ICON Seminar in AI: Dr. Noah J. Cowan (Johns Hopkins)
| Author: | Yunyue Elita Li |
|---|---|
| Event Date: | November 1, 2024 |
| Speaker: | Dr. Noah J. Cowan |
| Speaker Affiliation: | Johns Hopkins University |
| Priority: | No |
| College Calendar: | Show |
Time: 3-4 pm Eastern Time, Nov 1 (Friday), 2024
Location: MSEE 112
Zoom Link: https://purdue-edu.zoom.us/j/98798335169
Coffee and snacks will be provided.
Control and recalibration of path integration in the hippocampus
Abstract:
The hippocampus can be thought of as the “Simultaneous Localization and Mapping” (SLAM) center of the mammalian brain. For example, when an animal moves in a familiar environment, certain neurons in the hippocampus, called place cells, fire when the animal occupies a certain region in that environment, encoding the animals 2D position. Another subpopulation of neurons, called head direction cells, represent the animal’s compass heading. To continuously update the animal’s position and orientation on this internal ‘cognitive map’, the hippocampal system integrates self-motion signals over time. External landmarks then provide feedback to correct the errors in the position estimate that would otherwise inevitably accumulate. Using a novel virtual reality apparatus, we discovered that if path integration is biased, such that the animal consistently under- or overestimates its movement through space, the landmarks (Jayakumar et al, Nature, 2019) or optic flow cues (Madhav et al, Nature Neuroscience 2024) in the environment can serve as a teaching signal for recalibration of the path integrator. Using a biophysically plausible attractor neural network model of path integration, we show that for landmark-based recalibration the path integration error, or its integral, must be encoded at the level of individual neurons in order to enable path integration recalibration (Secer et al, 2024, bioRxiv). Using this prediction, we turned back to the physiological data and discovered a rate code for error at the level of individual neurons.
Speaker:
Noah J. Cowan received a B.S. degree from the Ohio State University, Columbus, in 1995, and M.S. and Ph.D. degrees from the University of Michigan, Ann Arbor, in 1997 and 2001 – all in electrical engineering. Following his Ph.D., he was a Postdoctoral Fellow in Integrative Biology at the University of California, Berkeley for 2 years. In 2003, he joined the mechanical engineering department at Johns Hopkins University, Baltimore, MD, where he is now tenured at the rank of Professor. Prof. Cowan’s research interests include mechanics and multisensory control in animals (including humans) and machines and he has published scholarly articles in a diverse range of fields, from control systems and robotics to neuroscience and biomechanics. Prof. Cowan received the NSF PECASE award in 2010, the James S. McDonnell Foundation Scholar Award in Complex Systems in 2012, the William H. Huggins Award for excellence in teaching in 2004, and Johns Hopkins University Discovery Awards in 2015, 2016 and 2023, and is a Fellow of the IEEE. Google scholar profile: https://scholar.google.com/citations?user=V6LJpwgAAAAJ&hl=en&oi=ao
Organizers: Ziran Wang (ziran@purdue.edu), Nak-seung Patrick Hyun (nhyun@purdue.edu), & Yunyue Elita Li (elitali@purdue.edu)
2024-11-01 08:00:00 2024-11-01 17:00:00 America/Indiana/Indianapolis ICON Seminar in AI: Dr. Noah J. Cowan (Johns Hopkins) Purdue University