Daming Li

Daming Li's picture
Applied Scientist
Uber
Research Areas: 
Biological Physics, Computational Neuroscience
Education: 
Ph.D. 2022, Yale University
Thesis Advisor: 
John Murray
Dissertation Title: 
Bistable Neural Dynamics Underlying Cognition and Spontaneous Activity: Computational Modeling and Empirical Analysis
Dissertation Abstract: 

Characterizing the spatiotemporal patterns of neural dynamics is crucial to understanding many brain functions and psychiatric disorders. A commonly observed dynamic pattern is bistability, where the neurons switch between a high and a low firing state over the course of time. However, the origin of bistable dynamics and its role in cognitive functions and spontaneous activity remain unclear. In this thesis, we combined empirical data analysis and dynamical systems modeling approaches to quantitatively study brain dynamics. Statistical analysis allowed discovering various patterns of interest hidden in the experimental data, while dynamical systems modeling allowed us to causally test different hypotheses of the underlying data generating mechanisms. We studied this topic in two separate scenarios. We first investigated the role of bistable dynamics in working memory (WM). A recently proposed mechanism of WM information maintenance based on local field potential analysis says that neural spiking activity displays on/off switching dynamics on single trials. Nonetheless, such bistable dynamic patterns have not yet been validated on single neuron spike train data, and triggered lots of debates in the field. Here we combined statistical modeling, neural circuit simulation, and empirical data analysis, to show that the contribution of bistable dynamics to WM information maintenance is strongly limited. Next, we studied the noradrenergic neuromodulatory effect on the restingstate dynamics. In this context, the widelyused single fixed point models fail to capture the effect on fMRI autocorrelation, suggesting the existence of multiple time scales in the underlying processes. To proceed, we created and simulated a largescale bistable network model, which successfully explained the observed treatment effects, thereby establishing a framework integrating cellular mechanism, macroscopic neuroimaging, and basic cognitive processes. Together, we show that neural dynamics is flexibly reshaped in response to external stimuli and pharmacological interventions in a dynamic environment.