Event-driven contrastive divergence: neural sampling foundations.

The author discusses the use of contrastive divergence (CD) framework in neural sampling. Topics discussed include the capability of neuron models to perform Boltzmann distribution sampling using Markov Chain Monte Carlo (MCMC) method, the stochastic nature of neural sampling, and the use of Boltzmann machine in evaluating integrate and fire (IF) neural network.