Brain Inspired BI 202 Eli Sennesh: Divide-and-Conquer to Predict
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Jan 3, 2025 In this engaging discussion, Eli Sennesh, a postdoctoral researcher at Vanderbilt University, sheds light on predictive coding and its implications for understanding brain functions. He navigates the intriguing concept of 'divide-and-conquer predictive coding' and its experimental applications. The conversation also touches on the relationship between neuroscience and AI, emphasizing the need for biologically plausible computational models. They explore the complexities of decision-making, consciousness, and the humor in our perceptions of task difficulty, offering a delightful blend of research insights and personal anecdotes.
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Constrained Experiments
- Eli Sennesh questions the usefulness of constrained neuroscience experiments, like head-fixed monkeys.
- He argues that such setups limit the study of broader behaviors and internal states.
Probabilistic Approach
- Eli Sennesh's interest in cognitive science was sparked by the work of Brendan Lake and Josh Tenenbaum.
- He found their probabilistic approach to concept learning compelling and began exploring similar ideas in neuroscience.
Collaboration with Barrett and Quigley
- Eli Sennesh cold-emailed Lisa Feldman Barrett and Karen Quigley, leading to a collaboration on probabilistic approaches to emotion.
- He was initially drawn to their work because of its focus on feelings and "why."
