Before revision: RNNs are popular dynamical models, used for processing sequential data  . Prior theoretical work in understanding the properties of RNNs has focused on models with additive interactions , where the input to a unit is a weighted sum of the output of the remaining units in network .
After revision: RNNs are popular dynamical models, used for processing sequential data , and also in neuroscience, to understand the emergent properties of networks of real neurons . Prior theoretical work in understanding the properties of RNNs has focused on models with additive interactions , where the input to a unit is a weighted sum of the output of the remaining units in network .