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.

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Background noise and spike overlap pose problems in contemporary spike-sorting strategies. The (non-linear) isometric feature mapping (ISOMAP) technique reveals the intrinsic data structure and helps with recognising the involved neurons.

To reproduce this tutorial in MATLAB you will need : Read the rest of this entry…

In this tutorial by Dr. Liam Paninski, the Expectation-Maximization (EM) algorithm is discussed and illustrated in a variety of neural examples. Read the rest of this entry…

By Dr. Nikolaos A. Laskaris

The term ‘‘pattern’’, currently, encompasses the notion of a variety of data-forms the machines have to tackle with. Despite the fact that in early days it was used mostly for pictorial information, i.e. 2D-signals, now the same term stands almost for any output from a data-source. For instance, any digital-signal can be considered as an 1D-pattern, a grey-scale image as a 2D-patterm, a video-sequence as a (temporal) multi-dimensional pattern etc. Read the rest of this entry…

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