Analysis methods for massively parallel neuronal data
Copyright: Emiliano Torre et al. 2016 Plos CB Figure 1
Temporally, precise correlations between simultaneously recorded neurons have been interpreted as signatures of cell assemblies, i.e., groups of neurons that form processing units. Evidence for this hypothesis was found on the level of pairwise correlations in simultaneous recordings of few neurons (e.g. Riehle et al, 1997; Kilavik et al, 2008). Increasing the number of simultaneously recorded neurons increases the chances to detect cell assembly activity due to the larger sample size. Recent technological advances have enabled the recording of 100 or more neurons in parallel. However, these massively parallel spike train data require novel statistical tools to overcome considerable combinatorial and multiple testing issues. Over the last decade we have developed such methods. First approaches were based on population or pairwise measures of synchronization, and later led to methods for the detection of various types of higher-order synchronization and also of spatio-temporal patterns. The latest techniques combine data mining with the assessment of statistical significance.
Publications
Quaglio P., Rostami V., Torre E., Grün S. (2018) Methods for identification of spike patterns in massively parallel spike trains. Biological Cybernetics 112:57-80.
DOI: 10.1007/s00422-018-0755-0Quaglio P., Yegenoglu A., Torre E., Endres DM., Grün S. (2017) Detection and Evaluation of Spatio-Temporal Spike Patterns in Massively Parallel Spike Train Data with SPADE. Frontiers in Computational Neuroscience 11:41.
DOI: 10.3389/fncom.2017.00041.Torre E., Canova C., Denker M., Gerstein G., Helias M., Grün S. (2016) ASSET: Analysis of Sequences of Synchronous Events in Massively Parallel Spike Trains. PLOS Computational Biology 12:e1004939.
DOI: 10.1371/journal.pcbi.1004939.Picado-Muiño D., Borgelt C., Berger D., Gerstein G., Grün S. (2013) Finding neural assemblies with frequent item set mining. Frontiers in Neuroinformatics 7:9.
DOI: 10.3389/fninf.2013.00009Pipa G., Grün S., van Vreeswijk C. (2013) Impact of Spike Train Autostructure on Probability Distribution of Joint Spike Events. Neural Computation 25:1123–1163.
DOI: 10.1162/NECO_a_00432Torre E., Picado-Muiño D., Denker M., Borgelt C., Grün S. (2013) Statistical evaluation of synchronous spike patterns extracted by frequent item set mining. Frontiers in Computational Neuroscience 7:132.
DOI: 10.3389/fncom.2013.00132Gerstein GL., Williams ER., Diesmann M., Grün S., Trengove C. (2012) Detecting synfire chains in parallel spike data. Journal of Neuroscience Methods 206:54–64.
DOI: 10.1016/j.jneumeth.2012.02.003.Shimazaki H., Amari S., Brown EN., Grün S. (2012) State-Space Analysis of Time-Varying Higher-Order Spike Correlation for Multiple Neural Spike Train Data. PLoS Computational Biology 8:e1002385.
DOI: 10.1371/journal.pcbi.1002385.Louis S., Borgelt C., Grün S. (2010) Complexity distribution as a measure for assembly size and temporal precision. Neural Networks 23:705–712.
DOI: 10.1016/j.neunet.2010.05.004.Louis SG., Gerstein GL., Grün S., Diesmann M. (2010) Surrogate spike train generation through dithering in operational time. Frontiers in Computational Neuroscience 4:127.
DOI: 10.3389/fncom.2010.00127.Staude B., Rotter S. (2010) Higher-order correlations in non-stationary parallel spike trains: statistical modeling and inference. Frontiers in Computational Neuroscience 4:16.
DOI: 10.3389/fncom.2010.00016.Staude B., Rotter S., Grün S. (2010) CuBIC: cumulant based inference of higher-order correlations in massively parallel spike trains. Journal of Computational Neuroscience 29:327–350.
DOI: 10.1007/s10827-009-0195-x.Grün S. (2009) Data-driven significance estimation for precise spike correlation. Journal of Neurophysiology 101:1126–1140.
DOI: 10.1152/jn.00093.2008.
Funding
- Deutsche Forschungsgemeinschaft Grant GR 1753/4-1 Priority Program (SPP 1665) (2013-2015)
Deutsche Forschungsgemeinschaft Grant GR 1753/4-2 Priority Program (SPP 1665) (2016-2019)