The Computational Neurobiology of Perceptual Awareness

Understanding the neurobiological basis of consciousness presents a critical obstacle: the explanatory gap between human and animal model-based neuroscience. Human participants can perform a rich array of behavioural tasks that precisely target psychological constructs, but our ability to non-invasively record and control neural activity is limited. Conversely, in animal models, it is possible to invasively record and perturb neural activity with exquisite detail and precision, but the range of psychological constructs that can be studied is highly restricted. This thesis bridges this explanatory divide by constructing biophysical models based on nonlinear dynamical systems theory that explicitly link systems neuroscience with human psychophysics. Part one focuses on the neurobiology of perceptual awareness. Following a review of the state-of-the-art in systems neuroscience, which identifies an essential role for thalamocortical loops in controlling the state and contents of consciousness, I introduce a spiking neural network model of perceptual awareness that incorporates the key cellular elements of thalamocortical loops covered in the review. The model reproduces neural signatures of perceptual awareness found in mouse models and generalises to visual rivalry, generating a series of novel predictions. Part two abstracts away from cellular detail to derive law-like expressions for awareness thresholds in a novel paradigm: tracking continuous flash suppression (tCFS). I demonstrate that a reduced neural mass model can be derived from the thalamocortical dynamics established in part one. Simulations of the reduced model confirm that the mechanisms governing binocular rivalry generalise to tCFS. I then derive closed-form expressions for dominance durations and awareness thresholds, validating them against existing psychophysical data. Collectively, the thesis provides a quantitative roadmap for integrating the tools of systems neuroscience with human psychophysics.