3 ms·
What I find interesting about this finding: I wonder if intra-dentrite vs. inter-dentrite vs. inter-neuronal learning has different learning rates, activation f
by GolDDranks 7y ago
What I find interesting about this finding: I wonder if intra-dentrite vs. inter-dentrite vs. inter-neuronal learning has different learning rates, activation functions etc. If that's the case, maybe artifical networks could benefit such variety.
- raverbashing 7y agoMore than the computational aspects of neurons (which might be abstracted computationally), the learning process is something that seems it's still more of a mystery. The brain doesn't backpropagate. So how do you do it?
- im3w1l 7y agoWell part of it is local unsupervised hebbian learning, fire-together-wire-together, as it's succinctly put. In particular, neurons will try to fire more like upstream neurons and less like downstream neurons as determined by time-delay in correlation. Then there is also the brains reward center, the one that famously goes wrong when you get addicted to drugs. It releases chemicals which reinforce behavior. And finally some parts are "hard-coded" to be a certain way our DNA.
- im3w1l 7y agoBrains are a lot more complex than artificial networks in many ways. We know that there are many types of neurons and they use different types of neurotransmitter. and have different connectivity patterns. So yeah even without intra-dendrite learning there are biological justifications for what you propose.