Finding neural assemblies with frequent item set mining - Picado-Muiño, David; Borgelt, Christian; Berger, Denise; Gerstein, George L.; Grün, Sonja (Frontiers in Neuroinformatics, 2013)
Cell assemblies, defined as groups of neurons exhibiting precise spike coordination, were proposed as a model of network processing in the cortex. Considerable progress made in recent years in multi-electrode technology enables to record massively parallel spike trains of hundred(s) of neurons. Nevertheless, due to the challenges inherent in multivariate approaches, most studies favoring cortical cell assemblies still resorted to analyzing pairwise interactions. However, to recover the underlying correlation structures, higher-order correlations need to be identified directly. Inspired by the Accretion method (Gerstein et al. 1978) we propose a new assembly detection method based on frequent item set mining (FIM), which searches effectively and without redundancy for individual spike patterns that exceed a given support threshold. We study search methods, with which the space of potential cell assemblies may be explored, as well as test statistics and subset conditions with which candidate assemblies may be assessed and filtered. It turns out that a core challenge of assembly detection is multiple testing, which causes a large number of false discoveries. Unfortunately, criteria that address individual candidate assemblies and try to assess them with statistical tests and/or subset conditions do not help much to tackle this problem. The core idea of our method is to shift the focus of statistical testing from specific assemblies (consisting of a specific set of neurons) to spike patterns of a certain size (i.e. with a certain number of neurons). This significantly reduces the number of necessary tests, thus alleviating the multiple testing problem. We demonstrate that our method is able to reliably suppress false discoveries, while it is still very sensitive in discovering synchronous activity. Since we exploit high-speed computational techniques from frequent item set mining (FIM) for the tests, our method is also computationally efficient.