Over the past couple of years there have been a number of new neurotechnology hardware platforms developed, from semi-invasive to fully invasive (Neuralink having recently reached their first human implantation), with capabilities for everything from high bandwidth interfacing with the retina to whole brain imaging to targeted neuromodulation. While this is incredibly exciting for the field, there's still a group of people with a potential to impact it who have almost no good routes for contributing: early-career software engineers.
I've met a number of ambitious young engineers with primarily software skills who want to either start a company in neurotech, build a portfolio project to demonstrate ability and interest in the area, or just make something cool using brain signals. There is basically one option available to them, which is consumer EEG (or in some cases surface EMG, which is a fairly indirect method of collecting brain activity), and it's a really limited one. Even research-grade EEGs, with hundreds of contacts in controlled lab conditions top out at a few cm of spatial resolution (10s of thousands of neurons) and are only reliable for collecting cortical data. There have been some impressive demos using EEG for control but studying the deep brain, getting high bandwidth input and output for high fidelity computer control, discovering new modalities for interfacing with AI, all would either require or greatly benefit from new hardware. The hardware is both years away and likely to come more and more from companies without open API access.
In the meantime, I think it would be extremely valuable to make simulation environments more available to outside contributors, which was the inspiration behind NDK, the Neurotech Development Kit, which I worked on with AE Studio, Milan Cvitkovic and Sumner Norman. Developing hardware and experimental design within neurotech companies often starts with or heavily uses simulation, and well-documented and open packages allow anyone with software skills to contribute to the space. Furthermore, given the speed of iteration in simulation, I could imagine these packages having an impact on the state of the art similar to what we've seen with robotics and reinforcement learning in the past, where simulation is a key part of making faster progress. In fact, part of the original impetus for NDK came from the success of the OpenAI gym in offering a standard environment to benchmark RL algorithms, which greatly accelerated the field.
I think some of the most impactful future neurotech devices will be completely noninvasive, and the first use case for NDK is for modeling transcranial ultrasound for neuromodulation. It's open for contributions to anyone who wants to work on neurotech but doesn't have a lab or the hardware, and we hope to see what the world can build with it!