Although the gradient method with error back-propagation has proved to be successful at teaching multilayered neural networks to perform many tasks, it has a number of rather unrealistic aspects, especially concerning the comparison with biological nerve nets. It is particularly troublesome in this respect: that complete knowledge of the deviation of the output from the desired reaction is required to determine the adjustment even of neurons in hidden layers far separated from the output layer. It is hard to believe that such extended back-coupling mechanisms can operate in complex biological neural networks.
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