After 7 years, thrilled to finally share our #MICrONS functional connectomics results! We recorded activity from ~75K neurons in visual cortex in a single mouse, then mapped its wiring using electron microscopy. To systematically characterize neuron function, we built the first foundation model of the mouse visual cortex—trained via deep learning on data pooled from multiple mice and visual cortical areas. Our foundation model generalized to new neurons, animals, and even unseen stimulus domains. It also accurately predicted entirely new modalities, such as anatomically defined cell types. Importantly, this robust generalization enabled us to create accurate functional digital twins of individual mouse brains. Using the digital twin of the MICrONS mouse—where we knew the exact neuronal wiring—we discovered that neurons don’t connect randomly, even when anatomically positioned to do so. Instead, given multiple potential partners (axons near dendrites), neurons preferentially choose partners with similar feature selectivity (“what”) rather than receptive field overlap (“where”). Foundation models offer a powerful approach to systematically decode the neural code of intelligence. Huge thanks to @IARPAnews for funding this groundbreaking effort through the @BRAINinitiative, and to our amazing team at @Stanford @StanfordMed @bcmhouston, @Allen, @Princeton, @uniGoettingen and others! #Neuroscience #MICrONS #NeuroAI #Connectomics #FoundationModels #AI nature.com/immersive/d42859-…

Following the news that eLife will not receive an Impact Factor in 2025, we’ve shared an update on how our model is doing since we were first placed “on hold” by Web of Science, and what we’re up to now. Find out more. elifesciences.org/inside-eli…

The portal is open: Our #ELLISPhD Program is now accepting applications! Apply by November 15 to work with leading #AI labs across Europe and choose your advisors among 200 top #machinelearning researchers! #JoinELLISforEurope #PhD #PhDProgram #ML ellis.eu/news/ellis-phd-prog…

Happy to share our work on modeling V4 and V1 single-cell responses to natural images with CNNs trained on multiple computer vision tasks. biorxiv.org/content/10.1101/… Work with @KonstantinWille, @kelli_restivo, @DenfieldGeorge, @sinzlab, @bethgelab, @AToliasLab, @alxecker.