Review for NeurIPS paper: System Identification with Biophysical Constraints: A Circuit Model of the Inner Retina

Additional Feedback: Is there any way to find the same phenomenon shown for the BCN in the LSTM? The BCN is a nice step towards merging biophysical models with backprop and gradient descent training routines, but I think it is still taking advantage of a large amount of retina-specific knowledge. Perhaps there's a way to identify the ersatz representation of bipolar and amacrine cells within the LSTM? I understand there's no cell types engineered into this model, but perhaps guided by knowledge of its specific computations (i.e., gates could act as a stand in for ACs), or a clustering analysis of its unit responses, you could identify a correspondence between its parameters and those in the neural data? Demonstrating that the LSTM representations are too idiosyncratic or entangled to do such a thing would strengthen the argument in the discussion that "...such predictions would not be easily possible from a pure systems identification approach."