Dept. of Computer Science, University of Toronto
How are error derivatives represented in the brain
Tuesday 27th of November 2007 at 12:00pm
Neurons need to represent both the presence of a feature in the sensory input and the derivative of an error function with respect to the neural activity. I will describe a simple way in which they can represent both of these very different quantities at the same time and show that this representational scheme would make it easy for real neurons to backpropagate error derivatives so that higher level feature detectors can fine-tune the receptive fields of lower level ones.
508-20 Evans Hall
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