Statistical approaches for reconstructing neuro-cognitive dynamics from high-dimensional neural recordings
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Authors: Durstewitz, D. and Balaguer-Ballester, E.
Recent advances in multiple single-unit recording and optical imaging techniques now routinely allow to observe the activity from tens to hundreds of neurons simultaneously. The result are high-dimensional multivariate time series which offer an unprecedented range of possibilities for gaining insight into the detailed spatio-temporal neural dynamics underlying cognition. For instance, they may pave the way for reliable single-trial analyses, for investigating the role of higher-order correlations in neural coding, the mechanisms of neural ensemble formation, or more generally of transitions among attractor states accompanying cognitive processes. At the same time, exploiting the information in these multivariate time series may require more sophisticated statistical methods beyond the commonly employed repertoire. Here we review, along some specific experimental examples, some of these methods for visualizing structure in high-dimensional data sets, for statistical inference about the apparent structure, for single-trial analysis of neural time series, and for reconstructing some of the dynamical properties of neural systems that can only be inferred from simultaneous recordings.