Learning Continuous Probability Distributions with Symmetric Diffusion Networks

@article{Movellan1993LearningCP,
  title={Learning Continuous Probability Distributions with Symmetric Diffusion Networks},
  author={Javier R. Movellan and James L. McClelland},
  journal={Cognitive Science},
  year={1993},
  volume={17},
  pages={463-496}
}
in this article we present symmetric diffusion networks, a family of networks that instantiate the principles of continuous, stochastic, adaptive and interactive propagation of information. Using methods of Markovlon diffusion theory, we formalize the activation dynamics of these networks and then show that they can be trained to reproduce entire muitivariote probability distributions an their outputs using the contrastive Hebbian learning rule (CHL).,We show that CHL performs gradient descent… CONTINUE READING