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).

The red lines in the bottom plot indicate linear fits and the red axis labels show the rank correlation coefficients ρ and p values. The matrix is orthogonal, thus avoiding a singular design. As scGen returns corrected input data, we performed PCA on the output data, which were used for further evaluation (cf. Appendix Section A.1). Here, we used the same number of principle components (PCs) as used for Embedded cells are colored by dataset. In Figure 9, we present the results of the simulation experiments discussed in the main text.

Integrating data from multiple experiments is common practice in systems neuroscience but it requires inter-experimental variability to be negligible compared to the biological signal of interest. This requirement is rarely fulfilled; systematic changes between experiments can drastically affect the outcome of complex analysis pipelines. Modern machine learning approaches designed to adapt models across multiple data domains offer flexible ways of removing inter-experimental variability where classical statistical methods often fail. While applications of these methods have been mostly limited to single-cell genomics, in this work, we develop a theoretical framework for domain adaptation in systems neuroscience. We implement this in an adversarial optimization scheme that removes inter-experimental variability while preserving the biological signal.

Integrating data from multiple experiments is common practice in systems neuroscience but it requires inter-experimental variability to be negligible compared to the biological signal of interest. This requirement is rarely fulfilled; systematic changes between experiments can drastically affect the outcome of complex analysis pipelines. Modern machine learning approaches designed to adapt models across multiple data domains offer flexible ways of removing inter-experimental variability where classical statistical methods often fail. While applications of these methods have been mostly limited to single-cell genomics, in this work, we develop a theoretical framework for domain adaptation in systems neuroscience. We implement this in an adversarial optimization scheme that removes inter-experimental variability while preserving the biological signal.