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Mean field theory treats very wide randomly initialized neural networks as averaging machines where each neuron behaves like a sample from a common distribution.
The Central Limit Theorem (CLT) says that the sum or average of many independent, identically distributed variables with finite variance becomes approximately normal (Gaussian).