Abstract: Neuroscience is currently undergoing an explosion in the availability of large-scale brain activity data, so the major challenge no longer lies in data collection, but also in deriving understanding from this abundant stream of complex, high-dimensional, noisy data with methods that fully leverage its potential. How can we understand neural representations and infer computational principles from large-scale brain activity data directly?

In this talk, I will present several lines of previous research aimed at tackling this question. First, I will present a line of data-driven modeling work that revealed the representational structure in the high-level visual cortex and led to the discovery of a neural population selectively responsive to images of food. Second, I will present a modeling framework, called response-optimization, for inferring computations directly from brain activity data with minimal apriori hypotheses. Here, we trained artificial neural network (ANN) models directly to predict the brain activity related to viewing natural images. We then developed techniques for interpreting the networks and characterizing the emergent functional capabilities of these brain response-optimized networks. This work highlights how models trained to capture human brain activity can spontaneously recapitulate human-like behavior. Finally, I will present my work on developing neural network models of brain responses across wide-spread cortical regions to dynamic, multi-modal stimuli like movies, with an integrated modeling approach that captured visual attention, multi-sensory auditory-visual interactions and temporal context.

This will be an in person only meeting.