Variation in the neural code between individuals contributes to making each person unique. Using [~]100 neural population recordings from major ganglion cell types in the macaque retina, we develop an interpretable computational representation of individual variability using machine learning. This representation preserves invariances, such as asymmetries between ON and OFF cells, while capturing individual variation and covariation in properties such as nonlinearity, temporal dynamics, and spatial receptive field size. The similarity of these properties across cell types was dependent on the similarity of their synaptic connections. Surprisingly, male retinas exhibited higher firing rates and faster temporal integration than female retinas. By exploiting data from previously recorded macaque retinas, a new macaque retina (and crucially, a human retina) could be efficiently characterized. Simulations indicated that combining a vast dataset of healthy macaque recordings with behavioral feedback could be used to identify the neural code and improve retinal implants for treating blindness.
bioRxiv Subject Collection: Neuroscience