NeuroStorm: Proposing a global platform for neuroscience

A recent Neuron Neuroview highlighted a new initiative to develop a platform for globalized neuroscience, entitled NeuroStorm. This initiative aims to build a unified computational ecosystem to accelerate global neuroscience discovery, in particular, via easing the process linking data to models to experiments.

This initiative was spawned from a series of workshops. In the first, Global Brain Hack 2016 hosted at Johns Hopkins University, participants worked together to highlight the three “Grand Challenges” of modern brain science, and suggest ways in which community members can get involved. The resulting manuscript summarizes these challenges, and encourages participation through the #NeuroStorm tag, here on Neurostars!

Please don’t hesitate to reach out with ideas or questions, and keep an eye on the #NeuroStorm tag if you’d like to hear more!

You missed a spot in fig 2! Language.
You are assuming that the language that neuroscientists use is consistent and uniform…which is a bad assumption. In fact we have made the argument that the only thing that unifies neuroscientists is the language. We have no common technique, no common critter, not even the brain or nervous system as some of us study algorithms or genes.

Many of the things you talk about are instantiated in NIF, neuroscience information framework (, a cloud based platform that deals with neuro-data. Clearly we have been lacking the education component.

…so to resolve some of that…

We have an upcoming neuro-tools webinar series that I would like to invite you to as soon as we have a webpage up.

Hi Anita!

Thanks so much for your feedback! I think you’re totally right, in that:

a- we missed explicitly stating language as a property, and
b- many tools that work towards this platform already happen to exist

To address (at least my understanding of) your concerns and how they fit into what we were discussing and proposing, I want to clarify each of these points.

explicitly stating language as a property
Though we didn’t explicitly state language, and I agree we should have, I don’t think that it flew under the radar in terms of things we were considering. Rather, each of the 4 technical components we propose - Data, Infrastructure, Apps, and Algorithms - are quite different from one another, and should be responsible for defining and unifying their own language, and propagating that forward through Education. For instance, many data-collectors working in electron microscopy don’t need to be involved in designing the terminology regarding computational infrastructure, though once it is finalized that lexicon should certainly be shared. It would’ve been more clear to state this directly in the manuscript, but hopefully this amendment will help.

many tools solving these problems already exist
Agreed - and we count on that! I don’t think we propose re-inventing the wheel, or even the development of any new “backbone” or core functionality, as there are more than enough tools to cover the features this type of a platform would need. Where these tools may currently fall short is from the perspective of unification - linking/interoperability through common APIs or portal design limits the ability that all of these tools can be brought together in their current state. Development is at least 90% of the way there when we look at off-the-shelf tools in the space, but it’s that last 10% of development that would be required for such a unified platform or ecosystem to powerfully leverage all of these independent pieces in a way that appears seamless to users.

Hopefully this addresses your concerns, at least somewhat, but please let me know if that is not the case and I’d very happily chat about it more.

P.s. the webinar series sounds amazing, looking forward to it.

Hey, thanks for the note!
Yes individual communities should definitely define their own terminologies, but neuroscience also has a shared terminology which defines the filed: brain regions, cells, etc. These terms should be shared so that when imagining people and microscopists talk then the ambiguity in their conversation is as small as possible.