Training a Limited-Interconnect, Synthetic Neural IC

Hardware implementation of neuromorphic algorithms is hampered by high degrees of connectivity. Functionally equivalent feedforward networks may be formed by using limited fan-in nodes and additional layers. No direct mapping of weights exists between fully and limited-interconnect nets. Low-level nonlinearities prevent the formation of internal representations of widely separated spatial features and the use of gradient descent methods to minimize output error is hampered by error magnitude dissipation. The judicious use of linear summations or collection units is proposed as a solution.