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

Ljungquist, Bengt LU ; Petersson, Per LU ; Johansson, Anders J. LU ; Schouenborg, Jens LU and Garwicz, Martin LU (2018) In Neuroinformatics 16(2). p.217-229
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... (More)

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.

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Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Data encoding, Databases, Electrophysiology, Real time
in
Neuroinformatics
volume
16
issue
2
pages
217 - 229
publisher
Humana Press
external identifiers
  • scopus:85045150330
ISSN
1539-2791
DOI
10.1007/s12021-018-9367-z
language
English
LU publication?
yes
id
5dc215db-aca5-4b57-9b86-77bcdf08d3cc
date added to LUP
2018-04-20 13:44:19
date last changed
2019-03-26 16:47:10
@article{5dc215db-aca5-4b57-9b86-77bcdf08d3cc,
  abstract     = {<p>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 (&lt; 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.</p>},
  author       = {Ljungquist, Bengt and Petersson, Per and Johansson, Anders J. and Schouenborg, Jens and Garwicz, Martin},
  issn         = {1539-2791},
  keyword      = {Data encoding,Databases,Electrophysiology,Real time},
  language     = {eng},
  number       = {2},
  pages        = {217--229},
  publisher    = {Humana Press},
  series       = {Neuroinformatics},
  title        = {A Bit-Encoding Based New Data Structure for Time and Memory Efficient Handling of Spike Times in an Electrophysiological Setup},
  url          = {http://dx.doi.org/10.1007/s12021-018-9367-z},
  volume       = {16},
  year         = {2018},
}