Abstract
We recently proposed frequent itemset mining (FIM) as a method to perform an optimized search for patterns of synchronous spikes (item sets) in massively parallel spike trains. This search outputs the occurrence count (support) of individual patterns that are not trivially explained by the counts of any superset (closed frequent item sets). The number of patterns found by FIM makes direct statistical tests infeasible due to severe multiple testing. To overcome this issue, we proposed to test the significance not of individual patterns, but instead of their signatures, defined as the pairs of pattern size z and support c. Here, we derive in detail a statistical test for the significance of the signatures under the null hypothesis of full independence (pattern spectrum filtering, PSF) by means of surrogate data. As a result, injected spike patterns that mimic assembly activity are well detected, yielding a low false negative rate. However, this approach is prone to additionally classify patterns resulting from chance overlap of real assembly activity and background spiking as significant. These patterns represent false positives with respect to the null hypothesis of having one assembly of given signature embedded in otherwise independent spiking activity. We propose the additional method of pattern set reduction (PSR) to remove these false positives by conditional filtering. By employing stochastic simulations of parallel spike trains with correlated activity in form of injected spike synchrony in subsets of the neurons, we demonstrate for a range of parameter settings that the analysis scheme composed of FIM, PSF and PSR allows to reliably detect active assemblies in massively parallel spike trains.
Highlights
The cortex is comprised of a highly interconnected network of neurons and one may speculate that information processing in the brain may only be understood on the basis of the concerted activity of the neuronal population. Hebb (1949) suggested that neurons coordinate their activities by organizing in functional groups, termed cell assemblies
Synchronous spike input to receiving neurons is known to be more effective in generating output spikes (Abeles, 1982; König et al, 1996), which leads to the hypothesis that temporal coordination of spiking activity or correlational processing is the defining expression of an active cell assembly (Singer et al, 1997; Harris, 2005)
In Picado-Muiño et al (2013) we presented the basic approach and relevant statistics to employ frequent item set mining (FIM) to identify significant patterns of spike synchrony in massively parallel spike trains
Summary
The cortex is comprised of a highly interconnected network of neurons and one may speculate that information processing in the brain may only be understood on the basis of the concerted activity of the neuronal population. Hebb (1949) suggested that neurons coordinate their activities by organizing in functional groups, termed cell assemblies. As excitatory postsynaptic potentials are small in amplitude compared to the gap between the resting potential and the neuronal firing threshold, it is expected that a cell assembly is composed of many neurons firing in a correlated fashion This observation is the basis for the assumption that higher-order synchronous spiking activity serves as a signature expression of an active assembly (Riehle et al, 1997; Berger et al, 2010; Staude et al, 2010b; Shimazaki et al, 2012). In recent years considerable progress has been made in the development of multi-electrode recording techniques [e.g., Nicolelis, 1998; Buzsaki, 2004; Hatsopoulos et al, 2007; Riehle et al, 2013], which enable to record the activity of hundred(s) of neurons Such massively parallel spike train data pose statistical challenges due to the inherent complexity of the required multivariate approaches. The discussion (Section 5) includes a step-by-step instruction on how to utilize the proposed method in the context of massively parallel spike trains obtained from electrophysiological recordings
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