BME Seminar- Tatiana Engel, Ph.D. Stanford University
- Starts: 4:00 pm on Tuesday, March 21, 2017
- Ends: 5:00 pm on Tuesday, March 21, 2017
Discovering dynamic computations in the brain from large-scale neural recordings Neural responses and behavior are influenced by internal brain states, such as arousal, vigilance, or task context. Ongoing variations of these internal states affect global patterns of neural activity, giving rise to apparent variability of neural responses to sensory stimuli, from trial-to-trial and across time within single trials. Demultiplexing these endogenously generated and externally driven signals proved difficult with traditional techniques based on trial-averaged responses of single neurons, which dismiss neural variability as noise. In this talk, I will describe my recent work leveraging multi-electrode neural activity recordings and computational models to uncover how internal brain states interact with perception and goal-directed behavior. I will show that ensemble neural activity within single columns of the primate visual cortex spontaneously fluctuates between phases of vigorous (On) and faint (Off) spiking. These endogenous On-Off dynamics, which reflect global changes in arousal, are also modulated at a local scale during spatial attention and predict behavioral performance. I will also demonstrate that these On-Off dynamics provide a single unifying mechanism that explains general features of correlated variability classically observed in cortical responses (e.g., changes in neural correlations during attention). I will conclude by sketching out a roadmap for developing a general theory that will allow us to discover dynamic computations from large-scale neural recordings and to link these computations to behavior. ————————— Short Bio ————————————— Tatiana Engel is a research scientist working jointly with Prof. Kwabena Boahen and Prof. Tirin Moore at Stanford University. Her research aims to understand how behavioral and cognitive functions (such as attention, decision making, learning) arise from dynamics in large neural circuits, using a combination of theory, biophysical modeling and model-driven analysis of large-scale neural recordings. Before coming to Stanford, Tatiana was a postdoctoral fellow in the lab of Prof. Xiao-Jing Wang at Yale University, where she worked on building computational and neural circuit models of category learning, working memory and decision making. Prior to that, Tatiana obtained PhD in Theoretical Physics from Humboldt University at Berlin in Germany, and MSc in Physics from Lomonosov Moscow State University in Russia.
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