Do our brains use the same kind of deep-learning algorithms used in AI?

This is an illustration of a multi-compartment neural network model for deep learning. The tree-like form separates "roots," where bottoms of cortical neurons are located just where they need to be to receive signals about sensory input, from "branches" at the top, which are well placed to receive feedback error signals. Right: Illustration of simplified pyramidal neuron models. Deep-learning researchers have found that certain neurons in the brain have shape and electrical properties that appear to be well-suited for "deep learning" -- the kind of machine-intelligence used in beating humans at Go and Chess. Canadian Institute For Advanced Research (CIFAR) Fellow Blake Richards and his colleagues -- Jordan Guerguiev at the University of Toronto, Scarborough, and Timothy Lillicrap at Google DeepMind -- developed an algorithm that simulates how a deep-learning network could work in our brains.