Appendix details

A.1 Linear mappings between zand x Usually, we have data x PRNˆD1 and latent representation z PRNˆD2 with N the number of neurons, D1 the dimensionality of the data, D2 the dimensionality of the latent space and, usually, D1 " D2. In cases where a method mdoes only produce some latent representation zm, we fit a reconstruction ˆxm "Wzm with a least squares projection W "pzTmzmq 1zTmx. In cases where a method mdoes only produce some reconstruction ˆxm, we produce a simple latent representation zm by extracting the first D2 columns of the left singular vectors U from the singular value decomposition x"USVT. Both of these projections are fitted on the training data, then fixed and also used on the validation and test data. We used three datasets, where the first two (dataset A [2] n=8417 cells; B [54] n=4600) are two-photon recordings of mouse retinal bipolar cell (BC) responses to the chirp stimuli (local and full-field, see [2] for details).