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If we have a neural network such as the multilayer perceptron back propagation neural network that uses sigmodial logistic activation functions is it possible to feed the network outputs and have it compute back a set of inputs? Since we can reverse the activation function by using the natural logarithm and inverse operations until we have a sum value that is made up of all the weights multiplied by their inputs i would think that it would be possible to at least get sets of possible inputs that will generate the specified output value.
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Backpropagation in neural network is a short form for “backward propagation of errors.” It is a standard method of training artificial neural networks. This method helps calculate the gradient of a loss function with respect to all the weights in the network.
Not only is this possible, but it is becoming standard practice for AI projects other than reverse engineering games.
There is a "school" of machine learning called extreme learning machine that does not use backpropagation. What they do do is to create a neural network with many, many, many nodes --with random weights-- and then train the last layer using minimum squares (like a linear regression).
In Vanchurin's theory, the most fundamental object is a neuron and the Universe can be described as a neural network. Neural networks are trainable mathematical structures inspired by the human brain. A neuron is a simple processing unit usually described by a simple mathematical function.
As there is an infinite number of possibilities to distribute the result to the feeding neurons this is only possible for each layer where the number of receiving neurons equals the number of the feeding neurons. There it is a simple linear equation which boils down to a Matrix multiplication/inversion problem, that can easily be solved.
If the number of neurons where the number of the receiving neurons (n) is smaller than the number of the feeding neurons (m), you have as solution space a manifold with m - n dimensions.
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