequisite for studying many types of brain function. Measuring the activity of individual neurons accurately can be difficult due to large amounts of background noise and the difficulty in distinguishing the action potentials of one neuron from those of others in the local area. This article reviews algorithms and methods for detecting and classifying action potentials, a problem commonly referred to as spike sorting. The article first discusses the challenges of measuring neural activity and the basic issues of signal detection and classification. It reviews and illustrates algorithms and techniques that have been applied to many of the problems in spike sorting and discusses the advantages and limitations of each and the applicability of these methods for different types of experimental demands. The article is written both for the physiologist wanting to use simple methods that will improve experimental yield and minimize the selection biases of traditional techniques and for those who want to apply or extend more sophisticated algorithms to meet new experimental challenges.
1,473 citations
TL;DR: It is hypothesized that automatic spike-sorting algorithms have the potential to significantly lower error rates, and implementation of a semi-automatic classification system confirms this suggestion, reducing errors close to the estimated optimum, in the range 0-8%.
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 ...
1,325 citations
TL;DR: Tetrode recording currently provides the best and most reliable method for the isolation of multiple single units in the neocortex using a single probe.
847 citations
Related Topics (5)
Performance Metrics
| Year | Papers |
|---|---|
| 2025 | 23 |
| 2024 | 25 |
| 2023 | 21 |
| 2022 | 57 |
| 2021 | 44 |
| 2020 | 49 |