of McGill University and the Mila AI Institute in Quebec revealed a new model of the brain’s learning algorithm that can mimic the backpropagation process. It appears so realistic that experimental neuroscientists have taken notice and are now interested in studying real neurons to find out whether the brain is actually doing it.
These changes are based on calculating how every neuron could contribute less to the overall error, starting with the neurons at the top, nearest the output layer, and then moving backward through each layer. If the backpropagation algorithm estimates that increasing a given neuron’s activity will improve the output prediction, for example, then that neuron’s weights will increase.
“[Our model] shows that you really can have two signals, one going up and one going down, and they can pass one another,” said Naud.
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