Topic: support states

A central goal of modern neuroscience is to relate structure, dynamics, and computation in recurrent neural networks (RNNs), in particular those that can be created via biological learning rules. To date, most work on RNN training has focused on either efficient but non-biological rules (e.g., backprop, FORCE) or biologically plausible rules with poor learning efficiency (e.g., Hebbian learning). However, some of the fastest, strongest learning rules thought to act in vivo are not Hebbian, for instance in the fly mushroom body, where plasticity depends on high-dimensional presynaptic activity