Abstract
The analysis of electrophysiological recordings often involves visual inspection of time series data to locate specific experiment epochs, mask artifacts, and verify the results of signal processing steps, such as filtering or spike detection. Long-term experiments with continuous data acquisition generate large amounts of data. Rapid browsing through these massive datasets poses a challenge to conventional data plotting software because the plotting time increases proportionately to the increase in the volume of data. This paper presents FTSPlot, which is a visualization concept for large-scale time series datasets using techniques from the field of high performance computer graphics, such as hierarchic level of detail and out-of-core data handling. In a preprocessing step, time series data, event, and interval annotations are converted into an optimized data format, which then permits fast, interactive visualization. The preprocessing step has a computational complexity of ; the visualization itself can be done with a complexity of and is therefore independent of the amount of data. A demonstration prototype has been implemented and benchmarks show that the technology is capable of displaying large amounts of time series data, event, and interval annotations lag-free with ms. The current 64-bit implementation theoretically supports datasets with up to bytes, on the x86_64 architecture currently up to bytes are supported, and benchmarks have been conducted with bytes/1 TiB or double precision samples. The presented software is freely available and can be included as a Qt GUI component in future software projects, providing a standard visualization method for long-term electrophysiological experiments.
Highlights
For understanding long-term neuronal processes, such as growth, plasticity/learning, degeneration and regeneration, it is necessary to monitor neuron activity over long periods of time
The FTSPlot project has shown that by using preprocessed datasets the exploration of time series data, event and interval annotations is possible in constant time—O(1)—and is independent of the amount of data
The necessary preprocessing step to prepare the datasets for visualization has a complexity of O(n|log(n)) and can be conducted unattended in a batch process
Summary
For understanding long-term neuronal processes, such as growth, plasticity/learning, degeneration and regeneration, it is necessary to monitor neuron activity over long periods of time. Numerous single and multichannel extra-cellular electrophysiology techniques offer long-term recording capability. The cultures can be patterned either chemically [16] or physically [17,18,19,20], enabling circuit level observation of neuronal development and plasticity. Continuous long-term recording results in huge datasets that pose a challenge to storage and analysis of the data. High costs of data storage were prohibitive for saving the complete recording in raw data format. Algorithms were developed to conduct filtering, spike detection, and classification during the experiment in real-time, and only short sweeps, time stamps, and selected parameters of the classified spikes were saved to the hard disk [22,23]
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.
