A learning algorithm based on temporal difference of membrane potential of the neuron is proposed for self-organizing neural networks. It is independent of the neuron nonlinearity, so it can be applied to analog or binary neurons. Two simulations for learning of weights are presented; a single layer fully-connected network and a 3-layer network with hidden units for a distributed semantic network. The results demonstrate that this potential difference learning (PDL) can be used with neural architectures for various applications. Unlearning based on PDL for the single layer network is also discussed. Finally, an optical implementation- of PDL is proposed.
C H Wang,
B K. Jenkins,
"Potential Difference Learning And Its Optical Architecture", Proc. SPIE 0882, Neural Network Models for Optical Computing, (3 May 1988); doi: 10.1117/12.944117; https://doi.org/10.1117/12.944117