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@INPROCEEDINGS{Sridhar:861711,
      author       = {Sridhar, Shashwat and Yegenoglu, Alper and Voges, Nicole
                      and Brochier, Thomas and Riehle, Alexa and Grün, Sonja and
                      Denker, Michael},
      title        = {{SWAN}: {A} tool to track single units across consecutive
                      electrophysiological recordings},
      reportid     = {FZJ-2019-02141},
      year         = {2019},
      abstract     = {Electrophysiological experiments often involve the
                      measurement of extracellular analog voltage signals from
                      brain tissue using implanted microelectrodes. The spiking
                      activity of neurons in the direct vicinity of an electrode
                      is captured as short-lasting voltage deflections. Some of
                      the deflections are action potentials (spikes) of neurons
                      close to the electrode tip [1]. A crucial first step in the
                      analysis of such data is the extraction of spikes and their
                      assortment into clusters corresponding to contributions from
                      different putative single neurons, or in short ‘units’
                      (spike sorting) [1, 2]. The sorting compares the shape of
                      waveforms and the spiking characteristics. With chronically
                      implanted electrodes, spiking activity is recorded for
                      several months over multiple sessions. It is not clear if
                      each electrode detects identical units over the entire
                      course of an experiment. However, this knowledge would help
                      to monitor long-term changes of neural activity during
                      training for a task. In practice, some single units
                      disappear, (re-)appear or progressively change their spike
                      shape, likely due to small movements of the electrode and/or
                      tissue growth. Thus, it becomes a challenge to keep track of
                      identical units across consecutive chronic recordings [3,
                      4]. In the absence of such a tracking of neurons across
                      sessions, detected units in one session are assumed to be
                      independent of those in other sessions of the experiment.
                      They may thus be considered more than once in analyses of
                      several sessions, and bias statistics across sessions.Here
                      we present the Sequential Waveform Analyzer (SWAN) - an
                      open-source tool developed to track individual units across
                      sessions, but also to identify units that are different. It
                      provides a graphical user interface (GUI) to visualize and
                      relate spike-sorted data across multiple sessions. The
                      configurable user interface is divided into several windows
                      (see Figure). In each window, a certain set of features
                      (e.g., mean waveforms, inter-spike interval histograms,
                      principal component analysis of mean waveforms, firing rate
                      profiles) are compared between different units and across
                      multiple sessions. Each set of similar units across sessions
                      is then assigned one global unit ID, represented by one
                      common color across all windows. Thus, we visualize the
                      tracking of a certain unit across consecutive sessions. The
                      assignment of units to global unit IDs is performed by
                      published [3,4] and newly developed automatic algorithms,
                      and can be easily edited by the experimenter in the GUI. We
                      demonstrate the capabilities of SWAN and practically
                      illustrate its application on large-scale recordings from
                      macaque monkey motor cortex [5].Acknowledgements: EU Horizon
                      2020 Framework Programme for Research and Innovation under
                      Specific Grant Agreements No. 720270 and No. 785907 (Human
                      Brain Projects SGA1 and SGA2); DFG Grants DE 2175/2-1 and GR
                      1753/4-2 Priority Program (SPP 1665); International
                      Associated Laboratory (LIA) “Vision for Action” between
                      CNRS and Aix-Marseille Univ, Marseille, France, and Research
                      Centre Juelich, Germany. Christoph Gollan created the
                      initial version of SWAN.References:1. Einevoll, G. T. et al.
                      Cur Opinion Neurobiol 22, 11 (2012).2. Buzsáki, G. Nat
                      Neurosci 7, 446 (2004).3. Dickey, A. S. et al. J Neurophys
                      102, 1331 (2012).4. Fraser, G. W. et al. J Neurophys 107,
                      1970 (2012).5. Brochier, T. et al. Sci Data 5, 180055
                      (2018).},
      month         = {Mar},
      date          = {2019-03-19},
      organization  = {13th Göttingen Meeting of the German
                       Neuroscience Society, Göttingen
                       (Germany), 19 Mar 2019 - 23 Mar 2019},
      subtyp        = {After Call},
      cin          = {INM-6 / IAS-6 / INM-10 / JSC},
      cid          = {I:(DE-Juel1)INM-6-20090406 / I:(DE-Juel1)IAS-6-20130828 /
                      I:(DE-Juel1)INM-10-20170113 / I:(DE-Juel1)JSC-20090406},
      pnm          = {574 - Theory, modelling and simulation (POF3-574) / HBP
                      SGA1 - Human Brain Project Specific Grant Agreement 1
                      (720270) / HBP SGA2 - Human Brain Project Specific Grant
                      Agreement 2 (785907) / DFG project 238707842 - Kausative
                      Mechanismen mesoskopischer Aktivitätsmuster in der
                      auditorischen Kategorien-Diskrimination (238707842) / DFG
                      project 322093511 - Kognitive Leistung als Ergebnis
                      koordinierter neuronaler Aktivität in unreifen
                      präfrontal-hippokampalen Netzwerken (322093511) / 511 -
                      Computational Science and Mathematical Methods (POF3-511)},
      pid          = {G:(DE-HGF)POF3-574 / G:(EU-Grant)720270 /
                      G:(EU-Grant)785907 / G:(GEPRIS)238707842 /
                      G:(GEPRIS)322093511 / G:(DE-HGF)POF3-511},
      typ          = {PUB:(DE-HGF)24},
      url          = {https://juser.fz-juelich.de/record/861711},
}