Bridging spatial scales in the statistical analysis of neural signals in speech and anesthesia (Emily Stephen - University of California San Francisco)

Starts: 4:00 pm on Thursday, January 30, 2020
Ends: 5:00 pm on Thursday, January 30, 2020
Location: MCS B39

While neuroscience research has traditionally focused on the firing rates of neurons at the micro scale and the brain areas involved in different tasks at the macro scale, there has been little progress in understanding the computational principles that organize and synthesize lower level signals to produce the effects that we see at larger scales. In this talk, I will describe my work using statistical models to bridge spatial scales: first, by modeling oscillatory activity during anesthesia-induced unconsciousness, and second, by modeling functional coupling during speech production and perception. In the process, I will make the case that building statistical models that bridge spatial scales is a valuable approach for a wide range of neuroscience questions. Such models can explain discrepancies in signals recorded at different scales (e.g. action potentials and LFPs) and improve our understanding of the micro-scale dynamics that underlie macro-scale biomarkers of pathological states.