[raindeer3]

Yes, but also in hebbian learning you must have some weakening of weights, otherwise the weights would just grow indefinitely? One example I guess is Oja's rule. The difference to bp is just how to select what weights to strengthen and what to weaken based on what information. Forgetting and learning must always happen one way or the other. Or am I not getting your point?

[subroutine]

I agree. Above I mention that neurons have finite resources so synaptic strength is essentially zero-sum. When a new set of synapses becomes strengthened, it implies that all the other synapses must be weakened by some amount.

Here is a nice animation of signal propagation in biological neural nets:

https://youtu.be/WCqNn9PEELw

To simulate the dynamics of synaptic strength I created 3D mesh of a dendrite segment with several synapses...

https://youtu.be/tDKUU0SqbSA

Then I simulate the diffusion of AMPA receptors on the surface (the number of AMPAR in a synapse is proportional to its strength)...

https://youtu.be/6ZNnBGgea0Y

I don't have animations of this process but you can imagine what happens when one synapse holds onto receptors longer than the others (has a reduced particle diffusion rate), when there are a finite number of receptors

[hos234]

Maybe Glial cells are playing a role - https://www.sciencedaily.com/releases/2018/06/180618222427.h...

[subroutine]

> In cell culture studies, they added neurons to astrocytes that overexpressed ephrin-B1 and were able to see synapse removal, with the astrocytes "eating up" the synapses... "We think that astrocytes expressing too much of ephrin-B1 can attack neurons and remove synapses"

Yeah that would be a problem. Maybe Glial cells play a role, maybe they don't, opinions vary. https://streamable.com/1vi7r