PL Neuro Salon

What are possible targets for large-scale experimental designs?

What naturalistic settings does it make sense to explore first?

Where are opportunities for studying longitudinal learning?

How can we construct adversarial benchmarks for AI?

Do we really need to change what we are doing?

“Current hardware is good enough, let’s scale!” — how do we scale current neural interfaces?

“Current hardware is insufficient, let’s build!” — what new neural interfaces do we need to start building?

Less invasive neural interfaces are the only path to truly scalable human data sets.

Do we ever need a gigabit-per-second BCI?

How do we deal with petabyte human data sets?

Modeling: What modeling and theory frameworks are we missing? Can we reverse engineer the brain's algorithmic structure from data? What interventions might we need?

Applications: If we could build a faithful emulation of the brain, what could we do with it?

Learning and Memory: How do time, memory, and development shape intelligence, and what does it mean to model a brain that learns across a lifetime?

Interpretation: What will it mean to build an “interpretable” model of the brain or neural circuits?

Uniquely Human: What are the biggest questions we need to focus on to model aspects of intelligence that are uniquely human? What are the major barriers?

If rebuilt from scratch, how must the neuro R&D ecosystem be structured to drive rapid progress?

What incentives and capital deployment approaches are required to drive rapid progress?

What innovations in scientific practice or capabilities are needed to drive rapid progress?

How must we redesign the training ecosystem to produce scientists and entrepreneurs that are empowered to drive rapid progress?

What proactive steps are required to ensure a utopic – as opposed to dystopic – future world with advanced neurotechnologies?