
The best way of looking at the brain is from within - rbanffy
https://www.economist.com/news/technology-quarterly/21733195-hunt-smaller-safer-and-smarter-brain-implants-best-way-looking?fsrc=scn/tw/te/bl/ed/thebestwayoflookingatthebrainisfromwithininsideintelligence
======
tbenst
The challenge isn’t just the hardware. Software in neuroscience is in an
exceptionally sorry state.

As I write, I have a job running to infer the number of neurons and their
spike trains from a recording done with 4096 electrodes. Sampling of voltage
is done at 18kHz, and the big challenge is dealing with spikes that are
observed from multiple electrodes. One approach to this is to detect spikes by
a threshold and convert to a 5ms voltage vector, run PCA over these vectors,
construct template waveforms for each cluster, and then merge similar clusters
over neighboring electrodes.

It will take >24 hours to just extract the spike trains from a 1 hr recording,
and that’s not including any interesting analysis. Fortunately I can run this
on a few dozen nodes and start approaching real-time, but we are quite far off
from being able to handle these data firehoses at the rate necessary for BCI.

~~~
kkylin
I don't know much about BCI, but I'd imagine not all applications require
spike sorting
([http://www.scholarpedia.org/article/Spike_sorting](http://www.scholarpedia.org/article/Spike_sorting)
for those who are unfamiliar with this), though of course if one could spike-
sort in close to real time, one could do a lot more.

In any case, I absolutely agree with you -- at the algorithmic and
implementation level, there are lots of nice problems in making sense of
neuronal recordings.

------
etrautmann
This is a well researched and well articulated piece that largely gets it
right regarding the importance of recording intracortical signals for neural
interfaces. These address the significant limitations introduced by non-
invasive methods like EEG.

