From fb595966ef9a3124035056d0fbca6c8246673f40 Mon Sep 17 00:00:00 2001 From: Claude Date: Mon, 9 Nov 2026 12:17:10 +0000 Subject: [PATCH] Use canonical NeurIPS URL for research-page paper link Replace the legacy proceedings.nips.cc PDF link (which redirects twice) with the canonical proceedings.neurips.cc abstract page, matching the URL publications.yaml already uses for the same paper. Co-Authored-By: Claude Opus 5.5 Claude-Session: https://claude.ai/code/session_01BqmrqnVhSnHv9mzFEXhW6S --- _data/research.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/_data/research.yml b/_data/research.yml index d687aab..66965ef 100644 --- a/_data/research.yml +++ b/_data/research.yml @@ -34,7 +34,7 @@ model, and manipulate brain activity, with [Eva Naumann's lab](https://www.neuro.duke.edu/research/faculty-labs/naumann-lab). body: | - Together with [Eva Naumann's](https://www.neuro.duke.edu/research/faculty-labs/naumann-lab) lab, we've developed *[improv](https://github.com/project-improv/improv)* ([paper](https://www.biorxiv.org/content/10.1101/2021.02.22.432006v1)), a software platform for designing and orchestrating adaptive experiments. By analyzing data in real time, we can measure, model, and manipulate neural activity in response to new data. We've shown how these tools, in conjunction with holographic photostimulation, could in principle map functional connectivity of large circuits in a few hours ([paper](https://proceedings.nips.cc/paper/2020/file/531d29a813ef9471aad0a5558d449a73-Paper.pdf), [expanded version](https://arxiv.org/abs/2007.13911)). More recently, we've worked on methods for fast dimensionality reduction and modeling of neural populations in real time ([paper](https://arxiv.org/abs/2108.13941)). + Together with [Eva Naumann's](https://www.neuro.duke.edu/research/faculty-labs/naumann-lab) lab, we've developed *[improv](https://github.com/project-improv/improv)* ([paper](https://www.biorxiv.org/content/10.1101/2021.02.22.432006v1)), a software platform for designing and orchestrating adaptive experiments. By analyzing data in real time, we can measure, model, and manipulate neural activity in response to new data. We've shown how these tools, in conjunction with holographic photostimulation, could in principle map functional connectivity of large circuits in a few hours ([paper](https://proceedings.neurips.cc/paper_files/paper/2020/hash/531d29a813ef9471aad0a5558d449a73-Abstract.html), [expanded version](https://arxiv.org/abs/2007.13911)). More recently, we've worked on methods for fast dimensionality reduction and modeling of neural populations in real time ([paper](https://arxiv.org/abs/2108.13941)).
Closed-loop pipeline concept