Neuroscience/Neurobiology Seminar Series



For Zoom information, see email sent to cnbc-all list or contact christi@cmu.edu.

January 13: 
Dr. Anne ChurchlandProfessor of Neurobiology
UCLA 
Single-trial neural dynamics are dominated by richly varied movements

January 27: 
Dr. Jessica Cardin
Associate Professor of Neuroscience
Yale University

State-dependent cortical circuits

February 17:Dr. Yi Zuo
Professor of Molecular, Cell, & Developmental Biology

UC Santa Cruz

Experience-dependent synapse reorganization in the living brain 


March 24: 
Dr. Kwabena Boahen
Professor of Bioengineering and Electrical Engineering
Stanford University

TBD (topic: Brains in Silicon)


April 14: 
Dr. Julie Fudge
Professor of Neuroscience and Psychiatry
University of Rochester Medical Center

Cortical granularity shapes information flow to the amygdala and beyond: lessons from nonhuman primates

April 21: 
Dr. Takao Hensch
Professor of Molecular & Cellular BIology, and Neurology
Harvard University

TBD

April 28: 
Dr. Kamran Khodakhah
Professor of Neuroscience, Psychiatry & Behavioral Sciences
Albert Einstein College of Medicine

Cerebellar modulation of dopaminergic signaling

Yael Niv

Professor of Psychology and Neuroscience, Princeton Neuroscience Institute

Latent causes, prediction errors, and the organization of memory

May 6, 2021 at 4:00p.m. EST

Bio:

Yael Niv received her MA in Psychobiology from Tel Aviv University and her PhD in Computational Neuroscience from the Hebrew University in Jerusalem, having conducted a major part of her thesis research at the Gatsby Computational Neuroscience Unit in UCL. She is currently a professor at Princeton University, at the Psychology Department and the Princeton Neuroscience Institute. Her lab studies the neural and computational processes underlying reinforcement learning and decision making, with a particular focus on how the cognitive processes of attention, memory and learning interact in constructing task representations that allow efficient learning and decision making. She is co-founder and co-director of the Rutgers-Princeton Center for Computational Cognitive Neuropsychiatry, where she is applying ideas from reinforcement learning to questions pertaining to psychiatric disorders within the new field of computational psychiatry. 

Abstract:

Latent causes, prediction errors, and the organization of memory    

In recent years, my lab has suggested that incoming information is parsed into separate clusters (“states” in reinforcement learning parlance) — all events that are assigned to one cluster are learned about together, whereas events in different clusters do not interfere with each other in learning. Moreover, we have suggested that prediction errors are key to this separation into clusters. In this talk, I will revisit these ideas building not only on behavioral experiments showing evidence for clustering, but also experiments that show the effects of prediction errors on episodic memory. I will attempt to tie the different findings together into a hypothesis about how prediction errors affect not only learning, but also the organization of memory.