Local training of neural networks

Abstract

A method for performing learning is described. A free inference is performed on a learning network for input signals. The input signals correspond to target output signals. The learning network includes inputs that receive the input signals, neurons, weights interconnecting the neurons, and outputs. The learning network is described by an energy for the free inference. The energy includes an interaction term corresponding to interactions consisting of neuron pair interactions. The free inference results in output signals. A first portion of the plurality of weights corresponding to data flow for the free inference. A biased inference is performed on the learning network by providing the input signals to the inputs and bias signals to the outputs. The bias signals are based on the target output signals and the output signals. The bias signals are fed back to the learning network through a second portion of the weights corresponding to a transpose of the first portion of the weights. At locations in the learning network, learning network equilibrium states are determined for the biased inference. The weights are updated based on the learning network equilibrium states.