Diving Into Neuroprosthetics
For the past 7 months, I explored the improvement of current assistive mobility devices.
Last month, with the freedom provided by the $100K Thiel fellowship, I realized that I had the opportunity to work on projects with higher technical risk.
This understanding lead me to dive into neuroprosthetic research, driven by the goal of understanding the human brain and processing coupled with the goal to create neuroprosthetics with the control and dexterity comparable to natural movements and lifetime usability.
I started with the convergence analysis of common decoder algorithms.
As I examined decoder optimizations to deal with the noisy data sets collected from most sensor arrays, I realized that the low resolution sensing methods currently employed are a barrier to implementing optimally functional and elegant algorithmic solutions.
With respect to accurate sensing for accurate neuroprosthetics, optical recording seems to be the superior alternative to an electrode array. Reading through the publications of the Synthetic Neurobiology Group at MIT sparked a crazy idea.
Neuroprosthetic Application of Sensing Hardware
Using accurate 3D images which capture the state of a sparse set of synapses to train control signal classifiers to automatically learn the structural connections that create natural movement is a huge leap to achieving the goal of optimally functional prosthetics.
Accurate sensing extends far beyond prosthetics by advancing medical imaging for diagnosis, monitoring and research!
tl;dr Place sensors in synapses to identify the 3D coordinates of firing synapses.
Semi-permanent localized deposition of florescent voltage sensor to neuromuscular junction