Manifold representation in continuous attractor neural networks: a general constructive approach
Federico Claudi, Sarthak Chandra, Ila Fiete · COSYNE 2023
Low dimensional attractor dynamics have now been established as a core mechanism by which neural populations represent, remember, and compute with continuous-valued external variables. Arriving at this state in the field has relied on the intuition-guided construction of neural circuit models with continuous attractor dynamics that led to population-level predictions about dynamics, followed by validation from large-scale recordings. Recent deep learning approaches require less intuition but extensive training to generate networks with continuous attractor and integrator dynamics, however extr
Fri, Mar 10, 2023