Pachitariu, M; (2015) Neural dynamics in cortical populations. Doctoral thesis , UCL (University College London).

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Abstract

Many essential neural computations are implemented by large populations of neurons working in concert. Recent studies have sought both to monitor increasingly large groups of neurons and to characterise their collective behaviour, but the standard computational approaches available to identify the collective dynamics scale poorly with the size of the dataset. We develop new efficient methods for discovering the low-dimensional dynamics that underlie simultaneously-recorded spike trains from a neural population. We use the new models to analyze two different sets of population recordings, one from motor cortex and another from auditory cortex. In motor cortex, we describe the nature of the trial-by-trial spontaneous fluctuations identified by the model and connect these fluctuations to behavioral events. The spatio-temporal structure of the spontaneous events was tracked by three trajectories identified by the model. These trajectories followed similar dynamics during hand reaches as they did when the hands were stationary. The structure of the models we developed allow them to be used as decoders of hand position from neural activity, significantly improving upon previous state-of-the-art methods. The decoders were able to predict information about the direction, onset time and speed profile of movements. In auditory cortex, we use the statistical models to identify population dynamics under different brain states. We report major differences in dynamics and stimulus coding between synchronized and desychronized brain states. Synchronized but not desynchronized brain states imposed constraints on neural dynamics such that a four-dimensional system accounted for most of the dynamical structure of population events. We used the low-dimensional representation of the data to construct network simulations that reproduced the patterns present in the recordings. The simulations suggest that the overall level of feedback inhibition controls the stability of each local cortical network, with unstable dynamics resulting in synchronized brain states. Finally we propose a functional role for dynamics in the representation of visual motion in visual cortex.

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