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education, for example, results in brain wave frequencies that are higher than average. Dealing with burst rates and frequencies is poorly suited to the arti cial neural network or perceptron concept. Frequency weighting and summation would have to be related to Fourier integrals and nonlinear mixing, which is far from amplitude processing. Nonlinear activation functions with Fourier inputs would be a puzzle. Hebbian self-organized learning applies when neurons are used repeatedly and need to build up their frequency of ring. This concept is remotely related to a need-to-learn signal enabled by frequent neural usage. Unfortunately, Hebbian learning relies on modulating the frequency of bursts, something that is dif cult to explain physically. What can be explained physically is that a standardized burst is instigated by a connecting neuron whose frequency of action potentials depends entirely on how often it is excited by a connecting neuron. Arti cial neural networks tacitly assume that neurons for learning are already in place and only need to be adjusted. The real question is: What causes the growth of the rst synapse that creates the rst burst in the history of a new neuron Finally, when designing self-contained self-directed robots, an all-digital system, which admittedly has a large overhead of gates, and so seems impractical, might eventually prove easier to design than a large arti cial neural network. A digital VLSI chip, for instance, has millions of gates and continues to grow in capacity, doubling approximately every two years, as predicted under Moore s law. asp.net ean 13 EAN - 13 ASP . NET Control - EAN - 13 barcode generator with free ...
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