Abstract
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). However, depending on the number of spike trains and the length of recording, this method can exhibit long runtimes. 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. 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. 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. The implementation has been evaluated using a traditional workstation based on an Intel Broadwell Xeon E5-1650 v4 as a baseline. 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). Depending on the platform, our implementation is between 27 and 200 times faster than the original implementation. At the same time, the energy consumption was reduced by up to two orders of magnitude.
Highlights
Increasing evidence from neuroscience suggests that in order to understand the principles of information processing in the brain, it is important to study the activity of isolated neurons in response to the environment and behavior, and to investigate the concerted dynamics of neuronal networks as a whole
We primarily focused on accelerating the pattern mining and filtering, only the runtimes of the associated steps are examined in the following
By integrating the pattern filtering function directly into the FP-Growth implementation developed in this work, we dramatically reduced the number of produced patterns that need to be converted to Python
Summary
Increasing evidence from neuroscience suggests that in order to understand the principles of information processing in the brain, it is important to study the activity of isolated neurons in response to the environment and behavior, and to investigate the concerted dynamics of neuronal networks as a whole. Efficient methods to detect and characterize this coordinated activity are in high demand (Quaglio et al, 2018) 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. While the nature and underlying assumptions of these approaches differ, they share the need to scale in runtime performance as the number of observed neurons or the length of the recording increases This holds true, in particular, with an increasing interest to employ such techniques to analyze and validate simulations of large-scale models of neuronal networks (cf., e.g., Trensch et al, 2018; Gutzen et al, 2018) that exceed the volume of available experimental data. A pattern P is regarded as a maximal frequent pattern if it has no frequent superset, i.e., there exists no frequent pattern containing P
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
