BRAIN Initiative

Some of those are leveraging AI and ML to discover new antibodies.

We're part of the NIH's Brain Initiative to sort of map the brain.

And we're working on ways to improve AAV productivity to bring the cost down and the complexity of

AAVs.

The goal is to build a model, test an idea, analyze the neural activity, and find associated patterns to ask better questions.

The National Institutes of Health's Brain Initiative describes computational and quantitative approaches as an important part of understanding how the neural circuits encode, process, and transmit information.

And here's something I love about this career.

There are several doorways into it.

But it does ask the question, can this model help us understand something about the real brain? The question is at the heart of computational neuroscience.

Computational neuroscience uses mathematical and computational approaches to understand how the nervous system processes information.

The National Institutes of Health's BRAIN Initiative has identified quantitative approaches, including theory, modeling, statistics, and computation as critical to understanding increasingly complex neuroscience data.

Every day the field continues to evolve.

Mm-hmm

... uh, done incredible work in this space with the Brain Initiative.

I mean, some of those, the atlases of, of the human and mouse brain are absolutely incredible.

One thing that's nice about the brain is, is neurons have this other form of DNA methylation.

Again, ahead of the curve, before this all happens, so that when the technology reaches us, we have at least some legal protection.

Just finally, Raphael, and this has been fascinating talking to you, but I wonder if there's a tension in the centre of your life here, your career, because you helped develop the proposal that became the US Brain Initiative.

You're at the cutting edge of this science designed to dramatically improve our ability to understand the brain, already dramatically improving some people's lives and capacities, the people... as you say, who are paralysed, who have locked-in syndrome.

These are great medical breakthroughs.

But we didn't know fundamentally much more than that.

So that was one goal, and once we knew the cell types, then we could bring in tools like optogenetics and say, okay, what happens if you turn up or turn down this cell type in this context? And that was one, probably the first major goal of the BRAIN Initiative, was to get that parts list, and then once we had the parts list, to start bringing the optogenetic tools, which we'd been developing along the way, to bear on those parts.

So I know that one of the things that they talk about and that you talk about, you mentioned it even earlier in our conversation, is this idea of causality.

That perhaps too often people were looking at the correlation of certain phenomenon in the brain to certain behaviors or certain illnesses.

So one thing that strikes me is that that means that there is an almost infinite number of combinations of cells that you could look at.

So do we need or do we have a theory for which cells actually should be working together so that our search space of which combinations can be brought into a manageable range?

So this is very, very important and this was another key pillar of the BRAIN Initiative because we realized this issue that very clearly we needed theory guidance to deal with this combinatorial explosion you're alluding to.

And it hadn't been as serious a problem before because we didn't have the technological abilities to make it a problem.