Inferring neural activity before plasticity as a foundation for learning beyond backpropagation.

Difference in learning between artificial neural networks and the brain, illustrated on an example of a bear fishing for salmon. The bear can see the river and it has learnt that if it can also hear the river and smell the salmon it is likely to catch one. But one day, the bear arrives at the river with a damaged ear, so it can’t hear it. The bear needs to weaken the connections between its neurons encoding the sight of the river and its sound (top left display). In an artificial neural network information processing model, this lack of hearing would also result in a lack of smell because while learning there is no sound, backpropagation would change multiple connections including those between neurons encoding the river and the smell of salmon (top right), and the bear would conclude that there is no salmon, and go hungry. But in the animal brain, where the neurons first settle to prospective configuration (bottom left), the lack of sound does not interfere with the knowledge that there is still the smell of the salmon (bottom right), therefore the salmon is still likely to be there for catching.