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 đź§
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⚡️
â–¸ Retinal inputs to the SC form functionally distinct, laminar-organized response types, identified via Gaussian mixture model clustering
â–¸ 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
đź“„ 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 🙌