ePoster · Computational Neuroscience
Identifying task-specific dynamics in recurrent neural networks using Dynamical Similarity Analysis
Alireza Ghalambor, Mohammad Taha Fakharian, Roxana Zeraati, Shervin Safavi · Bernstein Conference 2024
Understanding the circuit mechanisms of brain computation and behavior is one of the central questions in neuroscience. Recent advances in task-optimized recurrent neural networks (RNNs) have opened a new avenue to directly link neural dynamics to behavior and uncover the underlying mechanisms by reverse engineering the trained RNNs. Previous studies leveraged fixed points to compare RNNs across limited tasks and found that while the geometry of neural representations varies, fixed point topology is consistent across RNNs despite differences in model architecture [1]. However, linearizing dyna