Uncovering neural patterns of cognition by aligning oscillatory dynamics

A primary aim of cognitive neuroscience is to explain how cognition is physically realized by the brain. Toward this end, neuroscientists study consistent activity patterns produced across ensembles of neurons. Importantly, such ensembles are subject to excitability fluctuations imposed by neural oscillations, which are used in a self-organized way to realize windows of effective communication, coding schemes, a switch between memory processes, interareal information exchange, and other functions. Given the intimate link between oscillations and neural processing, this thesis explores the power of studying activity patterns of cognition with reference to brain dynamics. Specifically, I submit that spectral information like phase and oscillatory cycles offer a brain-intrinsic coordinate system by which the readout of neurocognitive patterns can be assisted. From this vantage point, I explore two methodological advances: (1) brain time warping, which incorporates oscillatory dynamics post-hoc after brain data has been acquired, and (2) visual perturbation or “pings”, which artificially regularize oscillations as memory retrieval is ongoing. We demonstrate that brain time warping can reveal activity patterns otherwise left undetected, and we introduce a comprehensive toolbox to apply the algorithm and test its effects. On the other hand, we found no evidence that pings enhance the readout of memory representations from electroencephalography data. Together, these empirical and theoretical points underscore the need for a neurally inspired methodology in which scientists are cast as spectators with privileged access to external world variables.