This center leverages the strengths of Carnegie Mellon in cognitive and computational neuroscience and those of the University of Pittsburgh in basic and clinical neuroscience to support a coordinated cross-university research and educational program of international stature.


Events

Nov
12
Tue
Simon Initiative Distinguished Lecture: Chi
Nov 12 @ 4:30 PM – 6:30 PM

Michelene (Micki) T.H. Chi
Foundation Professor and Dorothy Bray Endowed Professor of Science and Teaching, Mary Lou Fulton Teachers College; Director of the Learning and Cognition Lab, Arizona State University

Tuesday, November 12
4:30 — 6:30 p.m.
Simmons Auditorium A, Tepper Buidling
A reception will immediately follow the lecture.
ICAP: How to Promote Deeper Learning by Engaging Students Cognitively

Nov
14
Thu
CNBC Colloquium – Jennifer Raymond
Nov 14 @ 4:00 PM – 5:00 PM
Nov
21
Thu
CNBC Colloquium – Matthew Botvinick @ MI 328
Nov 21 @ 4:00 PM – 5:00 PM

“A distributional code for value in dopamine-based reinforcement learning”
Matthew Botvinick, MD, PhD
Director of Neuroscience, DeepMind

Thursday, November 21, 2019
4:00pm
328 Mellon Institute
Twenty years ago, a link was discovered between the neurotransmitter dopamine and the computational framework of reinforcement learning. Since then, it has become well established that dopamine release reflects a reward prediction error, a surprise signal that drives learning of reward predictions and shapes future behavior. According to the now canonical theory, reward predictions are represented as a single scalar quantity, which supports learning about the expectation, or mean, of stochastic outcomes. I’ll present recent work in which we have proposed a novel account of dopamine-based reinforcement learning, and adduced experimental results which point to a significant modification of the standard reward prediction error theory. Inspired by recent artificial intelligence research on distributional reinforcement learning, we hypothesized that the brain represents possible future rewards not as a single mean, but instead as a probability distribution, effectively representing multiple future outcomes simultaneously and in parallel. This idea leads immediately to a set of empirical predictions, which we tested using single-unit recordings from mouse ventral tegmental area. Our findings provide strong evidence for a neural realization of distributional reinforcement learning.

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