A Bit-Encoding Based New Data Structure for Time and Memory Efficient Handling of Spike Times in an Electrophysiological Setup

Research output: Contribution to journalArticle

Abstract

Recent neuroscientific and technical developments of brain machine interfaces have put increasing demands on neuroinformatic databases and data handling software, especially when managing data in real time from large numbers of neurons. Extrapolating these developments we here set out to construct a scalable software architecture that would enable near-future massive parallel recording, organization and analysis of neurophysiological data on a standard computer. To this end we combined, for the first time in the present context, bit-encoding of spike data with a specific communication format for real time transfer and storage of neuronal data, synchronized by a common time base across all unit sources. We demonstrate that our architecture can simultaneously handle data from more than one million neurons and provide, in real time (< 25 ms), feedback based on analysis of previously recorded data. In addition to managing recordings from very large numbers of neurons in real time, it also has the capacity to handle the extensive periods of recording time necessary in certain scientific and clinical applications. Furthermore, the bit-encoding proposed has the additional advantage of allowing an extremely fast analysis of spatiotemporal spike patterns in a large number of neurons. Thus, we conclude that this architecture is well suited to support current and near-future Brain Machine Interface requirements.

Details

Authors
Organisations
External organisations
  • Umeå University
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Computer Science

Keywords

  • Data encoding, Databases, Electrophysiology, Real time
Original languageEnglish
Pages (from-to)217-229
JournalNeuroinformatics
Volume16
Issue number2
Early online date2018 Mar 5
Publication statusPublished - 2018 Apr
Publication categoryResearch
Peer-reviewedYes

Related research output

Ljungquist, B., 2018, Lund: Lund University, Faculty of Medicine. 56 p.

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