A Classification EM algorithm for clustering and two stochastic versions
Computational Statistics & Data Analysis - Special issue on optimization techniques in statistics
On the separation of signals from neighboring cells in tetrode recordings
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
A comparative investigation on subspace dimension determination
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems
Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems
Computer Methods and Programs in Biomedicine
In vitro neuronal networks: evidence for synaptic plasticity
Neurocomputing
IWINAC'05 Proceedings of the First international conference on Mechanisms, Symbols, and Models Underlying Cognition: interplay between natural and artificial computation - Volume Part I
In quest of the missing neuron: Spike sorting based on dominant-sets clustering
Computer Methods and Programs in Biomedicine
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Deciphering the electrical activity of individual neurons from multi-unit noisy recordings is critical for understanding complex neural systems. A widely used spike sorting algorithm is being evaluated for single-electrode nerve trunk recordings. The algorithm is based on principal component analysis (PCA) for spike feature extraction. In the neuroscience literature it is generally assumed that the use of the first two or most commonly three principal components is sufficient. We estimate the optimum PCA-based feature space by evaluating the algorithm's performance on simulated series of action potentials. A number of modifications are made to the open source nev2lkit software to enable systematic investigation of the parameter space. We introduce a new metric to define clustering error considering over-clustering more favorable than under-clustering as proposed by experimentalists for our data. Both the program patch and the metric are available online. Correlated and white Gaussian noise processes are superimposed to account for biological and artificial jitter in the recordings. We report that the employment of more than three principal components is in general beneficial for all noise cases considered. Finally, we apply our results to experimental data and verify that the sorting process with four principal components is in agreement with a panel of electrophysiology experts.