Journal ArticleDOI
Spike sorting based on automatic template reconstruction with a partial solution to the overlapping problem
TLDR
A new method for spike sorting is proposed which partly solves the overlapping problem and the over-fitting problem can be partly avoided.About:
This article is published in Journal of Neuroscience Methods.The article was published on 2004-05-30. It has received 133 citations till now. The article focuses on the topics: Spike train & Spike sorting.read more
Citations
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Journal ArticleDOI
A Fully Automated Approach to Spike Sorting
Jason E. Chung,Jeremy F. Magland,Alex H. Barnett,Vanessa Tolosa,Angela C. Tooker,Kye Y Lee,Kedar G. Shah,Sarah Felix,Loren M. Frank,Loren M. Frank,Leslie Greengard +10 more
TL;DR: An automated clustering approach and associated software package that has the potential to enable reproducible and automated spike sorting of larger scale recordings than is currently possible and has accuracy comparable to or exceeding that achieved using manual or semi-manual techniques.
Journal ArticleDOI
An online spike detection and spike classification algorithm capable of instantaneous resolution of overlapping spikes
TL;DR: A combined spike detection and classification algorithm that operates online, detects and classifies overlapping spikes in real time, and adapts to non-stationary data that outperforms other methods under realistic signal-to-noise ratios and in the presence of overlapping spikes.
Journal ArticleDOI
On the Use of Longitudinal Intrafascicular Peripheral Interfaces for the Control of Cybernetic Hand Prostheses in Amputees
Silvestro Micera,Xavier Navarro,Jacopo Carpaneto,Luca Citi,O. Tonet,Paolo Maria Rossini,Maria Chiara Carrozza,Klaus-Peter Hoffmann,Meritxell Vivó,Ken Yoshida,Paolo Dario +10 more
TL;DR: The potentials and limits of the use of this interface to control robotic devices are presented and the open issues which have to be addressed for a chronic usability of this approach are presented.
Journal ArticleDOI
A Model-Based Spike Sorting Algorithm for Removing Correlation Artifacts in Multi-Neuron Recordings
TL;DR: This work investigates the geometry of failures in traditional sorting algorithms, and document the prevalence of such errors in multi-electrode recordings from primate retina, and develops diagnostic tools that can be used to assess errors in spike sorting in the absence of ground truth.
Journal ArticleDOI
How many neurons can we see with current spike sorting algorithms
TL;DR: This work has shown that sparse neurons are strongly affected by the maximum number of correctly identified neurons in Spike sorting algorithms, so further development of algorithms is needed to address sparse neurons detection.
References
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Journal ArticleDOI
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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Robust clustering methods: a unified view
TL;DR: This paper analyzes several popular robust clustering methods and concludes that they have much in common, establishing a connection between fuzzy set theory and robust statistics, and pointing out the similarities between robust clusters methods and statistical methods.
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Approximate clustering via the mountain method
TL;DR: A simple and effective approach for approximate estimation of the cluster centers on the basis of the concept of a mountain function, based upon a griding on the space, the construction of amountain function from the data and then a destruction of the mountains to obtain the cluster center centers.
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Using noise signature to optimize spike-sorting and to assess neuronal classification quality.
TL;DR: A simple and expandable procedure for classification and validation of extracellular data based on a probabilistic model of data generation using an empirical characterization of the recording noise to optimize the clustering of recorded events into putative neurons.
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Bayesian modeling and classification of neural signals
TL;DR: By defining a probabilistic model of the waveform, the probability of both the form and number of spike shapes can be quantified and this framework is used to obtain an efficient algorithm for the decomposition of arbitrarily complex overlap sequences.