Every experimental neuroscientist knows the feeling: you have a hypothesis, but running the experiment takes months👩‍🔬 In our new preprint @biorxiv_neursci , we present an openly available functional ''digital twin'' of the retinal input to the mouse superior colliculus that lets you test hypotheses in the model first — try it out yourself using the link below 🧠 We combined chronic two-photon imaging of >200k retinal ganglion cell axonal boutons in the mouse superior colliculus (SC) with deep dynamic models that predict neural responses to parametric light stimuli and natural movies. Key findings⚡️ ▸ Retinal inputs to the SC form functionally distinct, laminar-organized response types, identified via Gaussian mixture model clustering ▸ The functional diversity of retinal output matches that of retinal input to the SC. We show this by aligning our dataset with a retinal reference dataset using a variational autoencoder with adversarial training ▸ Our deep dynamic digital twin learns stimulus–response transformations and generalizes to stimuli it was never trained on, including parametric stimuli used for cell type identification The model functions as a virtual lab bench: feed in any stimulus you're curious about and generate predicted neural responses. As a proof of concept, we fed in a looming stimulus — known to trigger defensive behavior in mice — and identified putative response types selective for this stimulus Try it in our Colab notebook with your own stimulus and see what the model predicts 📄 Preprint: biorxiv.org/content/10.64898…💻 Colab: colab.research.google.com/dr…📂 Code: github.com/yongrong-qiu/reti… Huge thanks to an incredible cross-institutional team spanning @StanfordMed , @uktuebingen , @uniGoettingen , @bcm_neurosci & many more @YongrongQ, @lisa_schmors, Na Zhou, Mels Akhmetali, Dominic Gonschorek, Cameron Smith, Anton Sumser, Marie Vallens, @crcadwell, Fabrizio Gabbiani, Maximilian Joesch, @AToliasLab, Philipp Berens, Thomas Euler, @sinzlab, @viajake 🙌