Abstract
Pattern recognition of neuronal discharges is the electrophysiological basis of the functional characterization of brain processes, so the implementation of a spike-sorting algorithm is an essential step for the analysis of neural codes and neural interactions in a network or brain circuit. We developed an unsupervised automatic computational algorithm for the detection, identification, and classification of the neural action potentials distributed across electrophysiological recordings and for the clustering of these potentials based on the shape, phase, and distribution features, which are extracted from the first-order derivative of the potentials under study. This algorithm was implemented in a customized spike-sorting software called VISSOR (Viability of Integrated Spike Sorting of Real Recordings). The validity and effectiveness of this software were tested by the classification of the action potentials detected in extracellular recordings of the rostro-medial prefrontal cortex (rmPFC) of rabbits during the classical eyelid conditioning.
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Acknowledgments
The authors wish to thank Dr. Alessandro E.P. Villa for provided simulated records. VISSOR was developed by Carmen Rocío Caro Martín (PhD student) under the supervision of Dr. Raudel Sánchez Campusano and Dr. Agnès Gruart i Massó. This study was supported by grants from the Spanish MINECO (BFU2011-29286/BES-2012-052748) and Junta de Andalucía (BIO122, CVI 2487, and P07-CVI-02686) to Agnès Gruart i Massó and José María Delgado García.
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Rocío Caro-Martín, C., Delgado-García, J.M., Gruart, A., Sánchez-Campusano, R. (2018). VISSOR: An Algorithm for the Detection, Identification, and Classification of the Action Potentials Distributed Across Electrophysiological Recordings. In: Delgado-García, J., Pan, X., Sánchez-Campusano, R., Wang, R. (eds) Advances in Cognitive Neurodynamics (VI). Advances in Cognitive Neurodynamics. Springer, Singapore. https://doi.org/10.1007/978-981-10-8854-4_30
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DOI: https://doi.org/10.1007/978-981-10-8854-4_30
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