Publications:8
Evan Schaffer, PhD
About Me
Dr. Evan Schaffer joined the Department of Neuroscience at the Friedman Brain Institute in 2023. His lab focuses on understanding the mechanisms and computational principles behind distributed signals in the brain and how neural representations change due to behavioral state and ongoing learning. The Schaffer lab uses mathematical and computational tools to explore the roles of proprioception and behavioral state in higher order cognitive processing and how they are impacted by psychiatric disorders.
Dr. Schaffer received his Ph.D. in Neurobiology from Columbia University in Dr. Larry Abbott’s Lab, where he studied the dynamics of recurrent neural networks. Dr. Schaffer completed his postdoctoral work in Dr. Richard Axel’s Lab at Columbia University. There, he developed network models to understand the consequences of random connectivity in the olfactory system and also developed novel methods to examine activity across the entire brain of behaving Drosophila. For more information, visit www.schafferlab.com.
Language
English
Position
ASSISTANT PROFESSOR | Neuroscience, ASSISTANT PROFESSOR | Graduate Education
Research Topics
Computational Biology, Computational Neuroscience, Neural Code, Neural Networks, Neuroscience, Systems Neuroscience, Theoretical Biology, Theoretical Neuroscience
Multi-Disciplinary Training Areas
Artificial Intelligence and Emerging Technologies in Medicine [AIET], Neuroscience [NEU]
Education
BA, Swarthmore College
PhD, Columbia University
Awards
2023
Simons Collaboration on the Global Brain Award
Research
In many brain regions, the stimulus tuning of neurons is stable on a timescale of hours but not on a timescale of weeks, a phenomenon often called ‘representational drift’. For example, in piriform cortex, which is commonly considered primary olfactory cortex, the cells responsive to a given odor are completely uncorrelated with those activated by the same odor a few weeks later. This would seem to imply that piriform cortex, like other brain regions whose activity appears to drift, is useless for the retrieval of associative memories learned several weeks prior. We study how representations change over time in both models and data in order to understand which properties of these representations are stable over time and how this could be leveraged by downstream areas.
Publications
Selected Publications
- MULTI-MODAL GAUSSIAN PROCESS VARIATIONAL AU-TOENCODERS FOR NEURAL AND BEHAVIORAL DATA. Rabia Gondur, Usama Bin Sikandar, Evan S. Schaffer, Mikio Aoi, Stephen Keeley.
- The spatial and temporal structure of neural activity across the fly brain. Evan S. Schaffer, Neeli Mishra, Matthew R. Whiteway, Wenze Li, Michelle B. Vancura, Jason Freedman, Kripa B. Patel, Venkatakaushik Voleti, Liam Paninski, Elizabeth M.C. Hillman, L. F. Abbott, Richard Axel. Nature Communications
- Deep graph pose: A semi-supervised deep graphical model for improved animal pose tracking. Anqi Wu, E. Kelly Buchanan, Matthew R. Whiteway, Michael Schartner, Guido Meijer, Jean Paul Noel, Erica Rodriguez, Claire Everett, Amy Norovich, Evan Schaffer, Neeli Mishra, C. Daniel Salzman, Dora Angelaki, Andrés Bendesky, John Cunningham, Liam Paninski. Advances in Neural Information Processing Systems