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@INPROCEEDINGS{Stella:894221,
      author       = {Stella, Alessandra and Bouss, Peter and Palm, Günther and
                      Riehle, Alexa and brochier, thomas and Grün, Sonja},
      title        = {{S}ignificant {S}patio-{T}emporal {S}pike {P}atterns in
                      {M}acaque {M}onkey {M}otor {C}ortex},
      reportid     = {FZJ-2021-03110},
      year         = {2021},
      abstract     = {The cell assembly hypothesis [1] postulates that neurons
                      coordinate their activity through the formation and
                      repetitive co-activation of groups. While the classical
                      theory of neural coding revolves around the concept that
                      information is encoded in firing rates, we assume that
                      assembly activity is expressed by the occurrence of
                      precisely timed spatio-temporal patterns (STPs) of spikes
                      emitted by neurons that are members of the assembly, e.g. a
                      synfire chain. We focus on a method that is capable of
                      detecting significant STPs in parallel spike trains, called
                      SPADE [2,3,4]. SPADE first identifies repeating STPs using
                      Frequent Itemset Mining [5], and then evaluates the detected
                      patterns for significance through comparison to patterns
                      found in surrogate data. Various surrogate techniques can be
                      used to evaluate significance, and their correct choice is
                      crucial to ensure that by-chance patterns are not classified
                      as significant [6]. The final step of the method is the
                      removal of false positive patterns being a by-product of
                      true patterns with background activity. Here we first
                      evaluate how different six types of surrogate techniques
                      affect the results of SPADE, in terms of the general
                      statistics of the generated surrogates, and in terms of the
                      amount of false positives. We conclude that spike-train
                      shifting [7] is the preferable choice for our type of data,
                      which typically show a CV < 1 and have a dead time after the
                      spikes of 1.6/1.2ms induced by the spike sorter (Plexon).
                      Uniform dithering, in contrast, leads to a high false
                      positive rate.In a next step, we evaluate if cell assemblies
                      are active in relation to motor behavior [2]. Therefore, we
                      analyze 20 experimental sessions, each of about 15min
                      recording, consisting of parallel spike data recorded by a
                      10x10 electrode Utah array in the pre-/motor cortex of two
                      macaque monkeys performing a reach-to-grasp task [8, 9]. The
                      monkeys have four possible behavioral conditions of grasping
                      and pulling an object consisting of combinations of two
                      possible grip types (precision or side grip) and two
                      different amounts of force required to pull the object (low
                      or high). We segment each session into 6 periods of 500ms
                      duration and analyze them independently for the occurrence
                      of STPs. Each significant STP is identified by its neuron
                      composition, its number and times of occurrences and the
                      delays between spikes.We find that significant STPs indeed
                      occur in all phases of the behavior. Their size ranges
                      between 2 and 6 neurons, and their maximal spatial extent is
                      60ms. The STPs are specific to the behavioral context, i.e.
                      within the different trial epochs and across conditions
                      (different grip and force type combinations). This suggests
                      that different assemblies are active in the context of
                      different behavior. Within a recording session, we typically
                      find one neuron that is involved in all STPs. The neurons
                      involved in STPs within a session are not clustered on the
                      Utah array, but may be far apart. We further plan to
                      investigate the spatial arrangement of the patterns on the
                      Utah array, to determine whether there are preferred spatial
                      directions of pattern spike sequences, as found in [2] for
                      synchronous patterns. Finally, we plan to investigate
                      whether the grip type can be better decoded on the basis of
                      the type of STPs or by using the firing rates of the
                      neurons. References[1] Hebb, D. O. (1949). John Wiley $\&$
                      Sons[2] Torre et al (2016) J Neurosci 36:8329–8340. DOI:
                      10.1523/JNEUROSCI.4375-15.2016.[3] Quaglio et al. (2017).
                      Front Comp Neurosci, 11:41. DOI: 10.3389/fncom.2017.00041[4]
                      Stella et al. (2019). Biosystems, [5] Pormann et al. (2021).
                      Submitted[6] Stella et al. (2021). In preparation[7] Pipa et
                      al. (2013) [8] Brochier et al. (2018). Scientific data, 5,
                      180055. DOI: 10.1038/sdata.2018.55[9] Riehle et al. (2013)},
      month         = {Jul},
      date          = {2021-07-26},
      organization  = {Neural Coding 2021, Online (Germany),
                       26 Jul 2021 - 29 Jul 2021},
      subtyp        = {After Call},
      cin          = {INM-6 / IAS-6 / INM-10},
      cid          = {I:(DE-Juel1)INM-6-20090406 / I:(DE-Juel1)IAS-6-20130828 /
                      I:(DE-Juel1)INM-10-20170113},
      pnm          = {5232 - Computational Principles (POF4-523) / 571 -
                      Connectivity and Activity (POF3-571) / 5231 -
                      Neuroscientific Foundations (POF4-523) / HAF - Helmholtz
                      Analytics Framework (ZT-I-0003) / HBP SGA2 - Human Brain
                      Project Specific Grant Agreement 2 (785907) / HBP SGA3 -
                      Human Brain Project Specific Grant Agreement 3 (945539)},
      pid          = {G:(DE-HGF)POF4-5232 / G:(DE-HGF)POF3-571 /
                      G:(DE-HGF)POF4-5231 / G:(DE-HGF)ZT-I-0003 /
                      G:(EU-Grant)785907 / G:(EU-Grant)945539},
      typ          = {PUB:(DE-HGF)6},
      url          = {https://juser.fz-juelich.de/record/894221},
}