Modeling realistic extracellular recordings of neuronal populations for the purpose of evaluating automatic spike-sorting algorithms
Espen Hagen (Dept. Mathematichal Sciences and Technology, UMB), Torbjørn B Ness (Dept. Mathematichal Sciences and Technology, UMB), Amir Khosrowshahi (Dept. Mathematichal Sciences and Technology, UMB), Felix Franke (Bio Engineering Laboratory, ETH Zürich), Gaute T Einevoll (Dept. Mathematichal Sciences and Technology, UMB)
Automated spike sorting methods should ideally be validated against test data with known ground truth, where spiking activity of all neurons in the neuronal population is known. Such details of the underlying activity can only to some extent be acquired experimentally. One remedy is model-based simulation of extracellular recordings, as electrode position-dependent spike shapes (Figure 1a) can conveniently be modeled in a biophysically realistic way using a recently released simulation tool, LFPy (http://compneuro.umb.no/LFPy). LFPy implements a forward modeling scheme for extracellular potentials  in Python integrated with NEURON .
Test data for arbitrary electrode layouts, neuron models, noise content and spike time correlations can be produced at wish, and test data mimicking tetrode and polytrode recordings in cortex and hippocampus with realistic noise features will be presented. Additionally, finite element methods (FEM) are employed to generate test data for cases where significant effects from inhomogeneous extracellular media are present, as in recordings from cell cultures or retinal slice recordings using MEAs.
In order to facilitate usage of benchmark test data for evaluating spike sorting algorithms (Figure 1b), an algorithm evaluation website has been set up on http://www.g-node.org/spike
Supported by the Research Council of Norway (NevroNor, eScience, Notur), NIH (CRCNS) and INCF (G-Node, Norwegian Node).
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