Take, for example, the processing of sensations: the bottom of the pyramidal neurons

Are where they should, to get a touch input, and the tops are conveniently located to transmit errors through feedback. Can this complex structure be an evolutionary solution to combat an erroneous signal? Scientists have created a multilayer neural network based on previous algorithms. But instead of homogeneous neurons, they gave it neurons of middle layers - sandwiched between input and output - similar to real ones. Learning on handwritten figures, the algorithm proved much better than a single-layer network, despite the lack of a classic back-propagation error. Cell structures themselves could determine the error. Then, at the right time, the neuron combined both sources of information to find the best solution. There is a biological basis in this: neurobiologists have long known that the input branches of the neuron perform local calculations that can be integrated with the signals of the backward propagation of the error from the output branches. 

But we do not know if the brain really works that way - that's why Richards instructed the neuroscientists to find out. Moreover, this network handles the problem similar to the traditional method of in-depth training in a way: it uses a multi-layered structure to extract progressively more abstract ideas about each number. "This is a feature of in-depth training," the authors explain. Deep Learning Brain No doubt, in this story there will be more unexpected turns, because computer scientists are making more and more biological details in AI algorithms. Richards and his team view the predictive function from the top to the bottom, when signals from higher levels directly affect how the lower levels react to input. Feedback from the upper levels not only improves the signaling of errors; it can also encourage lower-level neurons to work "better" in real time, Richards says. While the network has not surpassed other non-biological networks of in-depth training.