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Towards reliable spike-train recordings from thousands of neurons with multielectrodes

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TLDR
Some of the challenges that must be met to achieve the critical need of realistic model data to be used as ground truth in the validation of spike-sorting algorithms are described.
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This article is published in Current Opinion in Neurobiology.The article was published on 2012-02-01 and is currently open access. It has received 208 citations till now. The article focuses on the topics: Spike train & Spike sorting.

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Citations
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Journal ArticleDOI

Modelling and analysis of local field potentials for studying the function of cortical circuits

TL;DR: Careful mathematical modelling and analysis are needed to take full advantage of the opportunities that this signal offers in understanding signal processing in cortical circuits and, ultimately, the neural basis of perception and cognition.
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Revealing neuronal function through microelectrode array recordings

TL;DR: The ongoing advancements in microelectrode technology are introduced, with focus on achieving higher resolution and quality of recordings by means of monolithic integration with on-chip circuitry.
Posted ContentDOI

Kilosort: realtime spike-sorting for extracellular electrophysiology with hundreds of channels

TL;DR: Kilosort models the recorded voltage as a sum of template waveforms triggered on the spike times, allowing overlapping spikes to be identified and resolved and is an important step towards fully automated spike sorting of multichannel electrode recordings.
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Past, present and future of spike sorting techniques

TL;DR: This work reviews the basic concepts of spike sorting, including the requirements for different applications, together with the problems faced by presently available algorithms, and proposes a roadmap stressing the crucial points to be addressed to support the neuroscientific research of the near future.
References
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Journal ArticleDOI

Phase relationship between hippocampal place units and the EEG theta rhythm

TL;DR: The phase was highly correlated with spatial location and less well correlated with temporal aspects of behavior, such as the time after place field entry, and the characteristics of the phase shift constrain the models that define the construction of place fields.
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Unsupervised spike detection and sorting with wavelets and superparamagnetic clustering

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.
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Large-scale recording of neuronal ensembles

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.
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A review of methods for spike sorting: the detection and classification of neural action potentials.

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.
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Accuracy of Tetrode Spike Separation as Determined by Simultaneous Intracellular and Extracellular Measurements

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%.
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