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Such methods need to deal with challenges related to the highly non-stationary spike time series and the statistical complexity of high-dimensional activity patterns, since the number of possible patterns exponentially increases with the number of observed neurons. Efficient methods to detect and characterize this coordinated activity are in high demand ( Quaglio et al., 2018). Signatures of assemblies in the observed dynamics are groups of synchronously active neurons (e.g., Harris, 2005), or spatio-temporal sequences of neuronal activation. The cell assembly hypothesis ( Hebb, 1949) postulates that information is represented by interactions within groups of neurons. With the rapid advancement of electrophysiological recording techniques in the recent decades, scientists are now able to monitor the spiking activity of individual nerve cells in large neuronal populations, enabling the investigation of the dynamics of hundreds of neurons recorded in parallel (e.g., Jun et al., 2017 Brochier et al., 2018 Steinmetz et al., 2018 Juavinett et al., 2019 Chen et al., 2020). Increasing evidence from neuroscience suggests that in order to understand the principles of information processing in the brain, it is important to study not only the activity of isolated neurons in response to the environment and behavior, but also to investigate the concerted dynamics of neuronal networks as a whole. At the same time, the energy consumption was reduced by up to two orders of magnitude. Depending on the platform, our implementation is between 27 and 200 times faster than the original implementation. Furthermore, the heterogeneous microserver platform RECS|Box has been used for evaluating the implementation on two HiSilicon Hi1616 (Kunpeng 916), an Intel Coffee Lake-ER Xeon E-2276ME, an Intel Broadwell Xeon D-D1577, and three NVIDIA Tegra devices (Jetson AGX Xavier, Jetson Xavier NX, and Jetson TX2).
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The implementation has been evaluated using a traditional workstation based on an Intel Broadwell Xeon E5-1650 v4 as a baseline.
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Our version allows for parallel and distributed execution, and due to the improvements made, an execution on heterogeneous and low-power embedded devices is now also possible. Therefore, in this paper, we propose a customized FP-Growth implementation tailored to the requirements of SPADE, which significantly accelerates pattern mining and result filtering. Based on a realistic benchmark data set, we identified that the combination of pattern mining (using the FP-Growth algorithm) and the result filtering account for 85–90% of the method's total runtime. However, depending on the number of spike trains and the length of recording, this method can exhibit long runtimes. The SPADE (spatio-temporal Spike PAttern Detection and Evaluation) method was developed to find reoccurring spatio-temporal patterns in neuronal spike activity (parallel spike trains). 3RWTH Aachen University, Aachen, Germany.2Institute of Neuroscience and Medicine (INM-6) and Institute for Advanced Simulation (IAS-6) and JARA-Institute Brain Structure-Function Relationships (INM-10), Jülich Research Center, Jülich, Germany.1Cognitronics and Sensor Systems, CITEC, Bielefeld University, Bielefeld, Germany.Florian Porrmann 1 *, Sarah Pilz 1, Alessandra Stella 2,3, Alexander Kleinjohann 2,3, Michael Denker 2, Jens Hagemeyer 1 and Ulrich Rückert 1