A am interested in physiologic neural network. Altough there are some opposite views, most probably there seems to be no plausible way to explain a physiologic backpropagation in the brain.
So I am trying to code a neural network without backpropagation yet my mathematical understanding is inadaquate, so I want to ask folowing simple question:
“If we do have only one node at the right side of the network, accepting that the inputs are on the left, can we train the network without backpropoagation and using the mean of weights instead? As we would know the y for all x it should be possible to calculate the mean w?”
The idea is that the system should work continiously, and train continiously for one class, until the trained network decides that the input is different from the known previously trained classes. And if it is different, it should create and train for that new class of inputs. I think that should be the working system of the brain, and as the cortex has similar cells, mathematically it also must be that easy?
But where is my flaw (with simplified math please :))
function getClass(input) {return "class 0";}
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