Correlation analysis of massively parallel spike trains
- Detection and identification of correlation structure
- Higher order correlations, recurring spike patterns
The assembly hypothesis (Hebb, 1949) implies that entities of thought or perception are represented by the coordinated activity of (large) neuronal groups. However, whether or not the dynamic formation of cell assemblies constitutes a fundamental principle of cortical information processing remains a controversial issue of current research. While initially mainly technical problems limited the experimental surge for support of the assembly hypothesis, the recent advent of multi-electrode array reveals fundamental shortcomings of available analysis tools.
Although larger samplings of simultaneous recordings from the cortical tissue are expected to ease the observation of assembly activity, it implies on the other hand an increase in the number of parameters to be estimated. It is usually infeasible to simply extend existing methods (e.g. Unitary Event analysis, Grün et al, 2002a,b) to massively parallel data due to a combinatorial explosion and a lack of reliable statistics if individual spike patterns are considered. Due to limitations in the length of experimental data, in particular in respect to stationarity, all parameters of the full system cannot be estimated. Therefore new data analysis concepts are required. We develop new routes that allow the analysis of massively parallel (hundred or more) spike trains for correlated activities.
Significant synchronous spike patterns of a sub-population of the observed neurons may be identified by distortions of the shape of the distribution of the population spike counts (Staude et al, 2010). Propagation of synchronous activity as realized in the synfire chain model is observed as diagonal entries of high intersection values of sets of active neurons in the intersection method (Schrader et al, 2008 and Gerstein et al, 2012). Frequent itemset mining enables to efficiently count spike patterns, and comparison of the derived pattern spectrum to the one derived from surrogate data (Louis et al, 2010) enables to extract significant spike patterns (Picado-Muino et al, 2013; Torre et al, 2013). Such concepts are further extended for application in a time-resolved manner on non-stationary data.
|-||Torre, Picado-Muiño, Denker, Borgelt, and Grün (2013) Statistical evaluation of synchronous spike patterns extracted by Frequent Item Set Mining. Front Comput Neurosci 7:132 DOI: 10.3389/fncom.2013.00132|
|-||Picado-Muiño, Borgelt, Berger, Gerstein, Grün (2013) Finding neural assemblies with frequent item set mining. Front Neuroinform 7:9 DOI: 10.3389/fninf.2013.00009|
|-||Gerstein, Williams, Diesmann, Grün, Trengove (2012) Detecting synfire chains in parallel spike data. J Neurosci Methods 206 (1): 54-64 DOI: 10.1016/j.jneumeth.2012.02.003|
|-||Shimazaki, Amari, Brown, Grün (2012) State-Space Analysis of Time-Varying Higher-Order Spike Correlation for Multiple Neural Spike Train Data. PLoS Comput Biol 8(3): e1002385 DOI: 10.1371/journal.pcbi.1002385|
|-||Berger, Borgelt, Louis, Morrison, Grün (2010). Efficient Identification of Assembly Neurons within Massively Parallel Spike Trains. Computational Intelligence and Neuroscience Vol. 2010, Article ID 439648, 18 pages. DOI: 10.1155/2010/439648|
|-||Louis, Gerstein, Grün, and Diesmann (2010) Surrogate spike train generation through dithering in operational time, Front Comput Neurosci, 4:127 DOI: 10.3389/fncom.2010.00127|
|-||Staude, Rotter, Grün (2010) CuBIC: cumulant based inference of higher-order correlations in massively parallel spike trains, J Comput Neurosci, 29 (1-2): 327-350, DOI: 10.1007/s10827-009-0195-x|
|-||Grün, Abeles, Diesmann (2008) Impact of higher-order correlations on coincidence distributions of massively parallel data. Lecture Notes in Computer Science, 5286, 96-114|
|-||Schrader, Grün, Diesmann, Gerstein (2008) Detecting synfire chain activity using massively parallel spike train recording. Journal of Neurophysiology 100(4), 2165-2176|
|-||Berger, Warren, Normann, Arieli, Grün (2007) Spatially organized spike correlation in cat visual cortex. Neurocomputing 70, 2112-2116|