Topic: universal approximator

Recurrent Neural Networks (RNNs) are a popular computational tool in neuroscience. Their architecture is inspired by biology, namely the abundance of lateral connections in cortex. Because they are universal approximators, they can be trained to match behavior in common neuroscience tasks, and their hidden state can be compared to neural recordings. Despite the biological inspiration, many features of real neurons are abstracted away (e.g. morphology, neuromodulators, excitability dynamics, short term plasticity). While many of these properties were included as add-ons to standard RNN models,