I was reading a paper on alternatives to backpropagation as a learning algorithm in neural networks. In this paper, the author talks about the disadvantages of backpropagation, and one of the disadvantages stated is that backpropagation requires symmetric weights and that's why it's not biologically plausible.
What do symmetric weights mean and how does it make backpropagation biologically implausible?
"Symmetric weights" means that the same weight value associated to a pair of nodes must be used during the forwards and backwards steps.
The reason it makes back propagation biologically impossible in its naive formulation is that neurons fire electrical signals in only one direction, from the dendrite through the axon to other dendrites of other neurons. They do receive of course "backwards" feedback, but by other means, e.g. chemical neurotransmitters or other signals from other neurons, but these signals are very likely not of the same intensity as the signal emitted by the neurons themselves (i.e. no symmetry).
