[Balgair]

I've written multi-tetrode cluster analysis SW. It is very much not simple. Deconvolving all the inputs is terribly difficult and computationally expensive. Generally we had undergrads as classifiers for the clustering ML, which never really worked all that well, TBH. The higher the sampling frequency, the better the spatiotemporal resolution, but the more data to work through. When you do get a cluster, the overlaps are very close together and it really is hard to say that they are 2 neurons. It takes a lot of data/time to be certain.

One thing to remember is that noise is a huge problem with all this. Not just the 'normal' electronic sources (how do you ground a brain well? It's intentionally evolved to be nothing but loops!), but the neuronal too. Neurons will often fire just because (or not), they will jiggle with animal movement, they will move with heartbeat, and they will die off or move on their own, the electrode dances about, the brain attacks the electrodes, some portion of the electrode snaps off, etc. It is a very hard thing to manage well.

Sorry, bad 'memories' of those projects.