Is there some of Hebb's rule behind the concept of backpropagation learning rule of a simple supervised neural network, that for example is trained for classification task ?
I was reading about the concept of synaptic plasticity that is explained in simple words here https://qbi.uq.edu.au/:
Plasticity is the ability of the brain to change and adapt to new information. Synaptic plasticity is change that occurs at synapses, the junctions between neurons that allow them to communicate. The idea that synapses could change, and that this change depended on how active or inactive they were, was first proposed in the 1949 by Canadian psychologist Donald Hebb.
And more in particular I found in Wikipedia that:
Hebbian theory is a neuroscientific theory claiming that an increase in synaptic efficacy arises from a presynaptic cell's repeated and persistent stimulation of a postsynaptic cell. It is an attempt to explain synaptic plasticity, the adaptation of brain neurons during the learning process.
In an artificial neural network, synaptic efficacy (that is the strength of communication between neurons, from https://www.frontiersin.org) depends on weights that are associated to connections between neurons (from what I understood reading the beginning of paragraph 44.3 The basic unit — the neuron in Handbook of Chemometrics and Qualimetrics: Part B https://www.sciencedirect.com).
Thus, when we train our supervised classifier so that it updates weights thanks to backpropagation algorithm (whose philosophy is the changing of weights to minimize an error function that characterizes the comparison between the network output and the ground truth), are we applying a sort of Hebb's Rule and, more properly, the concept of synaptic plasticity since weights change during the learning process?
Or Hebb's rule means only "Fire together, wire together" as I understand by reading the answer to this SE post How do you explain Hebbian Learning in an intuitive way?.
I read also this other SE answer to the post Is there a Hebb neural network? in which it is clearly explained that neural networks (or models) that can learn in a Hebbian fashion are different from those based on backpropagation algorithm.
But if synaptic plasticity means a changing in the connections between neurons (synapses), that in artifical neural networks means a changing in the weights, during a learning process, can I say that also neural networks based on back-propagation algorithms exploit the concept of synaptic plasticity (and so the Hebb's rule) ?