Topic: counterstream inhibitory bias

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