Abstract
We describe a modification of a growing grid neural net for the purpose of sorting neuronal spike waveforms from extracellular recordings in the central nervous system. We make use of the fact, that real neurons exhibit a refractory period after firing an action potential during which they can not create a new one. This information is utilized to control the growth process of a growing grid, which we use to classify spike waveforms. The new algorithm is an alternative to a standard self-organizing map used in our previously published spike sorting system. Using simulated data, we show that this modification can further improve the accuracy in sorting neuronal spike waveforms.
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Hermle, T., Bogdan, M., Schwarz, C., Rosenstiel, W. (2005). ANN-Based System for Sorting Spike Waveforms Employing Refractory Periods. In: Duch, W., Kacprzyk, J., Oja, E., Zadrożny, S. (eds) Artificial Neural Networks: Biological Inspirations – ICANN 2005. ICANN 2005. Lecture Notes in Computer Science, vol 3696. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550822_20
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DOI: https://doi.org/10.1007/11550822_20
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