Neural population dynamics underlying oculomotor decision making

Single neuron neurophysiology has contributed much to understanding the role of prefrontal and parietal cortices in visually guided decision making tasks. One of the drawbacks of single neuron studies in elucidating mental processes underlying behaviour is that, they usually cannot account for response diversity observed in single neurons. The recent advent in technologies that allow simultaneous monitoring of large populations of cells paired with powerful statistical models from machine learning are shedding light on population level mechanisms that underly cognitive processing. This project aims to leverage the above two aspects of simultaneous recordings and statistical modelling of neural data, to elucidate population level mechanisms involved in oculomotor decision making. Particularly, my goal is to explore the use of statistical models popular in dynamical systems and control theory to gain an understanding of internally generated sequences of neural activity in prefrontal cortex during evidence accumulation, saccade planning & preparation.


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