Exact dynamics in feedforward neural networks
Department of Physics, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
Accepted: 5 May 1997
I consider layered neural networks in which the weights are trained by optimizing an arbitrary performance function with respect to a set of examples. Using the cavity method and many-body diagrammatic techniques, the evolution in the network can be described by an overlap and a noise parameter. Parameter pairs corresponding to various input conditions are found to collapse on a universal curve. Simulations with the maximally stable network confirm the theory.
PACS: 87.10.+e – General, theoretical, and mathematical biophysics (including logic of biosystems, quantum biology, and relevant aspects of thermodynamics, information theory, cybernetics, and bionics) / 05.20.-y – Statistical mechanics / 02.50.-r – Probability theory, stochastic processes, and statistics
© EDP Sciences, 1997