Implementing belief propagation in neural circuits

There is growing evidence that neural circuits may employ statistical algorithms for inference and learning. Many such algorithms can be derived from independence diagrams (graphical models) showing causal relationships between random variables. A general algorithm for inference in graphical models is belief propagation, where nodes in a graphical model determine values for random variables by combining observed values with messages passed between neighboring nodes. We propose that small groups of synaptic connections between neurons in cortex correspond to causal dependencies in an underlying graphical model. Our results suggest a new probabilistic framework for computation in the neocortex.