Name
Abstract | ||
|---|---|---|
This paper introduces a new, unsupervised method for sorting and tracking the action potentials of individual neurons in multiunit extracellular recordings. Presuming the data are divided into short, sequential recording intervals, the core of our strategy relies upon an extension of a traditional mixture model approach that incorporates clustering results from the preceding interval in a Bayesian... |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/TBME.2009.2027604 | IEEE Transactions on Biomedical Engineering |
Keywords | Field | DocType |
Bayesian methods,Clustering methods,Neurons,Sorting,Extracellular,Clustering algorithms,Electrodes,Brain modeling,Signal generators,Principal component analysis | Feature vector,Pattern recognition,Naive Bayes classifier,Spike sorting,Computer science,Sorting,Artificial intelligence,Cluster analysis,Artificial neural network,Principal component analysis,Mixture model | Journal |
Volume | Issue | ISSN |
56 | 11 | 0018-9294 |
Citations | PageRank | References |
2 | 0.44 | 8 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michael T. Wolf | 1 | 75 | 9.96 |
| Burdick, J.W. | 2 | 2988 | 516.87 |