{"slug": "cross-species-representation-learning-aligns-mouse-and-human-neural-dynamics", "title": "Cross-species representation learning aligns mouse and human neural dynamics", "summary": "A dual-rule contrastive learning framework aligned mouse and human neural dynamics and retrospectively tracked known clinical efficacy across ten model-drug combinations, including a disease-specific detrimental effect, according to an arXiv paper submitted 8 October 2026 by Gabriel Ocana Santero and co-authors. The framework recovered conserved sensory-response structure across species, resolved distinct relationships between three mouse epilepsy models and heterogeneous human patient populations, and identified shared disease-associated dynamics between Fmr1-knockout mice and human 16p11.2 copy-number variant carriers despite differing genetic aetiology and recording modality. The authors present the approach as a way to map heterogeneous human disease onto experimentally tractable preclinical states and assess whether interventions restore human-relevant circuit function.", "body_md": "# Quantitative Biology > Quantitative Methods\n\n  [Submitted on 8 Oct 2026]\n\n# Title:Cross-species representation learning aligns mouse and human neural dynamics and tracks clinical drug efficacy\n\n[View PDF](https://arxiv.org/pdf/2610.11222)\n\n[HTML (experimental)](https://arxiv.org/html/2610.11222v1)\n\nAbstract:Preclinical models poorly predict human drug efficacy, particularly in neurological disorders. Neural activity offers a uniquely rich source of translational information because it captures high-dimensional variation in nervous-system function that can be measured in both animals and humans. However, its high dimensionality makes it difficult to distinguish conserved disease-related features from variation arising from species, recording modality and experimental context. Here, we test whether shared neural dynamics can be identified directly from electrophysiology data by learning representations organized by biological state rather than species. We develop a dual-rule contrastive learning framework that aligns corresponding mouse and human states while preserving separation between distinct phenotypes. This framework recovered conserved sensory-response structure across species and, in epilepsy, resolved distinct relationships between three mouse models and heterogeneous human patient populations. When treated animals were projected into a frozen cross-species representation, drug-induced movement towards the human-aligned healthy state retrospectively tracked known clinical efficacy across ten model-drug combinations including a disease-specific detrimental effect. The framework also identified shared disease-associated neural dynamics between Fmr1-knockout mice and human 16p11.2 copy-number variant carriers despite differences in genetic aetiology and recording modality. Together, these findings show the potential of cross-species neural representation learning to map heterogeneous human disease onto experimentally tractable preclinical states and assess whether interventions restore human-relevant circuit function.\n    \n\n## Submission history\n\nFrom: Gabriel Ocana Santero [\n[view email](https://arxiv.org/show-email/dc9ada7f/2610.11222)]\n\n**[v1]** Thu, 8 Oct 2026 04:19:41 UTC (6,134 KB)\n\n### Current browse context:\n\nq-bio.QM\n\n### References & Citations\n\nLoading...\n\n# Bibliographic and Citation Tools\n\nBibliographic Explorer \n\n*(*[What is the Explorer?](https://info.arxiv.org/labs/showcase.html#arxiv-bibliographic-explorer))\nConnected Papers \n\n*(*[What is Connected Papers?](https://www.connectedpapers.com/about))\nLitmaps \n\n*(*[What is Litmaps?](https://www.litmaps.co/))\nscite Smart Citations \n\n*(*[What are Smart Citations?](https://www.scite.ai/))\n# Code, Data and Media Associated with this Article\n\nalphaXiv \n\n*(*[What is alphaXiv?](https://alphaxiv.org/))\nCatalyzeX Code Finder for Papers \n\n*(*[What is CatalyzeX?](https://www.catalyzex.com))\nDagsHub \n\n*(*[What is DagsHub?](https://dagshub.com/))\nGotit.pub \n\n*(*[What is GotitPub?](http://gotit.pub/faq))\nHugging Face \n\n*(*[What is Huggingface?](https://huggingface.co/huggingface))\nScienceCast \n\n*(*[What is ScienceCast?](https://sciencecast.org/welcome))\n# Demos\n\n# Recommenders and Search Tools\n\nInfluence Flower \n\n*(*[What are Influence Flowers?](https://influencemap.cmlab.dev/))\nCORE Recommender \n\n*(*[What is CORE?](https://core.ac.uk/services/recommender))\n# arXivLabs: experimental projects with community collaborators\n\narXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.\n\nBoth individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.\n\nHave an idea for a project that will add value for arXiv's community? [**Learn more about arXivLabs**](https://info.arxiv.org/labs/index.html).", "url": "https://wpnews.pro/news/cross-species-representation-learning-aligns-mouse-and-human-neural-dynamics", "canonical_source": "https://arxiv.org/abs/2610.11222", "published_at": "2026-10-29 20:35:45+00:00", "updated_at": "2026-10-29 20:46:54.151310+00:00", "lang": "en", "topics": ["machine-learning", "ai-research", "neural-networks", "artificial-intelligence"], "entities": ["arXiv", "Gabriel Ocana Santero", "Fmr1", "16p11.2"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/cross-species-representation-learning-aligns-mouse-and-human-neural-dynamics", "markdown": "https://wpnews.pro/news/cross-species-representation-learning-aligns-mouse-and-human-neural-dynamics.md", "text": "https://wpnews.pro/news/cross-species-representation-learning-aligns-mouse-and-human-neural-dynamics.txt", "jsonld": "https://wpnews.pro/news/cross-species-representation-learning-aligns-mouse-and-human-neural-dynamics.jsonld"}}