Together with Fabian Sinz, we developed a deep recurrent neural network for predicting the activity of thousands of mouse V1 neurons simultaneously recorded with two-photon microscopy, while accounting for confounding factors such as the animal’s gaze position and brain state changes related to running state and pupil dilation. We investigated how well this large-scale model generalizes to stimulus statistics it was not trained on. While our model trained on natural movies can correctly predict some neural tuning properties in responses to artificial noise stimuli, unadapted transfer is not perfect. However, it can fully generalize from movies to noise and maintain high predictive performance on both stimulus domains by fine-tuning only the final layer’s weights. Check out the preprint on bioRxiv.