Connectome-constrained cortical circuits optimized for visual function and working memory tasks
Recurrent neural networks (RNNs) trained on working memory (WM) tasks have been the subject of extensive study. These networks typically contain one or more aspects of biological-implausibility, such as the adoption of saturating output functions (tanh), violation of Dale's Law or introduction of artificial elements (LSTM). They also have over-simplistic input pathways such as binary cues, which are insufficient to represent the rich dynamics found in sensory brain areas. In particular, the role of inter-areal communication between sensory and association areas for WM has been critically under
Fri, Mar 10, 2023