Popular Science  has reported a tidbit of information: Marc Christensen’s team at SMU is supposed to start testing if they can stimulate a rat’s leg with optical fibers.
This is the same DARPA-funded project I mentioned last September in my article “Softer, Better, Faster, Stronger” . DARPA held a related “Reliable Neural Interface Technology (RE-NET)” workshop back in 2009 :
A well-meaning motor prosthesis with even 90% reliability, such as a prosthetic leg that fails once every 10 steps, would quickly be traded for a less capable but more reliable alternative (e.g., a wheelchair). The functionality of any viable prostheses using recorded neural signals must be maintained while the patient is engaged in or has their attention directed to unrelated activities (e.g., moving, talking, eating, etc.). Since the neural-prosthesis-research community has yet to demonstrate the control of even a simple 1-bit switch with a long-term high level of speed and reliability, the success of more ambitious goals (e.g., artificial limbs) are placed in doubt.
DARPA is interested in identifying the specific fundamental challenges preventing clinical deployment of Reliable Neural Technology (RE-NET), where new agency funding might be able to advance neural-interface technology, thus facilitating its great potential to enhance the recovery of our injured servicemembers and assist them in returning to active duty.
Some of the challenges listed for the optical (neurophotonic sensing) approach are :
- Transduce action potential into optically measurable quantity
- Modes: ionic concentration / flux vs. electromagnetic field
- Field Overlap
- Can’t go straight from voltage (indirect detection)
- Sensitivity, Parallelism
- Packaging, Size
- “What is the minimum level of control-signal information required to recover a range of activities of daily living in both military and civilian situations?”
- “Need a method for characterizing tissue near implant to better understand long term degradation.”
Some of those challenges probably apply to all forms of neuro sensing. Likewise, the metrics for neurophotonic interfaces—resolution, signal-to-noise ratio, and density—probably apply to other methods as well.
The Need for Better Neural Interfaces
Maybe the neurophotonic approach won’t work in the end, or it will only work in combination with another method. Whatever the case, a lot of money should be put into this kind of project. We are in desperate need for more advanced neural interfaces. As Dr. Principe of the University of Florida writes :
Just Picture yourself being blindfolded in a noisy and cluttered night club that you need to navigate by receiving a voice command once a second…And you will understand the problem faced by engineers designing a BMI [Brain Machine Interface].
Present systems are signal translators and will not be the blue print for clinical applications. Current decoding methods use kinematic training signals – not available in the paralyzed. I/O models cannot contend with new environments without retraining. BMIs should not be simply a passive decoder – incorporate cognitive abilities of the user.
Interfaces to the nervous systems are the key enablers for all of future prosthetics—and of course other exotic devices that don’t even exist yet. Without overcoming this interface hurdle, we’ll be stuck in the stone age of prosthetics and nervous system repair.
 M. Peck, “Talk To The Hand: A New Interface For Bionic Limbs,” Popular Science, Feb 24, 2011.
 J.W. Judy & M.B. Wolfson, RE-NET website.
 “Softer, Better, Faster, Stronger: The Coming of Soft Cybernetics,” H+ Magazine, Sept 21, 2010.
 M.P. Christensen, “Neuro-photonic Sensing: Possibilities & Directions”, DARPA RE-NET Workshop, Nov 19, 2009.
 Optical Breakout Session Report, DARPA RE-NET Workshop, Nov 20, 2009.
 J.C. Principe, “Architectures for Brain-Machine Interfaces,” DARPA RE-NET Workshop, Nov 19, 2009.
 Rajeev Doshi, PopSci
 DARPA / CIPhER via Physorg
 scan of book cover, art by John Berkey