Join us September 11, 2023 for an exciting day of talks and poster presentations at the School of Medicine and Danforth campuses.
CNNG Symposium @ WashU is a prestigious symposium sponsored by the newly established Center for Theoretical and Computational Neuroscience (CTCN) at Washington University in St. Louis. Eight selected senior graduate students from the US and Europe, who are using primarily theoretical and computational techniques to study the principles of brain organization and function, are coming to WashU to share their thesis work, learn about WashU’s stellar research environment, and expand their scientific network.
For inquiries contact the CTCN.
Program
8:55 am Opening remarks – Geoff Goodhill (Director, CTCN; WashU Developmental Biology and Neuroscience)
9:00 am Daniel Barabasi (Engert lab, Harvard University) – “Complex computation from developmental priors“
9:30 am Sigrid Trägenap (Kaschube lab, International Max-Planck Research School for Neural Circuits) – “The nature-nurture transform underlying the emergence of reliable cortical representations“
10:00 am Gaia Tavoni (WashU Neuroscience) – “Understanding the influences of context on efficient sensory coding“
10:45 am Tim Kim (Brody lab, Princeton University) – “Flow-field inference from neural data using deep recurrent networks“
11:15 am Hayoung Song (Rosenberg lab, University of Chicago) – “Neural dynamics underlying naturalistic human cognition“
11:45 am ShiNung Ching (WashU Electrical & Systems Engineering) – “Data-driven identification of multi-scale neural dynamics for understanding and enhancing cognitive function“
1:00 pm Yusi Chen (Sejnowski lab; University of California, San Diego) – “Predictive sequence learning in the hippocampal formation“
1:30 pm Joseph Pemberton (Ponte Costa lab, University of Bristol) – “Cerebellar-driven strategies for brain-wide learning“
2:00 pm Naoki Hiratani (WashU Neuroscience) – “Synaptic credit assignment in the octopus vertical lobe“
2:45 pm Jacob Zavatone-Veth (Pehlevan lab, Harvard University) – “Effect of prior knowledge on generalization in deep linear neural networks“
3:15 pm Alessandro Marin-Vargas (Mathis lab, Ecole Polytechnique Fédérale de Lausanne (EPFL)) – “Task-driven neural network models predict neural dynamics of proprioception“
4:30 pm Poster session (Hillman Hall, Danforth Campus) – Refreshments will be served.
