3 ms·

Real cells use Hebbian learning, which in some cases is equivalent to back-propagation, but is way less efficient. Otherwise, yes, many of the techniques used i

by tormeh 8y ago

Real cells use Hebbian learning, which in some cases is equivalent to back-propagation, but is way less efficient. Otherwise, yes, many of the techniques used in ML is also used in the body. Not only ML, actually, but also electrical engineering and surely many other fields. The more you learn about the body the more machine-like it will look to you. In some respects. In others, it's fucking space technology. Nanobots exist, they are called proteins, and each of your cells have hordes of them, for example.

stochastic_monk 8y ago
The big difference is that biological neurons emit binary outputs. Because of this, there’s no gradient and they can’t be trained by SGD.
- danharaj 8y ago
  There's phase and frequency information in neural spike trains.
- yorwba 8y ago
  But biological neurons are also stochastic, and the firing probabilities are continuous, so you can do SGD on them.
traject_ 8y ago
Well, I think it may not just be Hebbian learning but likely Balanced Amplification learning [1] as well which propagates quite a bit faster. Promising paper came out recently echoing this phenomena globally with a model of visual perception in the macaque brain[2]. [1] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2667957/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2667957/ [2] https://www.cell.com/neuron/fulltext/S0896-6273(18)30152-1 https://www.cell.com/neuron/fulltext/S0896-6273(18)30152-1?