The decision about the actual number of active neurons is an open issue in spike sorting, with sparsely firing neurons and background activity the most influencing factors. Dominant-sets clustering algorithm is a graph-theoretical algorithmic procedure that successfully addresses this issue. The quality of grouping in the data is evaluated with the estimation of ‘cohesiveness’, i.e. a cluster-quality measure, for each group.
Applications of clustering algorithms in biomedical research are ubiquitous. However, due to the diversity of cluster analysis, the diversity of critical elements underlying different clustering algorithms can be daunting.
Spike sorting algorithms aim at decomposing complex extracellular signals to independent events from single neurons in the electrode’s vicinity. The decision about the actual number of active neurons is still an open issue, with sparsely firing neurons and background activity the most influencing factors. Read the rest of this entry…
SAN 2011 abstract in Neuroscience Letters 2011, vol. 500(Suppl.), e32-33. Read the rest of this entry…
An overview of the spike sorting problem and challenges in IEEE Signal Processing Magazine.
Biometrics Computational Neuroscience CPG LFP Noise Machine Learning Sparse neurons Wavelets neural recordings Information Theory Modelling Dimensionality Reduction Neural Networks Clustering Statistical Analysis Neurophysiology Brain Interfaces Spike Sorting Brain Research
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