there is nothing like backpropagation in the brain, or a probabilistic pattern matcher. there is evidence that a connectionist model is applicable, but learning is not deciphered, and there are aspects of it, like neuronal excitability, local dendritic spiking, oscillations, up and down states etc, which do not translate at all to DL systems. That said, the increasing success of connectionist architecture does point to the conclusion that the brain is also a connectionist machine.