ePoster · Neuroscience
Operative Dimensions in High-Dimensional Connectivity of Recurrent Neural Networks
Renate Krause,Matthew Cook,Valerio Mante,Giacomo Indiveri · COSYNE 2022
Recurrent Neural Networks (RNN) are useful models to study neural computation. Several approaches are available to train RNNs on neuroscience-related tasks and reproduce neural population dynamics. However, a comprehensive understanding of how the generated dynamics emerge from the underlying connectivity is largely lacking. Previous work derived such an understanding for specific types of constrained RNNs (Mastrogiuseppe and Ostojic, 2020; Curto et al., 2019; Biswas and Fitzgerald, 2020), but if and how the resulting insights apply more generally is unclear. In this work, we study how network