Neuroendocrine and hormone dynamics:
stress response and treatment

A schematic diagram of HPA axis dynamics The hypothalamic-pituitary-adrenal (HPA) axis is a set of endocrine glands that interact with each other to control stress response. One of the key functions of the HPA axis is regulation of cortisol production. I am interested in investigating how cortisol production is dysregulated during the onset of stress-related disorder, and in the long term, how hormonal dysregulation influences immune response, metabolic disease, and cancer initiation. To gain insight into the mechanism of exposure therapy, I have developed a dynamical systems model of the HPA axis that captures hourly cortisol oscillations and defines normal and diseased states as two stable equilibria. The key feature of our model is that it separates fast negative feedback mechanisms from slow ones. The paradigm of fast-slow timescale separation is borrowed from theories in mathematical neuroscience and can be used in many other unexplored physiological systems of clinical relevance.

Neural field model of retina-tectum:
encoding regularity and detection of novelty

A schematic diagram of retina-tectum network The detection of novelty in environmental stimulus is essential for adapting to a constantly changing environment. On the other hand, recognizing and establishing a regular pattern in stimulus and storing the information is another aspect of detecting novelty. These processes happen in various levels of our cognitive system in response to various types of stimulus. I am interested in understanding the novelty detection in the visual system in the retina-tectum network. I have developed a phenomenological model of the retina-tectum network, incorporating methods developed in continuous neural field theory. Using the model, I aim to understand how dynamic networks can establish and store an expectation of regularity and how different spatio-temporal patterns of activity may emerge in response to violations of such expected regularity of stimulus.