FPGA-based clustering of multi-channel neural spike trains
Conference: CNNA 2016 - 15th International Workshop on Cellular Nanoscale Networks and their Applications
08/23/2016 - 08/25/2016 at Dresden, Deutschland
Proceedings: CNNA 2016
Pages: 2Language: englishTyp: PDF
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Authors:
Schaeffer, Laszlo; Kincses, Zoltan (Dept. of Technical Informatics, Faculty of Science and Informatics, University of Szeged, Szeged, 6725, Hungary)
Nagy, Zoltan; Szolgay, Peter (Faculty of Information Technology and Bionics, Pazmany Peter Catholic University, 1083 Budapest, Hungary & Cellular Sensory and Optical Wave Computing Laboratory, Hungarian Academy of Sciences, 1111 Budapest, Hungary)
Voeroeshazi, Zsolt (Dept. of Electrical Engineering, University of Pannonia, 8200 Veszprem, Hungary)
Fiath, Richard (Institute of Cognitive Neuroscience and Psychology, Hungarian Academy of Sciences, 1117 Budapest, Hungary)
Ulbert, Istvan (Institute of Cognitive Neuroscience and Psychology, Hungarian Academy of Sciences, 1117 Budapest, Hungary & Faculty of Information Technology and Bionics, Pazmany Peter Catholic University, 1083 Budapest, Hungary)
Abstract:
Electro-physiological recording of neural bioelectrical activity contains local field potentials and unit activities. Unit activity is a mixture of action potentials generated by the neurons. Spike sorting is a method to determine which individual neurons produce the recorded unit activity. High-channel-count neural probes can measure more than a hundred different positions of the brain in parallel, so large amount of high-dimensional data is generated. To increase the computational speed and decrease the processing time Field-Programmable Gate Array (FPGA) architectures can be applied as hardware accelerators. In this paper an FPGA-based implementation of the Expectation-Maximization (EM) algorithm for neural spike clustering is presented.