Implementing radial basis functions using bump-resistor networks

Radial basis function (RBF) networks provide a powerful learning architecture for neural networks. The author has implemented a RBF network in analog VLSI using the concept of bump-resistors. A bump-resistor is a nonlinear resistor whose conductance is a Gaussian-like function of the difference of two other voltages. The width of the Gaussian basis functions may be continuously varied so that the aggregate interpolating function varies from a nearest-neighbor lookup, piece-wise constant function to a globally smooth function. The bump-resistor methodology extends to arbitrary dimensions while still preserving the radiality of the basis functions. The feedforward network architecture needs no additional circuitry other than voltage sources and the 1D bump-resistors