The mammalian brain is believed to be optimally designed for robust and adaptable computation of the sensory inputs from the world, with respect to both its hardware (network structure) and software (network dynamics). The precise connections between the intricate structural connectivity and the rich network dynamics, however, are yet unknown. Moreover, our understanding of how the network structure and dynamics shape (or are shaped by) underlying coding principles in the brain network, is limited. My research plan proposes to close this gap by leveraging rich dataset obtained by state-of-art experimental techniques at the Allen Institute for Brain Science and innovative mathematical methods. Specifically, my project aims to 1) link network structure and dynamic information processing in the brain, and to 2) bridge the gap between detailed biophysiological mechanisms and overarching neural coding principles with a focus on predictive coding theory, using data-driven mathematical models. To address Aim 1, I will investigate how network dynamics measured by synchronizability, metastability, and integrated information depend on local and global structure of the network. I will then study whether the experimentally obtained mouse brain connectome has optimal connectivity structures for unique dynamical characteristics. These analyses will be extended to the cell-type and layer-specific brain connectivity, based on the latest Allen Mouse Brain Connectivity data obtained from Cre-transgenic mice. During the independent phase, I will investigate whether brain-like networks can be evolved from optimization of dynamic measures. Regarding Aim 2, I will analyze data obtained from my current collaborative project which experimentally tests predictive coding models in the mouse visual cortex. In this study, we measure neural activity in response to expected and unexpected sequences of natural stimuli across three hierarchically related areas. Upon completion of the experiments, during the mentored phase, I will investigate mapping of algorithmic units in predictive coding models to neuronal populations in different layers. During my independent career period, I will extend the predictive coding model to incorporate active sensing and thalamo-cortical circuitries. The project during the mentored phase will be carried out at the University of Washington which provides a highly interdisciplinary environment and offers the ideal training for me to become an independent researcher. I will also have access to rich resources and outstanding collaborators at the Allen Institute for Brain Science. I will have two mentors, one from the University of Washington and another from the Allen Institute for Brain Science. This unique setup will allow me to study mathematical models based on experimental data obtained by cutting-edge techniques with guidance from mentors with strong theoretical backgrounds. With theories closely tied to experiments, I believe my proposed project will contribute to our understanding of the connection between structure and computation of the neuronal network, addressing BRAIN initiative?s high priorities.
The proposed research aims to probe how adaptable sensory coding arises from intricate connectivity structure and dynamics of the brain network at multiple scales. This study will contribute to our understanding of connections between unique structure and dynamics of the brain network as well as the mapping of complex neuronal dynamics in the cortical circuitry to algorithmic nodes in predictive coding models. By combining cutting-edge experimental and theoretical tools, this project will contribute to identifying fundamental principles underlying emergence of cognition and perception from neuronal network dynamics.