Neural Dynamic
Neural dynamics research focuses on understanding how the activity of interconnected neurons generates behavior and cognition. Current efforts concentrate on developing and applying computational models, including recurrent neural networks, spiking neural networks, and variational autoencoders, to analyze neural data and infer underlying dynamical systems from diverse experimental modalities. This work aims to bridge the gap between observed neural activity and behavior, leading to improved understanding of brain function and informing the design of more biologically plausible and efficient artificial intelligence systems. Furthermore, these advancements have implications for applications such as brain-computer interfaces and the development of more robust and interpretable machine learning models.
Papers
Learning low-dimensional dynamics from whole-brain data improves task capture
Eloy Geenjaar, Donghyun Kim, Riyasat Ohib, Marlena Duda, Amrit Kashyap, Sergey Plis, Vince Calhoun
From Data-Fitting to Discovery: Interpreting the Neural Dynamics of Motor Control through Reinforcement Learning
Eugene R. Rush, Kaushik Jayaram, J. Sean Humbert