We convert lab data to NWB and publish it on the DANDI Archive, so that funder data sharing requirements are met with files other researchers can reuse. We also maintain the tools the standard depends on: NeuroConv, NWB Inspector, and NWB GUIDE.
We bridge the gap between academic research and professional software engineering. Our team combines deep neuroscience domain expertise with production-grade development practices, delivering solutions that are both scientifically rigorous and built to last.
Our engineers have published research and understand the nuances of neurophysiology data.
No vendor lock-in. Every tool we build is open source and belongs to the community.
Professional testing, documentation, and long-term maintainability. Not just research prototypes.
CatalystNeuro has worked successfully with diverse scientific research groups in a variety of capacities. All work we do is open source, so our team can fit seamlessly with research groups without worry of expensive licensing fees or vendor lock-in.
Convert your data to Neurodata Without Borders (NWB) and publish on the DANDI Archive.
Integrate existing analysis, visualization, and data management tools with open data via NWB and DANDI.
Apply language models and machine learning to neurophysiology data, built on the same open standards.
Working with the team at CatalystNeuro was a terrific, highly productive experience. They revamped a bespoke volumetric imaging pipeline we had developed, integrated it with our behavioral data, and made it all compatible with NWB. My lab has already used their CatalystNeuro pipeline in publication, and will undoubtedly find their NWB toolkit of great use in the future.
NINDS has awarded us a five-year R25 grant for the NeuroData AI Summer School. After running NeuroDataReHack on year-to-year funding since 2022, we can finally plan for the long term. The program continues as the NeuroData AI Summer School, with its own site at neurodata-ai.org and applications for 2027 open now.
A given level of LLM intelligence costs about a thirtieth of what it did a year ago, and the rate of decline is accelerating. Using Artificial Analysis's measured cost per task, this post traces the falling price of each capability level, explains why cheaper intelligence leads to more spending on it, not less (Jevons paradox), and considers what changes when the drop reaches 100x.
NeuroConv 0.10.0 writes events as the NWBEP001 event tables, grows fiber photometry from one supported format to five, introduces metadata templates and annotation guides for optical physiology, and ships new session-wide converters and formats.
Book a free 30-minute call and we'll map out the fastest path to standardized, shareable, reusable data.
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